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This paper presents a selective order autoregressive model to forecast electricity demand. In the first stage, the autoregressive model is developed by critically selecting the number of lags from historical demand including to the forecasting model. At this stage, the change in the model performance is recorded in terms of the number of lags which result in optimum performance of the forecasting...
This paper presents a regression based moving window model for solving the short-term electricity forecasting problem. Moving window approach is employed to trace the demand pattern based on the past history of load and weather data. Regression equation is then formed and least square method is used to determine the parameters of the model. In this paper, a new concept associated with cooling and...
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