생산성을 고려한 건물에너지와 열쾌적 최적화 알고리즘 개발

초록

This paper suggests optimization algorithm through 4 steps between building energy and thermal comfort. Step 1, create models of building energy consumption components and PMV, PPD as thermal comfort index in cooling season. Step 2, make optimal points that cannot be selected as only one point through Pareto optimal using genetic algorithm. Step 3, use productivity index to generate weight factors that help to select one optimal point. Step 4, solve the one optimal point using Weighted Sum Method (WSM) considering building energy, thermal comfort and productivity. Result of case study applied algorithm shows that improving thermal comfort is important than saving energy cost. This indicates benefits from increased productivity by improving thermal comfort are bigger than cost saving from reducing energy consumption.

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