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

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 algor...

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Bibliographic Details
Published in:	Energies (Basel) Vol. 13; no. 10; p. 2640
Main Authors:	Park, Rae-Jun, Song, Kyung-Bin, Kwon, Bo-Sung
Format:	Journal Article
Language:	English
Published:	MDPI AG 01.05.2020
Subjects:	backpropagation neural network (BPNN) deep Q-network (DQN) long short-term memory (LSTM) reinforcement learning algorithm short-term load forecasting
ISSN:	1996-1073, 1996-1073
Online Access:	Get full text
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Summary:	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.
ISSN:	1996-1073 1996-1073
DOI:	10.3390/en13102640