Long‐term load forecasting using improved recurrent neural network

Yasuhiro Hayashi*, Shinichi Iwamoto

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


In general, electric power companies must prepare power supply capability for maximum electric load demand because it is very difficult at present to store electric power. It takes several years and requires a great amount of money to construct power generation and transmission facilities. Therefore, it is necessary to forecast long‐term load demand exactly in order to plan or operate power systems efficiently. Several methods have been investigated so far for the long‐term load forecasting. However, because the electric loads consist of many complex factors, good forecasting has been very difficult. This paper proposes a long‐term load forecasting method using a recurrent neural network (RNN). This is a mutually connected network that has the ability of learning patterns and past records. In general, when interpolation is used for unlearned data sets, the neural network provides reasonably good outputs. However, when extrapolation is used, such as in long‐term load forecasting, some kind of tunings have been necessary to obtain good results. Therefore, to solve the problem, a method is proposed in which growth rates are used as input and output data. Using the proposed method, successful results have been obtained and comparisons have been made with the conventional methods.

Original languageEnglish
Pages (from-to)41-54
Number of pages14
JournalElectrical Engineering in Japan
Issue number8
Publication statusPublished - 1994


  • Long‐term load forecasting
  • growth rate
  • modification multiplier
  • recurrent neural network

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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