Outputs (6)

Cost, context, or convenience? Exploring the social acceptance of demand response in the United Kingdom (2021)
Journal Article
Naghiyev, E., Shipman, R., Goulden, M., Gillott, M., & Spence, A. (2022). Cost, context, or convenience? Exploring the social acceptance of demand response in the United Kingdom. Energy Research and Social Science, 87, Article 102469. https://doi.org/10.1016/j.erss.2021.102469

The energy sector, and buildings in particular, are one of the main contributors to climate change. Demand-Side Management (DSM) has the potential to realise energy savings on the demand as well as the supply side. However, the domestic sector still... Read More about Cost, context, or convenience? Exploring the social acceptance of demand response in the United Kingdom.

Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic (2021)
Journal Article
Shipman, R., Roberts, R., Waldron, J., Rimmer, C., Rodrigues, L., & Gillott, M. (2021). Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic. Energies, 14(21), Article 7176. https://doi.org/10.3390/en14217176

Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a ser... Read More about Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic.

Assessing the impact of lockdown due to COVID-19 on the electricity consumption of a housing development in the UK (2021)
Book Chapter
Tubelo, R., Naghiyev, E., Gillot, M., Rodrigues, L., & Shipman, R. (2021). Assessing the impact of lockdown due to COVID-19 on the electricity consumption of a housing development in the UK. In J. R. Littlewood, R. J. Howlett, & L. C. Jain (Eds.), Sustainability in Energy and Buildings 2021 (45-55). Springer. https://doi.org/10.1007/978-981-16-6269-0_4

In March 2020, the United Kingdom (UK) government ruled that householders must stay home as a response to the COVID-19 outbreak to help flatten the curve of the epidemic and reduce the exponential growth of the virus. Commercial activities, workplace... Read More about Assessing the impact of lockdown due to COVID-19 on the electricity consumption of a housing development in the UK.

User engagement in community energy schemes: A case study at the Trent Basin in Nottingham, UK (2020)
Journal Article
Rodrigues, L., Gillott, M., Waldron, J., Cameron, L., Tubelo, R., Shipman, R., …Bradshaw-Smith, C. (2020). User engagement in community energy schemes: A case study at the Trent Basin in Nottingham, UK. Sustainable Cities and Society, 61, Article 102187. https://doi.org/10.1016/j.scs.2020.102187

© 2020 Elsevier Ltd ‘Community Energy’ refers to people working together to reduce and manage energy use and increase and support local energy generation. It has the potential to support the infrastructural, social and cultural changes needed to redu... Read More about User engagement in community energy schemes: A case study at the Trent Basin in Nottingham, UK.

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid (2020)
Journal Article
Shipman, R., Waldron, J., Naylor, S., Pinchin, J., Rodrigues, L., & Gillott, M. (2020). Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid. Energies, 13(8), Article 1933. https://doi.org/10.3390/en13081933

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Vehicle‐to‐... Read More about Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid.

Learning capacity: predicting user decisions for vehicle-to-grid services (2019)
Journal Article
Shipman, R., Naylor, S., Pinchin, J., Gough, R., & Gillott, M. (2019). Learning capacity: predicting user decisions for vehicle-to-grid services. Energy Informatics, 2(1), Article 37. https://doi.org/10.1186/s42162-019-0102-2

The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as import... Read More about Learning capacity: predicting user decisions for vehicle-to-grid services.