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• W2783136831 abstract "Distributed electric heating have become the main way to surrounding area user in winter, so it is very important to forecast short-term electric heating distribution load quickly and accurately. A novel method is proposed, in which the Back Propagation (BP) neural network algorithm is intelligently combined with ensemble empirical mode decomposition (EEMD), and it can deal with different weather type and temperature change on the residents' heating behavior. The daily load sequence is decomposed into four series of low-to-high frequency sub-sequences and a remnant sub-sequence by EEMD method. And then, each sub-sequence, temperature data and meteorological data are input into the BP neural network to predict. Finally, the predicted components are sum up to obtain the final prediction result. EEMD-BP is applied in Coal-to-Electricity Project in beijing and the distribution network load with a large proportion of electric heating is forecast. The simulation results show that EEMD-BP method can effectively reduce the prediction error." @default.
• W2783136831 created "2018-01-26" @default.
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• W2783136831 date "2017-11-01" @default.
• W2783136831 modified "2023-09-25" @default.
• W2783136831 title "A Novel combined forecasting method for short-term distributed electric heating power load" @default.
• W2783136831 doi "https://doi.org/10.1109/ei2.2017.8245378" @default.
• W2783136831 hasPublicationYear "2017" @default.
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