Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm

District heating systems are important utility systems. If these systems are properly managed, they can ensure economic and environmentally friendly provision of heat to connected customers. Potentials for further improvement of district heating systems' operation lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multi-step ahead predictive models of consumers' heat load are a starting point for creating a successful model predictive strategy. For the purpose of this article, short-term multi-step ahead predictive models of heat load of consumers connected to a district heating system were created. The models were developed using the novel method based on SVM (Support Vector Machines) coupled with a discrete wavelet transform. Nine different SVM-WAVELET predictive models for a time horizon from 1 to 24 h ahead were developed. Estimation and prediction results of the SVM-WAVELET models were compared with GP (genetic programming) and ANN (artificial neural network) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-WAVELET approach in comparison with GP and ANN. (C) 2015 Elsevier Ltd. All rights reserved.