• 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 ›› 2018, Vol. 39 ›› Issue (11): 42-50.doi: 10.3969/j.issn.1000-7229.2018.11.006

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Short-term Power Load Forecasting Based on Deep Forest Algorithm

CHEN Lüpeng1,2, YIN Linfei3, YU Tao1,2, WANG Keying1,2   

  1. 1. College of Electric Power Engineering, South China University of Technology, Guangzhou  510640, China;2. Guangdong Key Laboratory of Clean Energy Technology, Guangzhou 510640, China;3. College of Electrical Engineering, Guangxi University,  Nanning 530004, China
  • Online:2018-11-01
  • Supported by:
    This work is supported by National Natural Science Foundation of China(No. 51777078,No. 51477055).

Abstract: Conventional methods and the famous machine learning algorithms for short-term load forecasting have two shortcomings: (i) forecasting accuracy is low;(ii) experiences for the configuration of model hyper-parameters are needed. To mitigate the influence of these shortcomings, deep forest algorithm is applied to short-term load forecasting in power system. Deep forest algorithm, which can do representation learning, includes two procedures: multi-grained scanning procedure and cascading forest procedure. Compared with deep neural network, deep forest algorithm can be trained efficiently in parallel with the default settings for the hyper-parameters of deep forest. The data of systemic actual load and meteorological information are utilized to training the model of deep forest for short-term load forecasting. Two models of the forecasting are built in this paper, i.e., models with the data of previous 21-day and previous 40-day. The forecasting performances of deep forest algorithm are compared with that of numerous intelligent algorithms and conventional classification algorithms, and the results show that deep forest algorithm can forecast the short-term load effectively.

Key words: deep forest algorithm, short-term load forecasting, multi-grained scanning, cascading forest, configuration of hyper-parameters

CLC Number: