TY - GEN
T1 - Revisit Neural Network based Load Forecasting
AU - Tao, Yingshan
AU - Zhao, Fei
AU - Yuan, Haoliang
AU - Lai, Chun Sing
AU - Xu, Zhao
AU - Ng, Wing
AU - Li, Rongwei
AU - Li, Xuecong
AU - Lai, Loi Lei
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - back propagation
KW - Elman Network
KW - load forecasting
KW - long-short term memory
KW - neural networks
KW - radial basis function
UR - http://www.scopus.com/inward/record.url?scp=85084304004&partnerID=8YFLogxK
U2 - 10.1109/ISAP48318.2019.9065930
DO - 10.1109/ISAP48318.2019.9065930
M3 - Conference article published in proceeding or book
AN - SCOPUS:85084304004
T3 - 2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019
BT - 2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019
Y2 - 10 December 2019 through 14 December 2019
ER -
