Short-Term Prediction for Indoor Temperature Control Using Artificial Neural Network

Abstract

Recently, data-based artificial intelligence technology has been developing dramatically, and we are considering how to model, predict, and control complex systems. Energy system modeling and control have been developed in conjunction with building technology. This study investigates the use of an artificial neural network (ANN) for predicting indoor air temperature in a test room with windows on an entire side. Multilayer perceptron (MLP) models were constructed and trained using time series data obtained at one-second intervals. Several subsampling time steps of 1 s, 60 s, 300 s, 600 s, 900 s, 1800 s, and 3600 s were performed by considering the actual operation control mode in which the time interval is important. The performance indices of the neural networks were evaluated using various error metrics. Successful results were obtained and analyzed based on them. It was found that as the multi-step time interval increases, performance degrades. For system control designs, a shorter prediction horizon is suggested due to the increase in computational time, for instance, the limited computing capacity in a microcontroller. The MLP structure proved useful for short-term prediction of indoor air temperature, particularly when control horizons are set below 100. Furthermore, highly reliable results were obtained at multi-step time intervals of 300 s or less. For the multivariate model, both calculation time and data dispersion increased, resulting in worsened performance compared to the univariate model.