Abstract:

In order to improve the accuracy， generalization and robustness of short-term energy consumption prediction for public buildings， a short-term energy consumption prediction model for public buildings based on short-term memory network was proposed. The long-term and short-term memory network is used as the energy consumption feature extractor of public buildings to retain valuable historical energy consumption data in the process of continuous iteration， adjust the output of different time sequences through autonomous learning and self-organization， and introduce the gray system to reduce the number of sample data required and reduce errors. The output weight value is calculated by the minimum multiplication method to obtain the prediction value under the long-term and short-term memory network. The short-term prediction value of building energy consumption is obtained by accumulating the results after the inverse normalization function processing. The experimental results show that the proposed method has excellent energy consumption prediction ability and can be effectively used for public building energy consumption prediction.

Key words: short-term memory network, grey system, energy consumption of public buildings, forecast model, inverse normalization function, memory unit