Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand
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DOI: 10.1016/j.apenergy.2020.114752
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- Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003.
"Underlying trends and seasonality in UK energy demand: a sectoral analysis,"
Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
- Hunt, L.C. & Judge, G. & Ninomiya, Y., 2000. "Underlying Trends and Seasonality in UK Energy Demands: A Sectorial Analysis," Papers 134, Portsmouth University - Department of Economics.
- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
- Chen Zhang & Hua Liao & Zhifu Mi, 2019. "Climate impacts: temperature and electricity consumption," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1259-1275, December.
- Osamu Kimura and Ken-Ichiro Nishio, 2016. "Responding to electricity shortfalls: Electricity-saving activities of households and firms in Japan after Fukushima," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 1).
- Keita Honjo & Hiroto Shiraki & Shuichi Ashina, 2018. "Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
- Al-Garni, Ahmed Z. & Zubair, Syed M. & Nizami, Javeed S., 1994. "A regression model for electric-energy-consumption forecasting in Eastern Saudi Arabia," Energy, Elsevier, vol. 19(10), pages 1043-1049.
- Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
- Huebner, Gesche & Shipworth, David & Hamilton, Ian & Chalabi, Zaid & Oreszczyn, Tadj, 2016. "Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes," Applied Energy, Elsevier, vol. 177(C), pages 692-702.
- Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008.
"An hourly periodic state space model for modelling French national electricity load,"
International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
- V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
- Cho, Seong-Hoon & Tanaka, Katsuya & Wu, Junjie & Robert, Roland K. & Kim, Taeyoung, 2016. "Effects of nuclear power plant shutdowns on electricity consumption and greenhouse gas emissions after the Tohoku Earthquake," Energy Economics, Elsevier, vol. 55(C), pages 223-233.
- Fujimi, Toshio & Chang, Stephanie E., 2014. "Adaptation to electricity crisis: Businesses in the 2011 Great East Japan triple disaster," Energy Policy, Elsevier, vol. 68(C), pages 447-457.
- Miller, Reid & Golab, Lukasz & Rosenberg, Catherine, 2017. "Modelling weather effects for impact analysis of residential time-of-use electricity pricing," Energy Policy, Elsevier, vol. 105(C), pages 534-546.
- Isamu Matsukawa, 2016. "Consumer Energy Conservation Behavior After Fukushima," SpringerBriefs in Economics, Springer, number 978-981-10-1097-2, June.
- Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.
- V. Ramesh Kumar & Pradipkumar Dixit, 2018. "Artificial Neural Network Model for Hourly Peak Load Forecast," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 155-160.
- Abdel-Aal, R.E. & Al-Garni, A.Z., 1997. "Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis," Energy, Elsevier, vol. 22(11), pages 1059-1069.
- Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
- Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
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- Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).
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Keywords
Factor analysis; Machine learning; Hourly electricity demand; Partially linear additive model; Sparse modeling;All these keywords.
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