Prediction of HVAC Energy Consumption Using PSO Optimized Deep Neural Network

Heating, Ventilation and Air Conditioning (HVAC) system is a highly nonlinear system with a large amount of complex, coupled inputs. This paper presents a novel prediction method for HVAC energy consumption based on deep neural network (DNN). In order to solve the problem that traditional neural networks tend to fall into local optima, batch normalization and Adam optimization algorithm are significantly incorporated in the DNN. In addition, particle swarm optimization (PSO) is utilized to search for the optimal number of hidden layer nodes and increase the accuracy of prediction model. The cooling tower data of HVAC is used to validate the network. The results show the mean absolute error and the mean square error of the PSO-DNN model, from which it can be seen obviously that our proposed prediction model performs better than traditional ones. Accordingly, DNN optimized by PSO algorithm is of great validity and superiorities for energy consumption prediction of HVAC system.