An Efficient PSO-Based Approach for Optimizing Internal Gain Schedules in Building Energy Simulation

¹ School of Mechanical Engineering, Tongji University, Shanghai, 201804, China
² GD Midea Heating & Ventilating Equipment Co.,Ltd., Foshan, 528311, China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

In the development of building energy models, accurately determining the temporal distribution of internal gains is crucial, as it directly influences both the daily and total load distribution within the building. However, the traditional method of manually adjusting parameters for each time segment in internal gain schedules is highly subjective. Furthermore, due to the large number of schedule parameters, the solution space is extremely vast during calibration, leading to a substantial and cumbersome manual workload that severely restricts the efficiency and accuracy of model construction. To effectively address these challenges, this paper proposes a novel method for rapidly determining building internal gain schedules based on the Particle Swarm Optimization (PSO) algorithm. First, we employ a single normal distribution to approximate and fit complex building internal gain schedules, thereby significantly reducing parameter dimensions. The fitted results are then input into IDF files for simulation. Building upon this, the PSO algorithm is utilized, with the R² value between simulated results and measured energy consumption data serving as the objective function, to search the parameter space and identify the internal gain schedule parameter combination corresponding to the optimal r² value. This method demonstrates excellent performance in test cases, with its r² value of the fitted results reaching up to 0.7. This research provides an efficient and reliable solution for the automated calibration of internal gain schedules in building energy models, and offers new insights for optimizing similar complex time-series parameters.