Short Term Prediction of Colleges Building Itemized Energy Consumption Based on Long Short-term Memory Neural Network

Abstract

With the rapid development of Chinese colleges, the proportion of building energy consumption in total building energy consumption is rapidly expanding. The prediction of building energy consumption in colleges has become an increasingly important research topic in recent years. However, the most current building energy consumption forecasting models focus on the prediction research of the total energy consumption, but fewer about the perceptions of itemized building energy consumption. To solve this problem, an itemized energy consumption short term prediction model is given in this paper, which is based on long short-term memory (LSTM) neural networks. The input data include total history building energy consumption, local weather characteristics, date characteristics, and time characteristics. According to the results of experiments, an accurate prediction of lighting energy consumption and special energy consumption can be given by this model, which accounts for 96.51% of the total building energy. Moreover, the LSTM model can also achieve smaller computational complexity with little difference in prediction accuracy compared with other combined models.