|
Short-term power load forecasting using recurrent neural network with restricted Boltzmann machine |
DOI:10.7667/PSPC171255 |
Key Words:power load forecasting recurrent neural network restricted Boltzmann machine time series |
Author Name | Affiliation | LI Ruochen | State Grid Hebei Power Co., Ltd.Pingshan County Power Supply Branch, Pingshan 050400, China | ZHU Fan | Philosophy Department, Capital Normal University, Beijing 100037, China | ZHU Yongli | School of Control and Computer Engineering, North China Electric Power University, Baoding 071000, China | ZHAI Yujia | Department of Electrical Engineering, North China Electric Power University, Baoding 071000, China |
|
Hits: 3651 |
Download times: 1560 |
Abstract:The importance of short-term power load forecasting continues to improve with the development of power enterprises. Although the traditional load forecast has been developed relatively mature, the load forecast accuracy requirements gradually increase now. To meet the development needs, existing methods should be improved or new methods of prediction should be established. This paper analyzes the periodic load forecasting and periodic characteristics of load forecasting data. It also combines recursive neural networks with Limited Boltzmann machine's strong unsupervised learning ability in analyzing the unique advantages of time series data. The network principle and training process of the combination are described and the electric load data are predicted by experiment. The experiment compares the accuracy of the network and other networks in short-term load forecasting. The results show that the neural network proposed in this paper is more accurate than other networks in the power short-term load prediction experiment. This work is supported by National Natural Science Foundation of China (No. 51677072). |
View Full Text View/Add Comment Download reader |
|
|
|