Optimization of solar systems using artificial neural-networks and genetic algorithms

Optimization of solar systems using artificial neural-networks and genetic algorithms. Kalogirou, S. A. Applied Energy, 77(4):383--405, April, 2004.
abstract bibtex

The objective of this work is to use artificial intelligence methods, like artificial neural-networks and genetic algorithms, to optimize a solar-energy system in order to maximize its economic benefits. The system is modeled using a TRNSYS computer program and the climatic conditions of Cyprus, included in a typical meteorological year (TMY) file. An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of these two parameters, for maximizing life-cycle savings: thus the design time is reduced substantially. As an example, the optimization of an industrial process heat-system employing flat-plate collectors is presented. The optimum solutions obtained from the present methodology give increased life-cycle savings of 4.9 and 3.1% when subsidized and non-subsidized fuel prices are used respectively, as compared to solutions obtained by the traditional trial-and-error method. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs or by trial-and-error, which in most cases depends on the intuition of the engineer.

@article{ Kalogirou2004,
  abstract = {The objective of this work is to use artificial intelligence methods, like artificial neural-networks and genetic algorithms, to optimize a solar-energy system in order to maximize its economic benefits. The system is modeled using a TRNSYS computer program and the climatic conditions of Cyprus, included in a typical meteorological year (TMY) file. An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of these two parameters, for maximizing life-cycle savings: thus the design time is reduced substantially. As an example, the optimization of an industrial process heat-system employing flat-plate collectors is presented. The optimum solutions obtained from the present methodology give increased life-cycle savings of 4.9 and 3.1% when subsidized and non-subsidized fuel prices are used respectively, as compared to solutions obtained by the traditional trial-and-error method. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs or by trial-and-error, which in most cases depends on the intuition of the engineer.},
  author = {Kalogirou, Soteris A.},
  issn = {0306-2619},
  journal = {Applied Energy},
  month = {April},
  number = {4},
  pages = {383--405},
  title = {{Optimization of solar systems using artificial neural-networks and genetic algorithms}},
  volume = {77},
  year = {2004}
}

Downloads: 0

{"_id":{"_str":"524b09c4c0bcb22b6d000606"},"__v":0,"authorIDs":[],"author_short":["Kalogirou, S.<nbsp>A."],"bibbaseid":"kalogirou-optimizationofsolarsystemsusingartificialneuralnetworksandgeneticalgorithms-2004","bibdata":{"html":"<div class=\"bibbase_paper\"> \n\n\n<span class=\"bibbase_paper_titleauthoryear\">\n\t<span class=\"bibbase_paper_title\"><a name=\"Kalogirou2004\"> </a>Optimization of solar systems using artificial neural-networks and genetic algorithms.</span>\n\t<span class=\"bibbase_paper_author\">\nKalogirou, S. A.</span>\n\t\n</span>\n\n\n\n<i>Applied Energy</i>,\n\n77(4):383--405.\n\nApril 2004.\n\n\n\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content\">\n \n \n \n <a href=\"javascript:showBib('Kalogirou2004')\"\n class=\"bibbase link\">\n \n\t\n\t\n\t\n BibTeX\n <i class=\"fa fa-caret-down\"></i></a>\n \n \n  \n <a class=\"bibbase_abstract_link bibbase link\"\n href=\"javascript:showAbstract('Kalogirou2004')\">\n Abstract\n <i class=\"fa fa-caret-down\"></i></a>\n \n \n \n\n \n \n \n</span>\n\n<div class=\"well well-small bibbase\" id=\"bib_Kalogirou2004\"\n style=\"display:none\">\n <pre>@article{ Kalogirou2004,\n abstract = {The objective of this work is to use artificial intelligence methods, like artificial neural-networks and genetic algorithms, to optimize a solar-energy system in order to maximize its economic benefits. The system is modeled using a TRNSYS computer program and the climatic conditions of Cyprus, included in a typical meteorological year (TMY) file. An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of these two parameters, for maximizing life-cycle savings: thus the design time is reduced substantially. As an example, the optimization of an industrial process heat-system employing flat-plate collectors is presented. The optimum solutions obtained from the present methodology give increased life-cycle savings of 4.9 and 3.1% when subsidized and non-subsidized fuel prices are used respectively, as compared to solutions obtained by the traditional trial-and-error method. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs or by trial-and-error, which in most cases depends on the intuition of the engineer.},\n author = {Kalogirou, Soteris A.},\n issn = {0306-2619},\n journal = {Applied Energy},\n month = {April},\n number = {4},\n pages = {383--405},\n title = {{Optimization of solar systems using artificial neural-networks and genetic algorithms}},\n volume = {77},\n year = {2004}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" id=\"abstract_Kalogirou2004\"\n style=\"display:none\">\n The objective of this work is to use artificial intelligence methods, like artificial neural-networks and genetic algorithms, to optimize a solar-energy system in order to maximize its economic benefits. The system is modeled using a TRNSYS computer program and the climatic conditions of Cyprus, included in a typical meteorological year (TMY) file. An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of these two parameters, for maximizing life-cycle savings: thus the design time is reduced substantially. As an example, the optimization of an industrial process heat-system employing flat-plate collectors is presented. The optimum solutions obtained from the present methodology give increased life-cycle savings of 4.9 and 3.1% when subsidized and non-subsidized fuel prices are used respectively, as compared to solutions obtained by the traditional trial-and-error method. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs or by trial-and-error, which in most cases depends on the intuition of the engineer.\n</div>\n\n\n</div>\n","downloads":0,"urls":{},"abstract":"The objective of this work is to use artificial intelligence methods, like artificial neural-networks and genetic algorithms, to optimize a solar-energy system in order to maximize its economic benefits. The system is modeled using a TRNSYS computer program and the climatic conditions of Cyprus, included in a typical meteorological year (TMY) file. An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of these two parameters, for maximizing life-cycle savings: thus the design time is reduced substantially. As an example, the optimization of an industrial process heat-system employing flat-plate collectors is presented. The optimum solutions obtained from the present methodology give increased life-cycle savings of 4.9 and 3.1% when subsidized and non-subsidized fuel prices are used respectively, as compared to solutions obtained by the traditional trial-and-error method. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs or by trial-and-error, which in most cases depends on the intuition of the engineer.","author":["Kalogirou, Soteris A."],"author_short":["Kalogirou, S.<nbsp>A."],"bibtex":"@article{ Kalogirou2004,\n abstract = {The objective of this work is to use artificial intelligence methods, like artificial neural-networks and genetic algorithms, to optimize a solar-energy system in order to maximize its economic benefits. The system is modeled using a TRNSYS computer program and the climatic conditions of Cyprus, included in a typical meteorological year (TMY) file. An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of these two parameters, for maximizing life-cycle savings: thus the design time is reduced substantially. As an example, the optimization of an industrial process heat-system employing flat-plate collectors is presented. The optimum solutions obtained from the present methodology give increased life-cycle savings of 4.9 and 3.1% when subsidized and non-subsidized fuel prices are used respectively, as compared to solutions obtained by the traditional trial-and-error method. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs or by trial-and-error, which in most cases depends on the intuition of the engineer.},\n author = {Kalogirou, Soteris A.},\n issn = {0306-2619},\n journal = {Applied Energy},\n month = {April},\n number = {4},\n pages = {383--405},\n title = {{Optimization of solar systems using artificial neural-networks and genetic algorithms}},\n volume = {77},\n year = {2004}\n}","bibtype":"article","id":"Kalogirou2004","issn":"0306-2619","journal":"Applied Energy","key":"Kalogirou2004","month":"April","number":"4","pages":"383--405","title":"Optimization of solar systems using artificial neural-networks and genetic algorithms","type":"article","volume":"77","year":"2004","role":"author","bibbaseid":"kalogirou-optimizationofsolarsystemsusingartificialneuralnetworksandgeneticalgorithms-2004"},"bibtype":"article","biburl":"http://www.eeci.cam.ac.uk/publications/references.bib","downloads":0,"search_terms":["optimization","solar","systems","using","artificial","neural","networks","genetic","algorithms","kalogirou"],"title":"Optimization of solar systems using artificial neural-networks and genetic algorithms","year":2004,"dataSources":["HiecPnf5mmbLNkHM8"]}