Multi-objective optimization of building energy performance using a particle swarm optimizer with less control parameters

Abstract

Good energy performance is very important for decreasing the consumption of energy-intensive buildings. This paper studies a new multi-objective evolutionary approach to optimize the building energy performance. Firstly, the problem is modeled as two-objective optimization functions, i.e., minimizing the energy consumption and maximizing the comfort level. Next, a new solving algorithm based on bare-bones particle swarm optimization is developed. In the algorithm, an improved update strategy with adaptive perturbation is proposed to overcome the disadvantage of easily local convergence of the traditional algorithm. The algorithm deletes control parameters including inertia weight and acceleration coefficients from the traditional algorithm, showing the advantage of easy-to-use. Applying the proposed algorithm into several buildings located in China, experiment result shows that the proposed algorithm can obtain good non-dominated solutions, which is obviously better than that of those compared algorithms. The uncomfortable hours of the proposed algorithm on part cases reduce 11.82%, but its energy consumption only increases 1.74%. All indicate that the proposed algorithm is a powerful tool for optimizing building energy preference.