Multi-Objective Optimization of Illuminance, Heating, and Cooling Setpoints in Office Buildings Using a Fuzzy-Based Approach

This article introduces a novel optimization approach grounded in fuzzy logic, which transforms the multi-objective optimization problem into a single-objective one. Instead of providing a Pareto front, this method delivers a final optimal point based on predefined design priorities. The proposed methodology is applied to optimize illuminance, heating, and cooling setpoints in an office building across six cities with diverse climates to assess its performance under various conditions. The multi-objective optimization of these setpoints represents a novel contribution to smart building design. Compared to the NSGA-II method, the newly introduced approach exhibits simplicity and achieves a 50% reduction in computational time. The method leverages user experiences in formulating fuzzy rules, yielding more optimal solutions compared to the NSGA-II. The proposed method combines neural network, fuzzy logic, and genetic algorithm to create an efficient and intelligent framework for fast and accurate multi-objective optimization in energy-related problems.