@article{yang_optimal_2019,
	title = {Optimal design and performance analysis of solar hybrid {CCHP} system considering influence of building type and climate condition},
	volume = {174},
	issn = {0360-5442},
	url = {http://www.sciencedirect.com/science/article/pii/S0360544219304050},
	doi = {10.1016/j.energy.2019.03.001},
	abstract = {Incorporating solar energy technologies to the conventional combined cooling, heating and power (CCHP) system has been considered as an effective solution to mitigate the looming energy and environmental challenges. The mathematical model of a conventional CCHP system hybridized with photovoltaic (PV) panels and solar thermal collectors is built in this study. The particle swarm optimization (PSO) algorithm is employed to find the optimal size of key components of the solar hybrid CCHP system. The simulation work of solar hybrid CCHP systems in three building prototypes across seven climate zones is carried out to find appropriate design schemes of these cases. Besides, some guiding principles of the design of the solar hybrid CCHP system in early stage are summarized. The results show that the system under the following thermal load (FTL) strategy is the first choice in most cases of hospitals and hotels, except for the systems of hotels in the cold and very cold zones. On the contrary, the systems in offices perform better in the following electric load (FEL) mode in the majority of climate zones except for the hot-humid zone. Generally, the average optimal integrated performance (S) values of hospitals, hotels and offices can reach 28.95\%, 28.20\% and 22.69\%, respectively.},
	urldate = {2019-08-28},
	journal = {Energy},
	author = {Yang, G. and Zhai, X. Q.},
	month = may,
	year = {2019},
	keywords = {Building energy consumption, Climate condition, Combined cooling heating and power, Hybrid system, Particle swarm optimization algorithm, Solar energy},
	pages = {647--663},
}

{"_id":"NHLjnx5iCdu3q2s4b","bibbaseid":"yang-zhai-optimaldesignandperformanceanalysisofsolarhybridcchpsystemconsideringinfluenceofbuildingtypeandclimatecondition-2019","author_short":["Yang, G.","Zhai, X. Q."],"bibdata":{"bibtype":"article","type":"article","title":"Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition","volume":"174","issn":"0360-5442","url":"http://www.sciencedirect.com/science/article/pii/S0360544219304050","doi":"10.1016/j.energy.2019.03.001","abstract":"Incorporating solar energy technologies to the conventional combined cooling, heating and power (CCHP) system has been considered as an effective solution to mitigate the looming energy and environmental challenges. The mathematical model of a conventional CCHP system hybridized with photovoltaic (PV) panels and solar thermal collectors is built in this study. The particle swarm optimization (PSO) algorithm is employed to find the optimal size of key components of the solar hybrid CCHP system. The simulation work of solar hybrid CCHP systems in three building prototypes across seven climate zones is carried out to find appropriate design schemes of these cases. Besides, some guiding principles of the design of the solar hybrid CCHP system in early stage are summarized. The results show that the system under the following thermal load (FTL) strategy is the first choice in most cases of hospitals and hotels, except for the systems of hotels in the cold and very cold zones. On the contrary, the systems in offices perform better in the following electric load (FEL) mode in the majority of climate zones except for the hot-humid zone. Generally, the average optimal integrated performance (S) values of hospitals, hotels and offices can reach 28.95%, 28.20% and 22.69%, respectively.","urldate":"2019-08-28","journal":"Energy","author":[{"propositions":[],"lastnames":["Yang"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Zhai"],"firstnames":["X.","Q."],"suffixes":[]}],"month":"May","year":"2019","keywords":"Building energy consumption, Climate condition, Combined cooling heating and power, Hybrid system, Particle swarm optimization algorithm, Solar energy","pages":"647–663","bibtex":"@article{yang_optimal_2019,\n\ttitle = {Optimal design and performance analysis of solar hybrid {CCHP} system considering influence of building type and climate condition},\n\tvolume = {174},\n\tissn = {0360-5442},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0360544219304050},\n\tdoi = {10.1016/j.energy.2019.03.001},\n\tabstract = {Incorporating solar energy technologies to the conventional combined cooling, heating and power (CCHP) system has been considered as an effective solution to mitigate the looming energy and environmental challenges. The mathematical model of a conventional CCHP system hybridized with photovoltaic (PV) panels and solar thermal collectors is built in this study. The particle swarm optimization (PSO) algorithm is employed to find the optimal size of key components of the solar hybrid CCHP system. The simulation work of solar hybrid CCHP systems in three building prototypes across seven climate zones is carried out to find appropriate design schemes of these cases. Besides, some guiding principles of the design of the solar hybrid CCHP system in early stage are summarized. The results show that the system under the following thermal load (FTL) strategy is the first choice in most cases of hospitals and hotels, except for the systems of hotels in the cold and very cold zones. On the contrary, the systems in offices perform better in the following electric load (FEL) mode in the majority of climate zones except for the hot-humid zone. Generally, the average optimal integrated performance (S) values of hospitals, hotels and offices can reach 28.95\\%, 28.20\\% and 22.69\\%, respectively.},\n\turldate = {2019-08-28},\n\tjournal = {Energy},\n\tauthor = {Yang, G. and Zhai, X. Q.},\n\tmonth = may,\n\tyear = {2019},\n\tkeywords = {Building energy consumption, Climate condition, Combined cooling heating and power, Hybrid system, Particle swarm optimization algorithm, Solar energy},\n\tpages = {647--663},\n}\n\n","author_short":["Yang, G.","Zhai, X. 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