Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning

Rae-Jun Park, Kyung-Bin Song and Bo-Sung Kwon
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Rae-Jun Park: Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea
Kyung-Bin Song: Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea
Bo-Sung Kwon: Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea

Energies, 2020, vol. 13, issue 10, 1-19

Abstract: Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

Keywords: short-term load forecasting; deep Q-network (DQN); backpropagation neural network (BPNN); long short-term memory (LSTM); reinforcement learning algorithm (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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