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- W2982437657 abstract "Short-term load prediction is important for demand side management at a distribution system. Accurate Load prediction for the distribution system operation is challenging due to the complicated load characteristics and numerous influence factors. In this paper a new hybrid load predicting strategy with input selection is introduced to improve the forecasting performance. The hybrid forecasting method contains a seasonal autoregressive integrated moving average function and a neural network (SARIMA-NN). The input selection aims to find the history days which has similar load curve as the forecasted day based on a heat index criteria (HI-SD), and the input variables are chosen by the factor affecting degree on different forecasted days. This novel strategy captures distinct load characteristics at different climate level, considering the effectively similarities between load days, choosing accurately input variables, and shows a great forecasting performance as demonstrated by a practical substation datasets." @default.
- W2982437657 created "2019-11-08" @default.
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- W2982437657 date "2019-01-01" @default.
- W2982437657 modified "2023-09-25" @default.
- W2982437657 title "A Hybrid Short-Term Load Prediction with Input Selection for the Distribution System" @default.
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- W2982437657 doi "https://doi.org/10.1109/iceict.2019.8846382" @default.
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