予測値変化の最小化を考慮したエネルギー需要量予測法(機械力学,計測,自動制御)

概要

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A method of predicting energy demands in consideration of minimizing variations in their predicted values is proposed to rationally determine operational strategies of energy supply systems. The autoregressive integrated moving average model is adopted to express time series data on energy demands. Values of model parameters are identified by minimizing not only the errors between measured and predicted energy demands but also the variations in predicted ones by a global optimization method for nonlinear programming. This method is applied to the prediction of the cooling demand in a building used for a bench mark test of a variety of prediction methods. The results obtained by the proposed method are compared with those by a conventional method by which only the errors are minimized. It turns out that the proposed method can predict energy demands with the variations reduced substantially at the sacrifice of small increases in the errors.
一般社団法人日本機械学会の論文
2004-09-25