Yang, Jiajia, Liu, Guolong, Zhao, Junhua, Wen, Fushuan, and Dong, Zhaoyang (2018) A Long Short-Term Memory Based Deep Learning Method for Industrial Load Forecasting. Electric Power Construction, 39 (10). pp. 20-27.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

Industrial load is mainly determined by the production schedule of a given factory, with a lower correlation to external factors such as temperature, time and demography. Under electricity market environment, accurate forecasting of industrial load is essential for industry end-users to develop profitable transaction plans. Given this background, this paper studied the short-term forecasting of industrial load and proposed a long short term memory (LSTM) network based deep learning algorithm for this purpose. Compared with some existing methods, the proposed LSTM-based deep learning algorithm is improved with respect to the following aspects. First, the proposed algorithm extended the layers of the deep learning network. Thus, both the capability of the network to extract information from historical data and the capability to forecast future load are strengthened. Secondly, the dropout technique is applied to each hidden layer composed of LSTM blocks, where a probability is assigned to a hidden unit in each layer of the network. The dropout technique can prevent the neural network from overfitting through the regularization. Consequently, the overall performance of the neural network is improved. Next, actual historical data of industrial load are used to test the proposed method. Case study results show that the proposed method can significantly improve the forecasting accuracy comparing with the auto-regressive and moving average model (ARMA), K nearest neighbor regression (KNN) and support vector regression (SVR) methods. Besides, the forecasting error measured by the mean absolute percentage error (MAPE) is less than 9% with the proposed forecasting method.

Item ID:	82456
Item Type:	Article (Research - C1)
ISSN:	1000-7229
Copyright Information:	© 2018 State Power Economic Research Institute. All rights reserved.
Date Deposited:	17 Jun 2024 23:38
FoR Codes:	40 ENGINEERING > 4008 Electrical engineering > 400805 Electrical energy transmission, networks and systems @ 100%
SEO Codes:	17 ENERGY > 1703 Energy storage, distribution and supply > 170309 Smart grids @ 100%
Downloads:	Total: 3
	More Statistics