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• W2629126622 abstract "A solar community of 100 residential houses was optimized for Finnish conditions with the aim of achieving a 90% solar fraction for both space heating and domestic hot water. Optimization was done using a novel method based on neural network metamodelling and compared to the standard NSGA-II genetic algorithm. Compared to NSGA-II, the new method obtained a larger hypervolume by finding better solutions both in the center and edge of the non-dominated front. The combined non-dominated front of both methods was better than either one separately. The performance target was achieved as the optimal solar community designs had heating solar fractions ranging from 64% to 95%." @default.
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• W2629126622 date "2017-10-01" @default.
• W2629126622 modified "2023-10-18" @default.
• W2629126622 title "Neural network metamodelling in multi-objective optimization of a high latitude solar community" @default.
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• W2629126622 doi "https://doi.org/10.1016/j.solener.2017.06.040" @default.
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