Extreme learning machine for prediction of heat load in district heating systems

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Extreme learning machine for prediction of heat load in district heating systems

• Shahin Sajjadi^a,


• Shahaboddin Shamshirband^{b, ,} ,


• Meysam Alizamir^c,


• Por Lip Yee^b,


• Zulkefli Mansor^d,


• Azizah Abdul Manaf^e,


• Torki A. Altameem^e, ,


• Ali Mostafaeipour^f
  

• ^aDepartment of Construction Management, University of Houston, Houston, TX, USA
  
• ^bDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia


• ^cYoung Researchers and Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran


• ^dResearch Center for software technology and management (SOFTAM), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Malaysia


• ^eAdvanced Informatics School, Universiti Teknologi Malaysia, Malaysia


• ^fIndustrial Engineering Department, Yazd University, Yazd, Iran

Received 25 January 2016, Revised 23 March 2016, Accepted 9 April 2016, Available online 12 April 2016    

Energy and Buildings

Volume 122, 15 June 2016, Pages 222–227

 

 

Highlights •

District heating systems for increase in fuel efficiency.

•

Control and prediction future improvement of district heating systems operation.

•

To predict the heat load for individual consumers in district heating systems.

•

A process which simulates the head load conditions.

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Soft computing methodologies.

Abstract

District heating systems are important utility systems. If these systems are properly managed, they can ensure economic and environmental friendly provision of heat to connected customers. Potentials for further improvement of district heating systems’ operation lie in improvement of present control strategies. One of the options is introduction of model predictive control. Multistep ahead predictive models of consumers’ heat load are starting point for creating successful model predictive strategy. In this article, short-term, multistep ahead predictive models of heat load of consumer attached to district heating system were created. Models were developed using the novel method based on Extreme Learning Machine (ELM). Nine different ELM predictive models, for time horizon from 1 to 24 h ahead, were developed. Estimation and prediction results of ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in district heating systems. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms.

Keywords

• District heating systems;


• Heat load;


• Estimation;


• Prediction;


• Extreme Learning Machine (ELM)
  

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