気象情報の不確かさを考慮した週間電力負荷予測

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タイトル別名
  • One-day-thorough Seven-day-ahead Electrical Load Forecasting in Consideration of Uncertainties of Weather Information
  • キショウ ジョウホウ ノ フタシカサ オ コウリョシタ シュウカン デンリョク

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We propose an approach to one-day-thorough seven-day-ahead electrical load forecasting based on a realistic problem formulation which should contribute to more reliable and economic weekly power station operation. Generally, the load forecasting has the following problems: (1) Although the load is affected by various factors, like temperatures, in the load forecasting, it is impossible to consider all of them; (2) The relationships between the load and some factors are not clear, and often vary with time; (3) Uncertainties in forecasts of the temperatures sometimes make the results of load forecasting worse. They are very influential to the power station operation. While a number of methods have been proposed to solve the problems (1) and (2), there have been few attempts on the problem (3). We propose the following approach in this paper, taking these problems into consideration. Firstly, concerning the problem (1), we focus on such factors that have major influence on the load and whose values are obtainable on a weekly basis. The other factors are all regarded as stochastic and are not included in the forecasting model. Secondly, regarding the problem (2), we use a self-organizing approach where the algorithm itself finds the optimal model structure or the optimal set of factors to be included in the model day by day. And finally, addressing the problem (3), we propose a new performance index of model structures which can measure the balance between i) improvement of the load forecasting accuracy due to inclusion of a factor in the model and, ii) degradation caused by uncertainty or error in the factor included. Using this index, we construct a model which does not yield a large error in spite of errors in the temperature forecasts. Examples show that this approach improves the forecasted results when erroneous temperature forecasts are fed into the model, and verify its effectiveness.

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