Heating System Control with Neural Network

Abstract

Building performance has significant impact on humans’ life and ecology as buildings account for 39% of total greenhouse gas emissions and consume about 40% of total global energy consumption. Smart building control is one of the key points to archive high energy efficiency. Each year, the complexity of building state control grows due to the increase in the number of controlled elements that are used to achieve better indoor climate. Therefore, in the manual analysis and implementation of the building control program, an error can easily appear due to the human factor. Artificial intelligence (AI) algorithms could be used as an alternative solution as they could evaluate building dynamics independently. One of strategies for automatic building control adaptation to its dynamic is model based predictive control where neural network is used for different control strategies evaluation. Performance of such control technique is highly dependent on control strategies evaluation accuracy. To achieve top accuracy, several hyperparameters of neural network could be tuned as well as data set for specific construction must be prepared. Preparation of data set could be a problem as random control of building for generation of dataset could be not unacceptable for building users as well as it could damage construction.In this paper authors process optimization of experimental building heating system control algorithm to achieve smaller fluctuations of temperature indoors. For dataset generation were used several data from weather station as well as heating system parameters and temperature indoors while building was controlled by thermostat with build in PID regulation. For evaluation of building dynamics were used temporal convolutional neural network. To achieve high accuracy results of control strategies evaluation, several hyperparameters of neural network were tested. Finally resulting model were tested on physical building. Results indicate that in some cases developed control model could prevent temperature fluctuations which could be caused by limits of heating system power.