Revisit Neural Network based Load Forecasting

Yingshan Tao, Fei Zhao, Haoliang Yuan, Chun Sing Lai, Zhao Xu, Wing Ng, Rongwei Li, Xuecong Li, Loi Lei Lai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The application of artificial neural network to load forecasting can overcome the problem of dynamic load change, and its ability to adapt to nonlinear relationships makes the prediction result satisfactory. This paper firstly reviews and introduces the concepts and basic principles of load prediction, discusses various methods for load forecasting, and then selects artificial neural network to establish a predictive model. In this paper, the European electric load is predicted with a BP neural network. From the prediction results, it is feasible to use BP neural network for load forecasting, and its accuracy can meet the needs of real-life engineering work. However, BP neural networks have the problem of slow convergence and easily falling into local minimum points. Therefore, this paper also uses three other neural networks for load forecasting, which are Radial Basis Network (RBF), Elman Network, and Long-Short Term Memory Network (LSTM). In the experiment, the four neural networks achieved expected prediction results, and the LSTM network had the best prediction effect. Scientific discussions are offered.

Original languageEnglish
Title of host publication2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131924
DOIs
Publication statusPublished - Dec 2019
Event20th International Conference on Intelligent System Application to Power Systems, ISAP 2019 - New Delhi, India
Duration: 10 Dec 201914 Dec 2019

Publication series

Name2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019

Conference

Conference20th International Conference on Intelligent System Application to Power Systems, ISAP 2019
Country/TerritoryIndia
CityNew Delhi
Period10/12/1914/12/19

Keywords

  • back propagation
  • Elman Network
  • load forecasting
  • long-short term memory
  • neural networks
  • radial basis function

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization

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