Neural network metamodelling in multi-objective optimization of a high latitude solar community

Janne Hirvonen*, Hassam Rehman, Kalyanmoy Deb, Kai Sirén

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Citations (Scopus)

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%.

Original languageEnglish
Pages (from-to)323-335
Number of pages13
JournalSolar Energy
Volume155
DOIs
Publication statusPublished - 1 Oct 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Neural network
  • Seasonal storage
  • Simulation-based optimization
  • Solar assisted heat pump
  • Solar community
  • Solar district heating

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