Deep Ensemble Learning Model for Short-Term Load Forecasting within Active Learning Framework

Zengping Wang, Bing Zhao, Haibo Guo, Lingling Tang and Yuexing Peng
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Zengping Wang: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Bing Zhao: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Haibo Guo: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Lingling Tang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Yuexing Peng: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Energies, 2019, vol. 12, issue 20, 1-13

Abstract: Short term load forecasting (STLF) is one of the basic techniques for economic operation of the power grid. Electrical load consumption can be affected by both internal and external factors so that it is hard to forecast accurately due to the random influencing factors such as weather. Besides complicated and numerous internal patterns, electrical load shows obvious yearly, seasonal, and weekly quasi-periodicity. Traditional regression-based models and shallow neural network models cannot accurately learn the complicated inner patterns of the electrical load. Long short-term memory (LSTM) model features a strong learning capacity to capture the time dependence of the time series and presents the state-of-the-art performance. However, as the time span increases, LSTM becomes much harder to train because it cannot completely avoid the vanishing gradient problem in recurrent neural networks. Then, LSTM models cannot capture the dependence over large time span which is of potency to enhance STLF. Moreover, electrical loads feature data imbalance where some load patterns in high/low temperature zones are more complicated but occur much less often than those in mild temperature zones, which severely degrades the LSTM-based STLF algorithms. To fully exploit the information beneath the high correlation of load segments over large time spans and combat the data imbalance, a deep ensemble learning model within active learning framework is proposed, which consists of a selector and a predictor. The selector actively selects several key load segments with the most similar pattern as the current one to train the predictor, and the predictor is an ensemble learning-based deep learning machine integrating LSTM and multi-layer preceptor (MLP). The LSTM is capable of capturing the short-term dependence of the electrical load, and the MLP integrates both the key history load segments and the outcome of LSTM for better forecasting. The proposed model was evaluated over an open dataset, and the results verify its advantage over the existing STLF models.

Keywords: short-term load forecasting; long short-term memory; active learning; deep ensemble learning (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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