Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm

Eiman Tamah Al-Shammari, Afram Keivani, Shahaboddin Shamshirband (), Ali Mostafaeipour, Por Lip Yee, Dalibor Petković and Sudheer Ch

Energy, 2016, vol. 95, issue C, 266-273

Abstract: District heating systems operation can be improved by control strategies. One of the options is the introduction of predictive control model. Predictive models of heat load can be applied to improve district heating system performances. In this article, short-term multistep-ahead predictive models of heat load for consumers connected to district heating system were developed using SVMs (Support Vector Machines) with FFA (Firefly Algorithm). Firefly algorithm was used to optimize SVM parameters. Seven SVM-FFA predictive models for different time horizons were developed. Obtained results of the SVM-FFA models were compared with GP (genetic programming), ANNs (artificial neural networks), and SVMs models with grid search algorithm. The experimental results show that the developed SVM-FFA models can be used with certainty for further work on formulating novel model predictive strategies in district heating systems.

Keywords: District heating systems; Heat load; Estimation; Prediction; Support Vector Machines; Firefly algorithm (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:95:y:2016:i:c:p:266-273

DOI: 10.1016/j.energy.2015.11.079

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