Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm
| Autor: | Shahaboddin Shamshirband, Por Lip Yee, Dalibor Petković, Afram Keivani, Eiman Tamah Al-Shammari, Sudheer Ch, Ali Mostafaeipour |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
Mathematical optimization Artificial neural network business.industry 020209 energy Mechanical Engineering Genetic programming 02 engineering and technology Building and Construction Pollution Industrial and Manufacturing Engineering Support vector machine Model predictive control General Energy Heating system Search algorithm Hyperparameter optimization 0202 electrical engineering electronic engineering information engineering Firefly algorithm Electrical and Electronic Engineering business Civil and Structural Engineering |
| Zdroj: | Energy. 95:266-273 |
| ISSN: | 0360-5442 |
| DOI: | 10.1016/j.energy.2015.11.079 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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