A LASSO-GRBFN-based Method for Electricity Price Forecasting with BSO
Author:
Affiliation:
1. Department of Network Design, Meiji University
Publisher
Institute of Electrical Engineers of Japan (IEE Japan)
Subject
Electrical and Electronic Engineering
Link
https://www.jstage.jst.go.jp/article/ieejeiss/143/2/143_125/_pdf
Reference27 articles.
1. (1) R. C. Garcia, et al.: “A GARCH Forecasting Model to Predict Day-Ahead Electricity Prices”, IEEE Trans. on Power Systems, Vol. 20, No. 2, pp. 867-874 (2005)
2. (2) A. J. Conejo, et al.: “Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models”, IEEE Trans. on Power Systems, Vol. 20, No. 2, pp. 1035-1042 (2005)
3. (3) A. Omura, Y. Fujimoto, et al.: “Forecast of Area Prices in Japanese Day-Ahead Electricity Market Using Open Data-Examination of Approach to Combine Multiple Forecasting Schemes-”, IEEJ Trans. on Power and Energy, Vol. 141, No. 5, pp. 366-373 (2021) (in Japanese)
4. 大村愛花・藤本 悠, 他:「オープンデータに基づく電力スポット市場のエリア価格予測―複数予測スキームの統合アプローチの検討―」, 電学論B, Vol. 141, No. 5, pp. 366-373 (2021)
5. (4) C. Sun, et al.: “Day-Ahead Electricity Price Forecasting Strategy Based on Machine Learning and Optimization Algorithm”, Proc. of 4th Asia Energy and Electrical Engineering Symposium (AEEES2022), pp. 254-259 (2022)
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