Application of аrtificial neural networks for prediction of heating energy consumption in university buildings

Abstract

In this study, the main objective is to predict heating consumption using artificial neural networks with several input parameters. For training and testing, daily meteorological and heating consumption data for Norwegian University of Science and Technology - NTNU University campus Gløshaugen were used. In order to determine the optimal network architecture, various network architectures were designed and different training algorithms were used. Also, the number of neurons and hidden layers and activation functions in the hidden layer/output layer were changed. Training of the network was performed by using Levenberg–Marquardt feedforward backpropagation algorithms. For each network, different indices of the prediction accuracy were calculated and compared.