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

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@Article{AlShammari:2016:Energy,

• author = "Eiman Tamah Al-Shammari and Afram Keivani and Shahaboddin Shamshirband and Ali Mostafaeipour and Por Lip Yee and Dalibor Petkovic and Sudheer Ch",
• title = "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm",
• journal = "Energy",
• volume = "95",
• pages = "266--273",
• year = "2016",
• ISSN = "0360-5442",
• DOI = "doi:10.1016/j.energy.2015.11.079",
• URL = "http://www.sciencedirect.com/science/article/pii/S0360544215016424",
• 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 = "genetic algorithms, genetic programming, District heating systems, Heat load, Estimation, Prediction, Support Vector Machines, Firefly algorithm",

}

Genetic Programming entries for Eiman Tamah Al-Shammari Afram Keivani Shahaboddin Shamshirband Ali Mostafaeipour Por Lip Yee Dalibor Petkovic Sudheer Ch

Citations