Neural network metamodelling in multi-objective optimization of a high latitude solar community
Research output: Contribution to journal › Article › Scientific › peer-review
- Michigan State University
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%.
|Number of pages||13|
|Publication status||Published - 1 Oct 2017|
|MoE publication type||A1 Journal article-refereed|
- Neural network, Seasonal storage, Simulation-based optimization, Solar assisted heat pump, Solar community, Solar district heating