Electricity consumption forecasting for integrated power system with seasonal patterns
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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.
- Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
- Hyndman, Rob J. & Khandakar, Yeasmin, 2008.
"Automatic Time Series Forecasting: The forecast Package for R,"
Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
- Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
- Haeran Cho & Yannig Goude & Xavier Brossat & Qiwei Yao, 2013. "Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 7-21, March.
- Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
- Clements, A.E. & Hurn, A.S. & Li, Z., 2016.
"Forecasting day-ahead electricity load using a multiple equation time series approach,"
European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
- Adam Clements & Stan Hurn & Zili Li, 2014. "Forecasting day-ahead electricity load using a multiple equation time series approach," NCER Working Paper Series 103, National Centre for Econometric Research, revised 06 May 2015.
- Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
- Cho, Seong-Hoon & Kim, Taeyoung & Kim, Hyun Jae & Park, Kihyun & Roberts, Roland K., 2015.
"Regionally-varying and regionally-uniform electricity pricing policies compared across four usage categories,"
Energy Economics, Elsevier, vol. 49(C), pages 182-191.
- Cho, Seong-Hoon & Kim, Taeyoung & Kim, Hyun J. & Park, Kihyun & Roberts, Roland K., 2015. "Regionally-varying and Regionally-uniform Electricity Pricing Policies Compared across Four Usage Categories," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204324, Agricultural and Applied Economics Association.
- Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
- Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
- Cho, Haeran & Goude, Yannig & Brossat, Xavier & Yao, Qiwei, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics 49634, London School of Economics and Political Science, LSE Library.
- Ozhegov, Evgeniy & Popova, Evgeniya, 2017. "Demand for electricity and weather conditions: Nonparametric analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 46, pages 55-73.
- Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016.
"Improving short term load forecast accuracy via combining sister forecasts,"
Energy, Elsevier, vol. 98(C), pages 40-49.
- Jakub Nowotarski & Bidong Liu & Rafal Weron & Tao Hong, 2015. "Improving short term load forecast accuracy via combining sister forecasts," HSC Research Reports HSC/15/05, Hugo Steinhaus Center, Wroclaw University of Technology.
- Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
- Harvey, Andrew, 2006. "Forecasting with Unobserved Components Time Series Models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 7, pages 327-412, Elsevier.
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More about this item
Keywordswholesale electricity market; electricity consumption forecasting; deterministic seasonality; stochastic seasonality;
All these keywords.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
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