Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland
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DOI: 10.1016/j.jup.2022.101446
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- McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
- Räsänen, Teemu & Voukantsis, Dimitrios & Niska, Harri & Karatzas, Kostas & Kolehmainen, Mikko, 2010. "Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data," Applied Energy, Elsevier, vol. 87(11), pages 3538-3545, November.
- Pan He & Jing Liang & Yueming (Lucy) Qiu & Qingran Li & Bo Xing, 2020. "Increase in domestic electricity consumption from particulate air pollution," Nature Energy, Nature, vol. 5(12), pages 985-995, December.
- Ürge-Vorsatz, Diana & Cabeza, Luisa F. & Serrano, Susana & Barreneche, Camila & Petrichenko, Ksenia, 2015. "Heating and cooling energy trends and drivers in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 85-98.
- Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
- Yating Li & William A. Pizer & Libo Wu, 2019. "Climate change and residential electricity consumption in the Yangtze River Delta, China," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(2), pages 472-477, January.
- Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
- Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
- Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
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Cited by:
- Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
- Pratik Mochi & Kartik Pandya & Joao Soares & Zita Vale, 2023. "Optimizing Power Exchange Cost Considering Behavioral Intervention in Local Energy Community," Mathematics, MDPI, vol. 11(10), pages 1-15, May.
- Li, Zhen & Niu, Shuwen & Halleck Vega, Sol Maria & Wang, Jinnian & Wang, Dakang & Yang, Xiankun, 2024. "Electrification and residential well-being in China," Energy, Elsevier, vol. 294(C).
- Brown, Alastair & Hampton, Harrison & Foley, Aoife & Furszyfer Del Rio, Dylan & Lowans, Christopher & Caulfield, Brian, 2023. "Understanding domestic consumer attitude and behaviour towards energy: A study on the Island of Ireland," Energy Policy, Elsevier, vol. 181(C).
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Keywords
Residential electricity consumption; Household load profiles; Machine learning;All these keywords.
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