• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2021, Vol. 42 ›› Issue (9): 120-128.doi: 10.12204/j.issn.1000-7229.2021.09.013

• Smart Grid • Previous Articles     Next Articles

Short-Term Power Load Forecasting Based on Phase Space Reconstruction and Stochastic Configuration Networks

ZHAO Yunwen1,2, LI Peng1,2, SUN Yuhao3, SHEN Xin4, YANG Xiaohua4   

  1. 1. School of Information, Yunnan University, Kunming 650500, China
    2. Internet of Things Technology and Application Key Laboratory of Universities in Yunnan, Kunming 650500, China
    3. CTC Intelligence (Shenzhen) Tech Co., Ltd., Shenzhen 518000, Guangdong Province, China
    4. Yunnan Power Grid Co., Ltd., Kunming 650217, China
  • Received:2020-12-16 Online:2021-09-01 Published:2021-09-02
  • Contact: LI Peng
  • Supported by:
    National Natural Science Foundation of China(61763049)

Abstract:

Aiming at the problem of the non-linear characteristics of the distribution network load changing with time and space, the short-term load forecasting accuracy is insufficient and the model training time cost is high. In this paper, a short-term load forecasting model based on phase space reconstruction (PSR) and stochastic configuration network (SCN) is designed. Firstly, the meteorological data related to the load in the distribution network data is reduced by principal component analysis (PCA), and is combined with the load sequence to form a multivariable time series. Using chaotic time series theory, the paper reconstructs the phase space through mutual information method and false nearest neighbor method, and finally uses stochastic configuration network to predict power load. The proposed method is verified with historical load and meteorological data of public data sets of European power grid. The results show that, compared with the support vector machine (SVM) optimized by the grid search method, backpropagation neural network (BP), long and short-term memory network (LSTM), and autoregressive integrated moving average (ARIMA), the proposed method can complete load forecasting relatively, accurately and efficiently. The analysis of the calculation example verifies that the proposed method has the characteristics of high level of intelligence and efficient operation, and has certain practical value.

Key words: distribution network, short-term load forecasting, phase space reconstruction (PSR), principal component analysis (PCA), stochastic configuration network(SCN)

CLC Number: