Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm. (15th January 2016)

• Record Type: Journal Article
• Authors: Al-Shammari, Eiman Tamah; Keivani, Afram; Shamshirband, Shahaboddin; Mostafaeipour, Ali; Yee, Por Lip; Petković, Dalibor; Ch, Sudheer
• Is Part Of: Energy. Volume 95(2016)
• Page Start: 266
• Page End: 273
• Publication Date: 2016-01-15
• Subjects: District heating systems -- Heat load -- Estimation -- Prediction -- Support Vector Machines -- Firefly algorithm; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.11.079
• Languages: English
• ISSNs: 0360-5442

Abstract

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. 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. Soft computing methodologies.