Tyndall Centre Inproceedings
2024
of Sciences, Engineering National Academies; Medicine,; Earth, Division; Studies, Life; Sciences, Board Atmospheric; Climate,; Agriculture, Board; Resources, Natural; Board, Polar Research; on Greenhouse Gas Emissions from Wildland Fires: Toward Improved Monitoring, Modeling Committee; Workshop, Management—A; Silvern, Rachel; Jones, Matthew
Approaches for Understanding Regional Fires Proceedings Article
In: Greenhouse Gas Emissions from Wildland Fires: Toward Improved Monitoring, Modeling, and Management, pp. 30–30, National Academies Press, 2024, ISBN: 978-0-309-71553-9.
@inproceedings{7b1f83c58ed84b5aa003ec1f77b6c02b,
title = {Approaches for Understanding Regional Fires},
author = {Engineering National Academies of Sciences and Medicine and Division Earth and Life Studies and Board Atmospheric Sciences and Climate and Board Agriculture and Natural Resources and Polar Research Board and Modeling Committee on Greenhouse Gas Emissions from Wildland Fires: Toward Improved Monitoring and Management—A Workshop and Rachel Silvern and Matthew Jones},
doi = {10.17226/27473},
isbn = {978-0-309-71553-9},
year = {2024},
date = {2024-04-30},
booktitle = {Greenhouse Gas Emissions from Wildland Fires: Toward Improved Monitoring, Modeling, and Management},
pages = {30–30},
publisher = {National Academies Press},
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2023
Capper, Timothy; Kuriakose, Jaise; Sharmina, Maria
Economic viability of domestic battery storage participation in British flexibility markets Proceedings Article
In: 19th International Conference on AC and DC Power Transmission (ACDC 2023), Institution of Engineering and Technology, United Kingdom, 2023.
@inproceedings{fec9eeec3d4c4f5e8eb60873f50f0995,
title = {Economic viability of domestic battery storage participation in British flexibility markets},
author = {Timothy Capper and Jaise Kuriakose and Maria Sharmina},
year = {2023},
date = {2023-03-01},
booktitle = {19th International Conference on AC and DC Power Transmission (ACDC 2023)},
publisher = {Institution of Engineering and Technology},
address = {United Kingdom},
abstract = {Curbing emissions to avoid the worst impacts of climate change is likely to lead to a significant increase in electricity demand due to the electrification of heating and transportation, and a decrease in the balancing reserve due to a decrease in the number of gas turbines. To alleviate the significant network investment costs and the fall in balancing resources, both distribution network operators and the system operator in Great Britain have created markets for behind-the-meter flexibility. For domestic battery storage to play a meaningful role in flexibility markets, it must be beneficial to the households who choose to finance and install it. In this paper we examine the value of domestic battery storage systems, both with and without associated PV installations. We show quantitatively, for the first time, that domestic battery storage is a financially worthwhile investment for households, and that participating in network operator and system operator flexibility markets can be lucrative. Finally, we show that domestic PV installations have a negative value when accounting for the capital investment. However, if a household wanted to install PV for non-financial reasons, investing in a battery alongside the PV gives the installed system a positive value.},
keywords = {},
pubstate = {published},
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}
Curbing emissions to avoid the worst impacts of climate change is likely to lead to a significant increase in electricity demand due to the electrification of heating and transportation, and a decrease in the balancing reserve due to a decrease in the number of gas turbines. To alleviate the significant network investment costs and the fall in balancing resources, both distribution network operators and the system operator in Great Britain have created markets for behind-the-meter flexibility. For domestic battery storage to play a meaningful role in flexibility markets, it must be beneficial to the households who choose to finance and install it. In this paper we examine the value of domestic battery storage systems, both with and without associated PV installations. We show quantitatively, for the first time, that domestic battery storage is a financially worthwhile investment for households, and that participating in network operator and system operator flexibility markets can be lucrative. Finally, we show that domestic PV installations have a negative value when accounting for the capital investment. However, if a household wanted to install PV for non-financial reasons, investing in a battery alongside the PV gives the installed system a positive value.
Shtanov, Mark
Drawing your incinerator: suitability of graphic anthropology for researching municipal solid waste infrastructure in the United Kingdom Proceedings Article
In: Sosiologia, pp. 139, The Westermarck Society, Finland, 2023, (proceedings published in the print version only).
@inproceedings{1e4d84d8c9e94be699ad702c2ffb841fb,
title = {Drawing your incinerator: suitability of graphic anthropology for researching municipal solid waste infrastructure in the United Kingdom},
author = {Mark Shtanov},
year = {2023},
date = {2023-01-01},
booktitle = {Sosiologia},
volume = {2},
pages = {139},
publisher = {The Westermarck Society},
address = {Finland},
abstract = {Societies in the Global North typically neglect, fear, or deny the existence of waste. They seek to marginalise waste management infrastructure geographically and cognitively. New research methods are needed to study, visualise, expose and debate, where our waste goes and what happens to it. This paper aims to position waste treatment facilities as urban typologies, workplaces, and locations of cultural significance, rather than merely as logistical non-places. Methodologically, the research uses graphic anthropology, which involves documenting industrial facilities through annotated sketches and diagrams. The approach has been previously employed in the examination of urban public spaces, private homes, psychiatric hospitals, and prisons. The suitability of graphic anthropology for research on waste treatment facilities will be evaluated. The study specifically focuses on energy-from-waste facilities in Manchester and Middlesbrough, United Kingdom, and includes findings from interviews with staff and stakeholders. Findings contribute to the fields of waste management, architecture, and Science and Technology Studies by providing methodological innovation and highlighting the potential benefits and drawbacks of using graphic anthropology to study waste management infrastructure. Furthermore, the findings contribute to the discussion about the design flaws impacting on the durability and operational efficiency of industrial facilities, as well as the comfort and well-being of their workers.},
note = {proceedings published in the print version only},
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pubstate = {published},
tppubtype = {inproceedings}
}
Societies in the Global North typically neglect, fear, or deny the existence of waste. They seek to marginalise waste management infrastructure geographically and cognitively. New research methods are needed to study, visualise, expose and debate, where our waste goes and what happens to it. This paper aims to position waste treatment facilities as urban typologies, workplaces, and locations of cultural significance, rather than merely as logistical non-places. Methodologically, the research uses graphic anthropology, which involves documenting industrial facilities through annotated sketches and diagrams. The approach has been previously employed in the examination of urban public spaces, private homes, psychiatric hospitals, and prisons. The suitability of graphic anthropology for research on waste treatment facilities will be evaluated. The study specifically focuses on energy-from-waste facilities in Manchester and Middlesbrough, United Kingdom, and includes findings from interviews with staff and stakeholders. Findings contribute to the fields of waste management, architecture, and Science and Technology Studies by providing methodological innovation and highlighting the potential benefits and drawbacks of using graphic anthropology to study waste management infrastructure. Furthermore, the findings contribute to the discussion about the design flaws impacting on the durability and operational efficiency of industrial facilities, as well as the comfort and well-being of their workers.
Shtanov, Mark
Drawing your incinerator: suitability of graphic anthropology for researching municipal solid waste infrastructure in the United Kingdom Proceedings Article
In: Sosiologia, pp. 139, The Westermarck Society, Finland, 2023, (proceedings published in the print version only).
@inproceedings{1e4d84d8c9e94be699ad702c2ffb841f,
title = {Drawing your incinerator: suitability of graphic anthropology for researching municipal solid waste infrastructure in the United Kingdom},
author = {Mark Shtanov},
year = {2023},
date = {2023-01-01},
booktitle = {Sosiologia},
volume = {2},
pages = {139},
publisher = {The Westermarck Society},
address = {Finland},
abstract = {Societies in the Global North typically neglect, fear, or deny the existence of waste. They seek to marginalise waste management infrastructure geographically and cognitively. New research methods are needed to study, visualise, expose and debate, where our waste goes and what happens to it. This paper aims to position waste treatment facilities as urban typologies, workplaces, and locations of cultural significance, rather than merely as logistical non-places. Methodologically, the research uses graphic anthropology, which involves documenting industrial facilities through annotated sketches and diagrams. The approach has been previously employed in the examination of urban public spaces, private homes, psychiatric hospitals, and prisons. The suitability of graphic anthropology for research on waste treatment facilities will be evaluated. The study specifically focuses on energy-from-waste facilities in Manchester and Middlesbrough, United Kingdom, and includes findings from interviews with staff and stakeholders. Findings contribute to the fields of waste management, architecture, and Science and Technology Studies by providing methodological innovation and highlighting the potential benefits and drawbacks of using graphic anthropology to study waste management infrastructure. Furthermore, the findings contribute to the discussion about the design flaws impacting on the durability and operational efficiency of industrial facilities, as well as the comfort and well-being of their workers.},
note = {proceedings published in the print version only},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Societies in the Global North typically neglect, fear, or deny the existence of waste. They seek to marginalise waste management infrastructure geographically and cognitively. New research methods are needed to study, visualise, expose and debate, where our waste goes and what happens to it. This paper aims to position waste treatment facilities as urban typologies, workplaces, and locations of cultural significance, rather than merely as logistical non-places. Methodologically, the research uses graphic anthropology, which involves documenting industrial facilities through annotated sketches and diagrams. The approach has been previously employed in the examination of urban public spaces, private homes, psychiatric hospitals, and prisons. The suitability of graphic anthropology for research on waste treatment facilities will be evaluated. The study specifically focuses on energy-from-waste facilities in Manchester and Middlesbrough, United Kingdom, and includes findings from interviews with staff and stakeholders. Findings contribute to the fields of waste management, architecture, and Science and Technology Studies by providing methodological innovation and highlighting the potential benefits and drawbacks of using graphic anthropology to study waste management infrastructure. Furthermore, the findings contribute to the discussion about the design flaws impacting on the durability and operational efficiency of industrial facilities, as well as the comfort and well-being of their workers.
2022
Li, Wenzhu; Cesena, Eduardo Alejandro Martinez; Cunningham, Lee; Panteli, Mathaios; Schultz, David; Mander, Sarah; Gan, Chin Kim; Mancarella, Pierluigi
In: Proceedings of IEEE Power and Energy Society: Innovative Smart Grid Technologies (ISGT) Europe 2022, pp. 1–5, IEEE, United States, 2022, ISBN: 9781665480321, (Funding Information: ACKNOWLEDGEMENT The authors gratefully acknowledge the financial support of the EPSRC for funding “TERSE” (EP/R030294/1), UKRI for the GCRF funds for “FutureDAMS” (ES/P011373/1), as well as the GCRF funds for PhD Studentship. The authors also sincerely thank the Research & Development team from Sarawak Energy Berhad, Malaysia, Dr Fergus McClean and Prof Richard Dawson from Newcastle University for providing data and CityCAT software for this work. Publisher Copyright: © 2022 IEEE.; IEEE PES Innovative Smart Grid Technologies (ISGT) Europe 2022, ISGT Europe 2022 ; Conference date: 10-10-2022 Through 12-10-2022).
@inproceedings{26fb4cfe31094370930393e702540405,
title = {Assessing Power System Resilience to Floods: A Geo-Referenced Statistical Model for Substation Inundation Failures: A Geo-Referenced Statistical Model for Substation Inundation Failures},
author = {Wenzhu Li and Eduardo Alejandro Martinez Cesena and Lee Cunningham and Mathaios Panteli and David Schultz and Sarah Mander and Chin Kim Gan and Pierluigi Mancarella},
url = {https://attend.ieee.org/isgt-europe-2022/},
doi = {10.1109/ISGT-Europe54678.2022.9960296},
isbn = {9781665480321},
year = {2022},
date = {2022-10-12},
booktitle = {Proceedings of IEEE Power and Energy Society: Innovative Smart Grid Technologies (ISGT) Europe 2022},
pages = {1–5},
publisher = {IEEE},
address = {United States},
series = {IEEE PES Innovative Smart Grid Technologies Conference Europe},
abstract = {Floods can cause widespread and prolonged power outages by inundating substations. However, assessing the inundation failure of substations to improve power system resilience is challenging, as there may not be sufficient historicaldata to capture the impact of flooding on substations at a given location, due to their evident temporal and spatial variability. To tackle this gap in knowledge, this paper proposes a georeferenced statistical model that is not constrained by historical flooding data. The geo-referenced model embeds a hydrologicalmodel that uses established digital rainfall and topography data to simulate flood depths at the location of selected substations and calculate associated inundation risks. The stochastic inundation profiles are used within a Monte Carlo simulation to model substation failures and explore options to improve power system resilience (e.g., asset elevation). The proposed model isdemonstrated using a case study from Bintulu, Malaysia, where real empirical data was used to identify the breaking points of the power system and estimate probability density functions of energy not supplied caused by inundated substations. The simulation results show that substation failures can abruptlylead to significant energy not being supplied, whereas elevating the substation to withstand an additional 20 cm flood depth will significantly delay flood impacts, and effectively improve system resilience. The proposed methodology and key findings will enable system planners and operators to understand when and where the power system will experience energy losses under unpredictable extreme floods and help them to decide on the most effective resilience enhancement strategies.},
note = {Funding Information: ACKNOWLEDGEMENT The authors gratefully acknowledge the financial support of the EPSRC for funding “TERSE” (EP/R030294/1), UKRI for the GCRF funds for “FutureDAMS” (ES/P011373/1), as well as the GCRF funds for PhD Studentship. The authors also sincerely thank the Research & Development team from Sarawak Energy Berhad, Malaysia, Dr Fergus McClean and Prof Richard Dawson from Newcastle University for providing data and CityCAT software for this work. Publisher Copyright: © 2022 IEEE.; IEEE PES Innovative Smart Grid Technologies (ISGT) Europe 2022, ISGT Europe 2022 ; Conference date: 10-10-2022 Through 12-10-2022},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Floods can cause widespread and prolonged power outages by inundating substations. However, assessing the inundation failure of substations to improve power system resilience is challenging, as there may not be sufficient historicaldata to capture the impact of flooding on substations at a given location, due to their evident temporal and spatial variability. To tackle this gap in knowledge, this paper proposes a georeferenced statistical model that is not constrained by historical flooding data. The geo-referenced model embeds a hydrologicalmodel that uses established digital rainfall and topography data to simulate flood depths at the location of selected substations and calculate associated inundation risks. The stochastic inundation profiles are used within a Monte Carlo simulation to model substation failures and explore options to improve power system resilience (e.g., asset elevation). The proposed model isdemonstrated using a case study from Bintulu, Malaysia, where real empirical data was used to identify the breaking points of the power system and estimate probability density functions of energy not supplied caused by inundated substations. The simulation results show that substation failures can abruptlylead to significant energy not being supplied, whereas elevating the substation to withstand an additional 20 cm flood depth will significantly delay flood impacts, and effectively improve system resilience. The proposed methodology and key findings will enable system planners and operators to understand when and where the power system will experience energy losses under unpredictable extreme floods and help them to decide on the most effective resilience enhancement strategies.
Li, Wenzhu; Cesena, Eduardo Alejandro Martinez; Cunningham, Lee; Panteli, Mathaios; Schultz, David; Mander, Sarah; Gan, Chin Kim; Mancarella, Pierluigi
In: Proceedings of IEEE Power and Energy Society: Innovative Smart Grid Technologies (ISGT) Europe 2022, pp. 1–5, IEEE, United States, 2022, ISBN: 9781665480321, (Funding Information: ACKNOWLEDGEMENT The authors gratefully acknowledge the financial support of the EPSRC for funding “TERSE” (EP/R030294/1), UKRI for the GCRF funds for “FutureDAMS” (ES/P011373/1), as well as the GCRF funds for PhD Studentship. The authors also sincerely thank the Research & Development team from Sarawak Energy Berhad, Malaysia, Dr Fergus McClean and Prof Richard Dawson from Newcastle University for providing data and CityCAT software for this work. Publisher Copyright: © 2022 IEEE.; IEEE PES Innovative Smart Grid Technologies (ISGT) Europe 2022, ISGT Europe 2022 ; Conference date: 10-10-2022 Through 12-10-2022).
@inproceedings{26fb4cfe31094370930393e702540405b,
title = {Assessing Power System Resilience to Floods: A Geo-Referenced Statistical Model for Substation Inundation Failures: A Geo-Referenced Statistical Model for Substation Inundation Failures},
author = {Wenzhu Li and Eduardo Alejandro Martinez Cesena and Lee Cunningham and Mathaios Panteli and David Schultz and Sarah Mander and Chin Kim Gan and Pierluigi Mancarella},
url = {https://attend.ieee.org/isgt-europe-2022/},
doi = {10.1109/ISGT-Europe54678.2022.9960296},
isbn = {9781665480321},
year = {2022},
date = {2022-10-12},
booktitle = {Proceedings of IEEE Power and Energy Society: Innovative Smart Grid Technologies (ISGT) Europe 2022},
pages = {1–5},
publisher = {IEEE},
address = {United States},
series = {IEEE PES Innovative Smart Grid Technologies Conference Europe},
abstract = {Floods can cause widespread and prolonged power outages by inundating substations. However, assessing the inundation failure of substations to improve power system resilience is challenging, as there may not be sufficient historicaldata to capture the impact of flooding on substations at a given location, due to their evident temporal and spatial variability. To tackle this gap in knowledge, this paper proposes a georeferenced statistical model that is not constrained by historical flooding data. The geo-referenced model embeds a hydrologicalmodel that uses established digital rainfall and topography data to simulate flood depths at the location of selected substations and calculate associated inundation risks. The stochastic inundation profiles are used within a Monte Carlo simulation to model substation failures and explore options to improve power system resilience (e.g., asset elevation). The proposed model isdemonstrated using a case study from Bintulu, Malaysia, where real empirical data was used to identify the breaking points of the power system and estimate probability density functions of energy not supplied caused by inundated substations. The simulation results show that substation failures can abruptlylead to significant energy not being supplied, whereas elevating the substation to withstand an additional 20 cm flood depth will significantly delay flood impacts, and effectively improve system resilience. The proposed methodology and key findings will enable system planners and operators to understand when and where the power system will experience energy losses under unpredictable extreme floods and help them to decide on the most effective resilience enhancement strategies.},
note = {Funding Information: ACKNOWLEDGEMENT The authors gratefully acknowledge the financial support of the EPSRC for funding “TERSE” (EP/R030294/1), UKRI for the GCRF funds for “FutureDAMS” (ES/P011373/1), as well as the GCRF funds for PhD Studentship. The authors also sincerely thank the Research & Development team from Sarawak Energy Berhad, Malaysia, Dr Fergus McClean and Prof Richard Dawson from Newcastle University for providing data and CityCAT software for this work. Publisher Copyright: © 2022 IEEE.; IEEE PES Innovative Smart Grid Technologies (ISGT) Europe 2022, ISGT Europe 2022 ; Conference date: 10-10-2022 Through 12-10-2022},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Floods can cause widespread and prolonged power outages by inundating substations. However, assessing the inundation failure of substations to improve power system resilience is challenging, as there may not be sufficient historicaldata to capture the impact of flooding on substations at a given location, due to their evident temporal and spatial variability. To tackle this gap in knowledge, this paper proposes a georeferenced statistical model that is not constrained by historical flooding data. The geo-referenced model embeds a hydrologicalmodel that uses established digital rainfall and topography data to simulate flood depths at the location of selected substations and calculate associated inundation risks. The stochastic inundation profiles are used within a Monte Carlo simulation to model substation failures and explore options to improve power system resilience (e.g., asset elevation). The proposed model isdemonstrated using a case study from Bintulu, Malaysia, where real empirical data was used to identify the breaking points of the power system and estimate probability density functions of energy not supplied caused by inundated substations. The simulation results show that substation failures can abruptlylead to significant energy not being supplied, whereas elevating the substation to withstand an additional 20 cm flood depth will significantly delay flood impacts, and effectively improve system resilience. The proposed methodology and key findings will enable system planners and operators to understand when and where the power system will experience energy losses under unpredictable extreme floods and help them to decide on the most effective resilience enhancement strategies.
Li, Wenzhu; Cunningham, Lee; Mander, Sarah; Schultz, David; Panteli, Mathaios; Gan, Chin Kim
Siting of Electrical Substations for Flood Resilience: Identification of Hazards and Vulnerable Assets Proceedings Article
In: Proceedings of the 2022 MACE PGR Conference, pp. 1–4, 2022, (The 2022 MACE PGR Conference ; Conference date: 01-07-2022 Through 01-07-2022).
@inproceedings{a68dea1043d34154847865006922fb44,
title = {Siting of Electrical Substations for Flood Resilience: Identification of Hazards and Vulnerable Assets},
author = {Wenzhu Li and Lee Cunningham and Sarah Mander and David Schultz and Mathaios Panteli and Chin Kim Gan},
year = {2022},
date = {2022-07-01},
booktitle = {Proceedings of the 2022 MACE PGR Conference},
pages = {1–4},
abstract = {Extreme weather events disrupt power-system assets, causing widespread and prolonged power outages. Proper siting of power-system assets can help stakeholders improve their resilience (i.e., the ability to withstand hazards and recover quickly from impacts) at the planning stage. However, resilience is context-dependent because different regions face distinct hazards and these damage power system assets differently. As such, the asset siting schemes for stakeholders involve a complex process of i) identifying hazards and vulnerable assets, ii) determining the failure mechanisms of assets under hazards and iii) weighing up the economic and social effectiveness involved in resilience enhancement measures. This paper will focus on the first step of the process, which is to demonstrate how to identify the most severe hazards (e.g., floods) and affected assets (e.g., substations), using Peninsular Malaysia as the study area. The results of this study can provide research objectives for the subsequent two steps: determining the failure mechanisms of assets subject to hazards, and developing resilience enhancement measures in the third step.},
note = {The 2022 MACE PGR Conference ; Conference date: 01-07-2022 Through 01-07-2022},
keywords = {},
pubstate = {published},
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}
Extreme weather events disrupt power-system assets, causing widespread and prolonged power outages. Proper siting of power-system assets can help stakeholders improve their resilience (i.e., the ability to withstand hazards and recover quickly from impacts) at the planning stage. However, resilience is context-dependent because different regions face distinct hazards and these damage power system assets differently. As such, the asset siting schemes for stakeholders involve a complex process of i) identifying hazards and vulnerable assets, ii) determining the failure mechanisms of assets under hazards and iii) weighing up the economic and social effectiveness involved in resilience enhancement measures. This paper will focus on the first step of the process, which is to demonstrate how to identify the most severe hazards (e.g., floods) and affected assets (e.g., substations), using Peninsular Malaysia as the study area. The results of this study can provide research objectives for the subsequent two steps: determining the failure mechanisms of assets subject to hazards, and developing resilience enhancement measures in the third step.
Foggitt, Ella; Mehta, Nikita; Roy, Manoj; Pickup, Roger; Ara, Yasmin; Veral, Mehmet; Kabir, Ehsan; Larbi, Reuben
Connecting the Unconnected: Informal Toilets and a Safe Circular Water Economy.: Proceedings of International Symposium Proceedings Article
In: Connecting the Unconnected: Informal Toilets and a Safe Circular Water Economy., Lancaster University, United Kingdom, 2022.
@inproceedings{101909bdeb4a40ecb5aab1b668d83786,
title = {Connecting the Unconnected: Informal Toilets and a Safe Circular Water Economy.: Proceedings of International Symposium},
author = {Ella Foggitt and Nikita Mehta and Manoj Roy and Roger Pickup and Yasmin Ara and Mehmet Veral and Ehsan Kabir and Reuben Larbi},
year = {2022},
date = {2022-01-01},
booktitle = {Connecting the Unconnected: Informal Toilets and a Safe Circular Water Economy.},
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Foggitt, Ella; Mehta, Nikita; Roy, Manoj; Pickup, Roger; Ara, Yasmin; Veral, Mehmet; Kabir, Ehsan; Larbi, Reuben
Connecting the Unconnected: Informal Toilets and a Safe Circular Water Economy.: Proceedings of International Symposium Proceedings Article
In: Connecting the Unconnected: Informal Toilets and a Safe Circular Water Economy., Lancaster University, United Kingdom, 2022.
@inproceedings{101909bdeb4a40ecb5aab1b668d83786b,
title = {Connecting the Unconnected: Informal Toilets and a Safe Circular Water Economy.: Proceedings of International Symposium},
author = {Ella Foggitt and Nikita Mehta and Manoj Roy and Roger Pickup and Yasmin Ara and Mehmet Veral and Ehsan Kabir and Reuben Larbi},
year = {2022},
date = {2022-01-01},
booktitle = {Connecting the Unconnected: Informal Toilets and a Safe Circular Water Economy.},
publisher = {Lancaster University},
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2021
Li, Jingyi; Tarpani, Raphael; Schmid, Alejandro Gallego; Stamford, Laurence
Adopting Circular Economy Strategies into Geothermal Power Generation Proceedings Article
In: 3rd Symposium on Circular Economy and Sustainability, 2021.
@inproceedings{4737f47e585f43698b3499c0a7d98204,
title = {Adopting Circular Economy Strategies into Geothermal Power Generation},
author = {Jingyi Li and Raphael Tarpani and Alejandro Gallego Schmid and Laurence Stamford},
year = {2021},
date = {2021-06-29},
booktitle = {3rd Symposium on Circular Economy and Sustainability},
abstract = {Geothermal energy is a stable and ubiquitous renewable energy source, which has been successfully deployed for heating, cooling, and electricity production in many nations around the world. Therefore, it has been considered a promising technique to mitigate climate change and other impacts. However, few studies have addressed circular economy (CE) strategies for geothermal energy utilisation, leaving an important but underexplored gap in literature. This work evaluates how different CE strategies can aid geothermal energy to become an even more sustainable energy source.},
keywords = {},
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}
Geothermal energy is a stable and ubiquitous renewable energy source, which has been successfully deployed for heating, cooling, and electricity production in many nations around the world. Therefore, it has been considered a promising technique to mitigate climate change and other impacts. However, few studies have addressed circular economy (CE) strategies for geothermal energy utilisation, leaving an important but underexplored gap in literature. This work evaluates how different CE strategies can aid geothermal energy to become an even more sustainable energy source.
Li, Jingyi; Tarpani, Raphael; Schmid, Alejandro Gallego; Stamford, Laurence
Adopting Circular Economy Strategies into Geothermal Power Generation Proceedings Article
In: 3rd Symposium on Circular Economy and Sustainability, 2021.
@inproceedings{4737f47e585f43698b3499c0a7d98204b,
title = {Adopting Circular Economy Strategies into Geothermal Power Generation},
author = {Jingyi Li and Raphael Tarpani and Alejandro Gallego Schmid and Laurence Stamford},
year = {2021},
date = {2021-06-29},
booktitle = {3rd Symposium on Circular Economy and Sustainability},
abstract = {Geothermal energy is a stable and ubiquitous renewable energy source, which has been successfully deployed for heating, cooling, and electricity production in many nations around the world. Therefore, it has been considered a promising technique to mitigate climate change and other impacts. However, few studies have addressed circular economy (CE) strategies for geothermal energy utilisation, leaving an important but underexplored gap in literature. This work evaluates how different CE strategies can aid geothermal energy to become an even more sustainable energy source.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Geothermal energy is a stable and ubiquitous renewable energy source, which has been successfully deployed for heating, cooling, and electricity production in many nations around the world. Therefore, it has been considered a promising technique to mitigate climate change and other impacts. However, few studies have addressed circular economy (CE) strategies for geothermal energy utilisation, leaving an important but underexplored gap in literature. This work evaluates how different CE strategies can aid geothermal energy to become an even more sustainable energy source.
Li, Wenzhu; Galeela, Mohamed; Panteli, Mathaios; Cesena, Eduardo Alejandro Martinez; Mancarella, Pierluigi
Comparative Studies on Cost, Reliability and Resilience of Off-Grid Energy Systems Proceedings Article
In: 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, pp. 1–6, IEEE, United States, 2021, (Funding Information: ACKNOWLEDGMENT The authors gratefully acknowledge the financial support of the EPSRC-funded “Techno-Economic framework for Resilient and Sustainable Electrification” (EP/R030294/1) and the Newton Prize project “Resilient Planning of Low-Carbon Power Systems”. Publisher Copyright: © 2021 IEEE.; 2021 IEEE Madrid PowerTech, PowerTech 2021 ; Conference date: 28-06-2021 Through 02-07-2021).
@inproceedings{0f2f895ac1b04dc9862dbb978d0ba57a,
title = {Comparative Studies on Cost, Reliability and Resilience of Off-Grid Energy Systems},
author = {Wenzhu Li and Mohamed Galeela and Mathaios Panteli and Eduardo Alejandro Martinez Cesena and Pierluigi Mancarella},
doi = {10.1109/PowerTech46648.2021.9495025},
year = {2021},
date = {2021-06-28},
booktitle = {2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings},
pages = {1–6},
publisher = {IEEE},
address = {United States},
series = {2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings},
abstract = {Rural electrification is critical for the social and economic development of developing countries. Off-grid energy systems are attractive solutions for rural electrification which may offer technical and economic benefits compared with network expansion. However, given the conditions in remote areas, off-grid energy systems are often exposed to high impact and low probability (HILP) events such as natural hazards. This is an issue as off-grid planning practices traditionally focus on average performance reliability indicators, and neglect studying resilience to HILP events. This paper proposes a practical off-grid assessment framework which brings together average and risk performance indices to optimally size off-grid energy systems and quantify their reliability and resilience performance. The proposed methodology, based on Sequential Monte Carlo simulations, is demonstrated with two off-grid energy systems: PV and CHP based systems. In addition, through case studies with applications on windstorms, the impacts of natural hazards are systematically quantified and explicitly considered in the sizing of the off-grid plant. Power system planners and operators can use this framework to quantify the reliability and resilience of off-grid energy systems and inform the decision-making on optimal portfolios for rural electrification.},
note = {Funding Information: ACKNOWLEDGMENT The authors gratefully acknowledge the financial support of the EPSRC-funded “Techno-Economic framework for Resilient and Sustainable Electrification” (EP/R030294/1) and the Newton Prize project “Resilient Planning of Low-Carbon Power Systems”. Publisher Copyright: © 2021 IEEE.; 2021 IEEE Madrid PowerTech, PowerTech 2021 ; Conference date: 28-06-2021 Through 02-07-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rural electrification is critical for the social and economic development of developing countries. Off-grid energy systems are attractive solutions for rural electrification which may offer technical and economic benefits compared with network expansion. However, given the conditions in remote areas, off-grid energy systems are often exposed to high impact and low probability (HILP) events such as natural hazards. This is an issue as off-grid planning practices traditionally focus on average performance reliability indicators, and neglect studying resilience to HILP events. This paper proposes a practical off-grid assessment framework which brings together average and risk performance indices to optimally size off-grid energy systems and quantify their reliability and resilience performance. The proposed methodology, based on Sequential Monte Carlo simulations, is demonstrated with two off-grid energy systems: PV and CHP based systems. In addition, through case studies with applications on windstorms, the impacts of natural hazards are systematically quantified and explicitly considered in the sizing of the off-grid plant. Power system planners and operators can use this framework to quantify the reliability and resilience of off-grid energy systems and inform the decision-making on optimal portfolios for rural electrification.
Li, Wenzhu; Galeela, Mohamed; Panteli, Mathaios; Cesena, Eduardo Alejandro Martinez; Mancarella, Pierluigi
Comparative Studies on Cost, Reliability and Resilience of Off-Grid Energy Systems Proceedings Article
In: 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, pp. 1–6, IEEE, United States, 2021, (Funding Information: ACKNOWLEDGMENT The authors gratefully acknowledge the financial support of the EPSRC-funded “Techno-Economic framework for Resilient and Sustainable Electrification” (EP/R030294/1) and the Newton Prize project “Resilient Planning of Low-Carbon Power Systems”. Publisher Copyright: © 2021 IEEE.; 2021 IEEE Madrid PowerTech, PowerTech 2021 ; Conference date: 28-06-2021 Through 02-07-2021).
@inproceedings{0f2f895ac1b04dc9862dbb978d0ba57ab,
title = {Comparative Studies on Cost, Reliability and Resilience of Off-Grid Energy Systems},
author = {Wenzhu Li and Mohamed Galeela and Mathaios Panteli and Eduardo Alejandro Martinez Cesena and Pierluigi Mancarella},
doi = {10.1109/PowerTech46648.2021.9495025},
year = {2021},
date = {2021-06-28},
booktitle = {2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings},
pages = {1–6},
publisher = {IEEE},
address = {United States},
series = {2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings},
abstract = {Rural electrification is critical for the social and economic development of developing countries. Off-grid energy systems are attractive solutions for rural electrification which may offer technical and economic benefits compared with network expansion. However, given the conditions in remote areas, off-grid energy systems are often exposed to high impact and low probability (HILP) events such as natural hazards. This is an issue as off-grid planning practices traditionally focus on average performance reliability indicators, and neglect studying resilience to HILP events. This paper proposes a practical off-grid assessment framework which brings together average and risk performance indices to optimally size off-grid energy systems and quantify their reliability and resilience performance. The proposed methodology, based on Sequential Monte Carlo simulations, is demonstrated with two off-grid energy systems: PV and CHP based systems. In addition, through case studies with applications on windstorms, the impacts of natural hazards are systematically quantified and explicitly considered in the sizing of the off-grid plant. Power system planners and operators can use this framework to quantify the reliability and resilience of off-grid energy systems and inform the decision-making on optimal portfolios for rural electrification.},
note = {Funding Information: ACKNOWLEDGMENT The authors gratefully acknowledge the financial support of the EPSRC-funded “Techno-Economic framework for Resilient and Sustainable Electrification” (EP/R030294/1) and the Newton Prize project “Resilient Planning of Low-Carbon Power Systems”. Publisher Copyright: © 2021 IEEE.; 2021 IEEE Madrid PowerTech, PowerTech 2021 ; Conference date: 28-06-2021 Through 02-07-2021},
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Rural electrification is critical for the social and economic development of developing countries. Off-grid energy systems are attractive solutions for rural electrification which may offer technical and economic benefits compared with network expansion. However, given the conditions in remote areas, off-grid energy systems are often exposed to high impact and low probability (HILP) events such as natural hazards. This is an issue as off-grid planning practices traditionally focus on average performance reliability indicators, and neglect studying resilience to HILP events. This paper proposes a practical off-grid assessment framework which brings together average and risk performance indices to optimally size off-grid energy systems and quantify their reliability and resilience performance. The proposed methodology, based on Sequential Monte Carlo simulations, is demonstrated with two off-grid energy systems: PV and CHP based systems. In addition, through case studies with applications on windstorms, the impacts of natural hazards are systematically quantified and explicitly considered in the sizing of the off-grid plant. Power system planners and operators can use this framework to quantify the reliability and resilience of off-grid energy systems and inform the decision-making on optimal portfolios for rural electrification.
Vrain, Emilie; Wilson, Charlie
Social Influence in the Adoption of Digital Consumer Innovations to Mitigate Climate Change Proceedings Article
In: Zhu, Xianli; Dias, Gabriela Prata (Ed.): Conference Proceedings - BEHAVE 2020-2021: the 6th European Conference on Behaviour Change for Energy Efficiency, pp. 85–88, 2021.
@inproceedings{9cf2d6dfd0c844899fb7ea6c012c96b6,
title = {Social Influence in the Adoption of Digital Consumer Innovations to Mitigate Climate Change},
author = {Emilie Vrain and Charlie Wilson},
editor = {Xianli Zhu and Gabriela Prata Dias},
year = {2021},
date = {2021-06-01},
booktitle = {Conference Proceedings - BEHAVE 2020-2021: the 6th European Conference on Behaviour Change for Energy Efficiency},
pages = {85–88},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Edo-Osagie, Oduwa; Iglesia, Beatriz De La; Lake, Iain; Edeghere, Obaghe
An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis Proceedings Article
In: Cal, Enrique Antonio; Flecha, José Ramón Villar; Quintián, Héctor; Corchado, Emilio (Ed.): Hybrid Artificial Intelligent Systems, pp. 160–171, Springer, Germany, 2020, ISBN: 978-3-030-61705-9.
@inproceedings{7b9b694e198e4be4926906e1868d6fe1,
title = {An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis},
author = {Oduwa Edo-Osagie and Beatriz De La Iglesia and Iain Lake and Obaghe Edeghere},
editor = {Enrique Antonio Cal and José Ramón Villar Flecha and Héctor Quintián and Emilio Corchado},
doi = {10.1007/978-3-030-61705-9_14},
isbn = {978-3-030-61705-9},
year = {2020},
date = {2020-11-04},
booktitle = {Hybrid Artificial Intelligent Systems},
pages = {160–171},
publisher = {Springer},
address = {Germany},
series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
abstract = {In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.},
keywords = {},
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}
In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.
Alahamade, Wedad; Lake, Iain; Reeves, Claire E.; Iglesia, Beatriz De La
Clustering Imputation for Air Pollution Data Proceedings Article
In: Cal, Enrique Antonio; Flecha, José Ramón Villar; Quintián, Héctor; Corchado, Emilio (Ed.): Hybrid Artificial Intelligent Systems, pp. 585–597, Springer, Germany, 2020, ISBN: 978-3-030-61705-9.
@inproceedings{4c35d4275646498b85196b2120b7dbc4,
title = {Clustering Imputation for Air Pollution Data},
author = {Wedad Alahamade and Iain Lake and Claire E. Reeves and Beatriz De La Iglesia},
editor = {Enrique Antonio Cal and José Ramón Villar Flecha and Héctor Quintián and Emilio Corchado},
doi = {10.1007/978-3-030-61705-9_48},
isbn = {978-3-030-61705-9},
year = {2020},
date = {2020-11-04},
booktitle = {Hybrid Artificial Intelligent Systems},
pages = {585–597},
publisher = {Springer},
address = {Germany},
series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
abstract = {Air pollution is a global problem. The assessment of air pollution concentration data is important for evaluating human exposure and the associated risk to health. Unfortunately, air pollution monitoring stations often have periods of missing data or do not measure all pollutants. In this study, we experiment with different approaches to estimate the whole time series for a missing pollutant at a monitoring station as well as missing values within a time series. The main goal is to reduce the uncertainty in air quality assessment. To develop our approach we combine single and multiple imputation, nearest neighbour geographical distance methods and a clustering algorithm for time series. For each station that measures ozone, we produce various imputations for this pollutant and measure the similarity/error between the imputed and the real values. Our results show that imputation by average based on clustering results combined with multiple imputation for missing values is the most reliable and is associated with lower average error and standard deviation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Air pollution is a global problem. The assessment of air pollution concentration data is important for evaluating human exposure and the associated risk to health. Unfortunately, air pollution monitoring stations often have periods of missing data or do not measure all pollutants. In this study, we experiment with different approaches to estimate the whole time series for a missing pollutant at a monitoring station as well as missing values within a time series. The main goal is to reduce the uncertainty in air quality assessment. To develop our approach we combine single and multiple imputation, nearest neighbour geographical distance methods and a clustering algorithm for time series. For each station that measures ozone, we produce various imputations for this pollutant and measure the similarity/error between the imputed and the real values. Our results show that imputation by average based on clustering results combined with multiple imputation for missing values is the most reliable and is associated with lower average error and standard deviation.
Jones, Matthew
Climate change as an enabler of wildfire Proceedings Article
In: The DRIVER+ Project Report, pp. 12–17, EU H2020 Project: Driving Innovation in Crisis Management for European Resilience, 2020.
@inproceedings{1e0d3a95459146619318c446bc19d0ab,
title = {Climate change as an enabler of wildfire},
author = {Matthew Jones},
year = {2020},
date = {2020-02-18},
booktitle = {The DRIVER+ Project Report},
pages = {12–17},
publisher = {EU H2020 Project: Driving Innovation in Crisis Management for European Resilience},
abstract = {This paper gives a brief overview of the latest science on climate change, its drivers and its consequences for global wildfire risk. Human-driven climate change promotes the conditions on which wildfires depend, enhancing their likelihood and challenging suppression efforts.},
keywords = {},
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This paper gives a brief overview of the latest science on climate change, its drivers and its consequences for global wildfire risk. Human-driven climate change promotes the conditions on which wildfires depend, enhancing their likelihood and challenging suppression efforts.
Tedstone, Aleksander; Fletcher, Carly; Greer, Adam; Oster, Kamil; Clair, Rebecca St; Tomatis, Marco; Azapagic, Adisa; Franca, Rosa Cuellar; Garforth, Arthur; Hardacre, Chris; Sharmina, Maria
Sustainable Hospitals - Recycling Healthcare Plastics Proceedings Article
In: Plastic Research and Innovation Fund Conference, 2020.
@inproceedings{58f42f3d35644d169653244ee1eaa02ab,
title = {Sustainable Hospitals - Recycling Healthcare Plastics},
author = {Aleksander Tedstone and Carly Fletcher and Adam Greer and Kamil Oster and Rebecca St Clair and Marco Tomatis and Adisa Azapagic and Rosa Cuellar Franca and Arthur Garforth and Chris Hardacre and Maria Sharmina},
year = {2020},
date = {2020-01-01},
booktitle = {Plastic Research and Innovation Fund Conference},
keywords = {},
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Tedstone, Aleksander; Fletcher, Carly; Greer, Adam; Oster, Kamil; Clair, Rebecca St; Tomatis, Marco; Azapagic, Adisa; Franca, Rosa Cuellar; Garforth, Arthur; Hardacre, Chris; Sharmina, Maria
Sustainable Hospitals - Recycling Healthcare Plastics Proceedings Article
In: Plastic Research and Innovation Fund Conference, 2020.
@inproceedings{58f42f3d35644d169653244ee1eaa02a,
title = {Sustainable Hospitals - Recycling Healthcare Plastics},
author = {Aleksander Tedstone and Carly Fletcher and Adam Greer and Kamil Oster and Rebecca St Clair and Marco Tomatis and Adisa Azapagic and Rosa Cuellar Franca and Arthur Garforth and Chris Hardacre and Maria Sharmina},
year = {2020},
date = {2020-01-01},
booktitle = {Plastic Research and Innovation Fund Conference},
keywords = {},
pubstate = {published},
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}
2019
Zeferina, Vasco; Birch, Christina; Edwards, Rodger; Wood, Ruth
Sensitivity analysis of peak and annual space cooling load at simplified office dynamic building model Proceedings Article
In: E3S Web Conf. Volume 111, 2019 CLIMA 2019 Congress, 2019, (CLIMA 2019 Congress ; Conference date: 27-05-2019).
@inproceedings{f47ba25a9e3f43b38419b6f968c6f584,
title = {Sensitivity analysis of peak and annual space cooling load at simplified office dynamic building model},
author = {Vasco Zeferina and Christina Birch and Rodger Edwards and Ruth Wood},
doi = {10.1051/e3sconf/201911104038},
year = {2019},
date = {2019-08-13},
booktitle = {E3S Web Conf. Volume 111, 2019 CLIMA 2019 Congress},
volume = {111},
abstract = {The focused investigation of building design is necessary to understand and quantify the implication of different design parameters on their energy performance. The design of future buildings is a major challenge, as current designs may be inappropriate in a future with global warming due to climate change impacts. In addition this understanding is necessary to be able to predict timing and profile of future energy demand, which is crucial for the long-term planning of energy infrastructures – particularly electricity. In this paper, the Morris Elementary Effects method is used as a screening method, to identify the key parameters of the design and operation of office buildings that affect the estimation of space cooling peak load and annual energy demand. Internal heat gains, cooling set-point and ventilation rates are identified as the parameters with larger implications for both annual and peak space cooling demand. In future climate scenarios, the magnitude of change of annual space cooling demand is significantly (around five times) larger than the change in the peak demand. Asides from the potential increase of space cooling demand in future scenarios, the sensitivity of the space cooling demand relative to the change in design parameters is potentially much larger.},
note = {CLIMA 2019 Congress ; Conference date: 27-05-2019},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The focused investigation of building design is necessary to understand and quantify the implication of different design parameters on their energy performance. The design of future buildings is a major challenge, as current designs may be inappropriate in a future with global warming due to climate change impacts. In addition this understanding is necessary to be able to predict timing and profile of future energy demand, which is crucial for the long-term planning of energy infrastructures – particularly electricity. In this paper, the Morris Elementary Effects method is used as a screening method, to identify the key parameters of the design and operation of office buildings that affect the estimation of space cooling peak load and annual energy demand. Internal heat gains, cooling set-point and ventilation rates are identified as the parameters with larger implications for both annual and peak space cooling demand. In future climate scenarios, the magnitude of change of annual space cooling demand is significantly (around five times) larger than the change in the peak demand. Asides from the potential increase of space cooling demand in future scenarios, the sensitivity of the space cooling demand relative to the change in design parameters is potentially much larger.
Zeferina, Vasco; Birch, Christina; Edwards, Rodger; Wood, Ruth
Sensitivity analysis of peak and annual space cooling load at simplified office dynamic building model Proceedings Article
In: E3S Web Conf. Volume 111, 2019 CLIMA 2019 Congress, 2019, (CLIMA 2019 Congress ; Conference date: 27-05-2019).
@inproceedings{f47ba25a9e3f43b38419b6f968c6f584b,
title = {Sensitivity analysis of peak and annual space cooling load at simplified office dynamic building model},
author = {Vasco Zeferina and Christina Birch and Rodger Edwards and Ruth Wood},
doi = {10.1051/e3sconf/201911104038},
year = {2019},
date = {2019-08-13},
booktitle = {E3S Web Conf. Volume 111, 2019 CLIMA 2019 Congress},
volume = {111},
abstract = {The focused investigation of building design is necessary to understand and quantify the implication of different design parameters on their energy performance. The design of future buildings is a major challenge, as current designs may be inappropriate in a future with global warming due to climate change impacts. In addition this understanding is necessary to be able to predict timing and profile of future energy demand, which is crucial for the long-term planning of energy infrastructures – particularly electricity. In this paper, the Morris Elementary Effects method is used as a screening method, to identify the key parameters of the design and operation of office buildings that affect the estimation of space cooling peak load and annual energy demand. Internal heat gains, cooling set-point and ventilation rates are identified as the parameters with larger implications for both annual and peak space cooling demand. In future climate scenarios, the magnitude of change of annual space cooling demand is significantly (around five times) larger than the change in the peak demand. Asides from the potential increase of space cooling demand in future scenarios, the sensitivity of the space cooling demand relative to the change in design parameters is potentially much larger.},
note = {CLIMA 2019 Congress ; Conference date: 27-05-2019},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The focused investigation of building design is necessary to understand and quantify the implication of different design parameters on their energy performance. The design of future buildings is a major challenge, as current designs may be inappropriate in a future with global warming due to climate change impacts. In addition this understanding is necessary to be able to predict timing and profile of future energy demand, which is crucial for the long-term planning of energy infrastructures – particularly electricity. In this paper, the Morris Elementary Effects method is used as a screening method, to identify the key parameters of the design and operation of office buildings that affect the estimation of space cooling peak load and annual energy demand. Internal heat gains, cooling set-point and ventilation rates are identified as the parameters with larger implications for both annual and peak space cooling demand. In future climate scenarios, the magnitude of change of annual space cooling demand is significantly (around five times) larger than the change in the peak demand. Asides from the potential increase of space cooling demand in future scenarios, the sensitivity of the space cooling demand relative to the change in design parameters is potentially much larger.
Edo-Osagie, Oduwa; Iglesia, Beatriz De La; Lake, Iain; Edeghere, Obaghe
Deep Learning for Relevance Filtering in Syndromic Surveillance: A Case Study in Asthma/Difficulty Breathing Proceedings Article
In: Marsico, Maria De; di Baja, Gabriella Sanniti; Fred, Ana L. N. (Ed.): Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods ICPRAM, pp. 491–500, Science and Technology Publications, Lda, 2019, ISBN: 9789897583513, (Funding Information: We acknowledge support from NHS 111 and NHS Digital for their assistance and support with the NHS 111 system; Out-of-Hours providers submitting data to the GPOOH syndromic surveillance and Advanced Heath & Care. The authors also acknowledge support from the Public Health England Real-time Syndromic Surveillance Team. Beatriz De La Iglesia and Iain Lake receive support from the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response. Publisher Copyright: © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 8th International Conference on Pattern Recognition Applications and Methods , ICPRAM 2019 ; Conference date: 19-02-2019 Through 21-02-2019).
@inproceedings{2392d2e910784a9fa6727b4ad68edc32,
title = {Deep Learning for Relevance Filtering in Syndromic Surveillance: A Case Study in Asthma/Difficulty Breathing},
author = {Oduwa Edo-Osagie and Beatriz De La Iglesia and Iain Lake and Obaghe Edeghere},
editor = {Maria De Marsico and Gabriella Sanniti di Baja and Ana L. N. Fred},
url = {http://www.icpram.org},
doi = {10.5220/0007366904910500},
isbn = {9789897583513},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods ICPRAM},
pages = {491–500},
publisher = {Science and Technology Publications, Lda},
series = {International Conference on Pattern Recognition Applications and Methods},
abstract = {In this paper, we investigate deep learning methods that may extract some word context for Twitter mining for syndromic surveillance. Most of the work on syndromic surveillance has been done on the flu or Influenza- Like Illnesses (ILIs). For this reason, we decided to look at a different but equally important syndrome, asthma/difficulty breathing, as this is quite topical given global concerns about the impact of air pollution. We also compare deep learning algorithms for the purpose of filtering Tweets relevant to our syndrome of interest, asthma/difficulty breathing. We make our comparisons using different variants of the F-measure as our evaluation metric because they allow us to emphasise recall over precision, which is important in the context of syndromic surveillance so that we do not lose relevant Tweets in the classification. We then apply our relevance filtering systems based on deep learning algorithms, to the task of syndromic surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE).We find that the RNN performs best at relevance filtering but can also be slower than other architectures which is important for consideration in real-time application. We also found that the correlation between Twitter and the real-world asthma syndromic surveillance data was positive and improved with the use of the deep- learning-powered relevance filtering. Finally, the deep learning methods enabled us to gather context and word similarity information which we can use to fine tune the vocabulary we employ to extract relevant Tweets in the first place.},
note = {Funding Information: We acknowledge support from NHS 111 and NHS Digital for their assistance and support with the NHS 111 system; Out-of-Hours providers submitting data to the GPOOH syndromic surveillance and Advanced Heath & Care. The authors also acknowledge support from the Public Health England Real-time Syndromic Surveillance Team. Beatriz De La Iglesia and Iain Lake receive support from the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response. Publisher Copyright: © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 8th International Conference on Pattern Recognition Applications and Methods , ICPRAM 2019 ; Conference date: 19-02-2019 Through 21-02-2019},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we investigate deep learning methods that may extract some word context for Twitter mining for syndromic surveillance. Most of the work on syndromic surveillance has been done on the flu or Influenza- Like Illnesses (ILIs). For this reason, we decided to look at a different but equally important syndrome, asthma/difficulty breathing, as this is quite topical given global concerns about the impact of air pollution. We also compare deep learning algorithms for the purpose of filtering Tweets relevant to our syndrome of interest, asthma/difficulty breathing. We make our comparisons using different variants of the F-measure as our evaluation metric because they allow us to emphasise recall over precision, which is important in the context of syndromic surveillance so that we do not lose relevant Tweets in the classification. We then apply our relevance filtering systems based on deep learning algorithms, to the task of syndromic surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE).We find that the RNN performs best at relevance filtering but can also be slower than other architectures which is important for consideration in real-time application. We also found that the correlation between Twitter and the real-world asthma syndromic surveillance data was positive and improved with the use of the deep- learning-powered relevance filtering. Finally, the deep learning methods enabled us to gather context and word similarity information which we can use to fine tune the vocabulary we employ to extract relevant Tweets in the first place.
2018
Manggala, Meilati Ligardini; Sunkar, Arzyana; Carmenta, Rachel
Local perceptions of oil palm plantation: Case study in Kumai subdistrict, Central Kalimantan Proceedings Article
In: Huyop, Fahrul Zaman Bin; Siswanto, Dian; Mastuti, Retno; Treesubsuntorn, Chairat (Ed.): 9th International Conference on Global Resource Conservation, ICGRC and AJI from Ritsumeikan University, American Institute of Physics Inc., 2018, (Funding Information: This work was supported by BPDPKS (Indonesian Oil Palm Estate Fund Agency). Thanks are also forwarded to GAPKI (Indonesian Palm Oil Associations) and PT. ASMR for the permissions and the facilities provided during the study period. Publisher Copyright: textcopyright 2018 Author(s).; 9th International Conference on Global Resource Conservation and AJI from Ritsumeikan University, ICGRC 2018 ; Conference date: 07-03-2018 Through 08-03-2018).
@inproceedings{6d7494e7574b40c1b1a5f4701f40cb8f,
title = {Local perceptions of oil palm plantation: Case study in Kumai subdistrict, Central Kalimantan},
author = {Meilati Ligardini Manggala and Arzyana Sunkar and Rachel Carmenta},
editor = {Fahrul Zaman Bin Huyop and Dian Siswanto and Retno Mastuti and Chairat Treesubsuntorn},
doi = {10.1063/1.5061876},
year = {2018},
date = {2018-10-10},
booktitle = {9th International Conference on Global Resource Conservation, ICGRC and AJI from Ritsumeikan University},
publisher = {American Institute of Physics Inc.},
series = {AIP Conference Proceedings},
abstract = {Being directly affected by oil palm plantation development, local people's opinions need to be acknowledged as they indicate the future development of the plantation. This study was conducted to analyse the local people's perceptions of oil palm plantations. A five-point Likert scale household survey was conducted in August 2017 in one hamlet and two villages where a new oil palm plantation had just been developed (trees planted in 2013). The statements were read by a surveyor to 97 respondents selected using convenience sampling. The results were analysed using descriptive qualitative analysis. The study found that the local people's perceptions of oil palm plantations had both common and distinct results, based on the location and community characteristics. People in the three study sites showed some common perceptions towards the economic and social impacts of the oil palm plantation, with economic impacts receiving the most positive perceptions. People showed the most positive attitudes towards the plantation's impacts in increasing employment (95.56%) and decreasing urbanisation (95.56%). On the contrary, their perceptions of the plantation's environmental impacts received the lowest positive perceptions. However, unlike the negative accusations towards the plantation's impact on forest and land fires, all respondents agreed that the company did not cause the fires.},
note = {Funding Information: This work was supported by BPDPKS (Indonesian Oil Palm Estate Fund Agency). Thanks are also forwarded to GAPKI (Indonesian Palm Oil Associations) and PT. ASMR for the permissions and the facilities provided during the study period. Publisher Copyright: textcopyright 2018 Author(s).; 9th International Conference on Global Resource Conservation and AJI from Ritsumeikan University, ICGRC 2018 ; Conference date: 07-03-2018 Through 08-03-2018},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Being directly affected by oil palm plantation development, local people's opinions need to be acknowledged as they indicate the future development of the plantation. This study was conducted to analyse the local people's perceptions of oil palm plantations. A five-point Likert scale household survey was conducted in August 2017 in one hamlet and two villages where a new oil palm plantation had just been developed (trees planted in 2013). The statements were read by a surveyor to 97 respondents selected using convenience sampling. The results were analysed using descriptive qualitative analysis. The study found that the local people's perceptions of oil palm plantations had both common and distinct results, based on the location and community characteristics. People in the three study sites showed some common perceptions towards the economic and social impacts of the oil palm plantation, with economic impacts receiving the most positive perceptions. People showed the most positive attitudes towards the plantation's impacts in increasing employment (95.56%) and decreasing urbanisation (95.56%). On the contrary, their perceptions of the plantation's environmental impacts received the lowest positive perceptions. However, unlike the negative accusations towards the plantation's impact on forest and land fires, all respondents agreed that the company did not cause the fires.
Ikoro, V.; Sharmina, M.; Malik, K.; Batista-Navarro, R.
Analyzing Sentiments Expressed on Twitter by UK Energy Company Consumers Proceedings Article
In: 2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018, 2018.
@inproceedings{5acee1c4ec3d4a9784d860bc26fec2c2b,
title = {Analyzing Sentiments Expressed on Twitter by UK Energy Company Consumers},
author = {V. Ikoro and M. Sharmina and K. Malik and R. Batista-Navarro},
doi = {10.1109/SNAMS.2018.8554619},
year = {2018},
date = {2018-01-01},
booktitle = {2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ikoro, V.; Sharmina, M.; Malik, K.; Batista-Navarro, R.
Analyzing Sentiments Expressed on Twitter by UK Energy Company Consumers Proceedings Article
In: 2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018, 2018.
@inproceedings{5acee1c4ec3d4a9784d860bc26fec2c2,
title = {Analyzing Sentiments Expressed on Twitter by UK Energy Company Consumers},
author = {V. Ikoro and M. Sharmina and K. Malik and R. Batista-Navarro},
doi = {10.1109/SNAMS.2018.8554619},
year = {2018},
date = {2018-01-01},
booktitle = {2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}