A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method

Sanlei Dang, Long Peng, Jingming Zhao, Jiajie Li and Zhengmin Kong
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Sanlei Dang: Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China
Long Peng: Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China
Jingming Zhao: Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China
Jiajie Li: Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China
Zhengmin Kong: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2022, vol. 15, issue 2, 1-20

Abstract: In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made multimodal spatial–temporal feature extraction is proposed, which integrates spatial features, time information, load, and electricity price to obtain more covert features. Thirdly, relying on quantile regression random forest, the probabilistic forecasting method is proposed, which exploits the results from the above three short-term load point forecasting models. Lastly, the experimental results demonstrate that the proposed method outperforms its conventional counterparts.

Keywords: short-term load forecasting; load point forecasting; LSTM; CNN; quantile regression random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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