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Short-term load forecasting of buildings based on artificial neural network and clustering technique

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In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings in Chonnam National University were used.
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Short-term load forecasting of buildings based on artificial neural network and clustering technique

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