Generation of sub-item load profiles for public buildings based on the conditional generative adversarial network and moving average method

Abstract

Increasing research on data-driven methods to optimize energy systems and power grid operation requires a large amount of data with regard to building energy consumption profiles; owing to the difficulty in availing load data, time-consuming collection and the privacy issues in the collection process have become limitations and previous research on total load generation cannot meet research requirements of refined energy control and optimization. In this study, we propose a novel approach based on the conditional generative adversarial network (CGAN) and moving average method to generate sub-item load profiles of building energy consumption to solve the aforementioned problems. Sub-item load profiles include light and socket load, HVAC (Heating Ventilation and Air Conditioning) load, impetus load and special load. The CGAN algorithm is employed to generate sub-item load profiles considering specific conditions (multiple labels) e.g. time, weather, and load shape labels. In addition, the moving average method was used to reduce noise in the generated profiles. The case study was conducted based on real-world sub-item load data collected from office buildings, commercial buildings, and hospitals in Shenzhen, China. We validated the generation performance of the sub-item load profile of CGAN-MA by comparing it with the traditional load profile generation method GAN and variational autoencoder based on three aspects: similarity, variability and diversity. Compared with the traditional model, the proposed model improves the similarity and variability by about 5.7% to 64.8%, 76.7% to 135.5% respectively, and can satisfy the requirements of diversity with the diversity indicator of four sub item generated load is 1.36, 1.93, 1.81 and 2.08 respectively. Furthermore, we compared the generated load and real load possibility distributions under the selected conditions. The results show that the load generated by CGAN-MA is higher on working days, rainy days and hot days than on non-working days, sunny days and cool days, which correspond to the real circumstances, and sub-item B (HVAC) is the most sensitive one to different conditions. The proposed model can be applied to effectively generate sub-load profiles under the required conditions and further help in studies related to the development of data-driven methods for energy consumption prediction, demand-side management and the optimization of power grid operation.