Air Conditioning Load Prediction of an Office Building Based on Long Short Term Memory Neural Network

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy but also extend the life of the equipment. Introduction: The neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which helps improving the prediction accuracy. Combined with the grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short-Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with the Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has a better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time, and great application potential. © 2022 Bentham Science Publishers.