Daily peak electrical load forecasting with a multi-resolution approach
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DOI: 10.1016/j.ijforecast.2022.06.001
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- Niematallah Elamin & Mototsugu Fukushige, 2018. "Quantile Regression Model for Peak Load Demand Forecasting with Approximation by Triangular Distribution to Avoid Blackouts," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 119-124.
- Amato, Umberto & Antoniadis, Anestis & De Feis, Italia & Goude, Yannig & Lagache, Audrey, 2021. "Forecasting high resolution electricity demand data with additive models including smooth and jagged components," International Journal of Forecasting, Elsevier, vol. 37(1), pages 171-185.
- Bercedis Peterson & Frank E. Harrell, 1990. "Partial Proportional Odds Models for Ordinal Response Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(2), pages 205-217, June.
- Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
- 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.
- Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
- Anestis Antoniadis & Efstathios Paparoditis & Theofanis Sapatinas, 2006. "A functional wavelet–kernel approach for time series prediction," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(5), pages 837-857, November.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Uddin, Moslem & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Abd Halim, Syahirah & Abu Bakar, Ab Halim & Chia Kwang, Tan, 2018. "A review on peak load shaving strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3323-3332.
- Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
- Dong-Hoon Kim & Eun-Kyu Lee & Naik Bakht Sania Qureshi, 2020. "Peak-Load Forecasting for Small Industries: A Machine Learning Approach," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
- Saxena, Harshit & Aponte, Omar & McConky, Katie T., 2019. "A hybrid machine learning model for forecasting a billing period’s peak electric load days," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1288-1303.
- Yang, Yandong & Hong, Weijun & Li, Shufang, 2019. "Deep ensemble learning based probabilistic load forecasting in smart grids," Energy, Elsevier, vol. 189(C).
- Sigauke, C. & Chikobvu, D., 2011. "Prediction of daily peak electricity demand in South Africa using volatility forecasting models," Energy Economics, Elsevier, vol. 33(5), pages 882-888, September.
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Keywords
Generalised additive models; Neural networks; Peak load forecasting; Smart grids; Automated feature engineering; Multi-resolution;All these keywords.
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