Enhancing Zero-Energy Building Operations for ESG: Accurate Solar Power Prediction through Automatic Machine Learning

Abstract

Solar power systems, such as photovoltaic (PV) systems, have become a necessary feature of zero-energy buildings because efficient building design and construction materials alone are not sufficient to meet the building’s energy consumption needs. However, solar power generation is subject to fluctuations based on weather conditions, and these fluctuations are higher than other renewable energy sources. This phenomenon has emphasized the importance of predicting solar power generation through weather forecasting. In this paper, an Automatic Machine Learning (AML)-based method is proposed to create multiple prediction models based on solar power generation and weather data. Then, the best model to predict daily solar power generation is selected from these models. The solar power generation data used in this study was obtained from an actual solar system installed in a zero-energy building, while the weather data was obtained from open data provided by the Korea Meteorological Administration. In addition, To verify the validity of the proposed method, an ideal data model with high accuracy but difficult to apply to the actual system and a comparison model with a relatively low accuracy but suitable for application to the actual system were created. The performance was compared with the model created by the proposed method. Based on the validation process, the proposed approach shows 5–10% higher prediction accuracies compared to the comparison model.

Keywords

• zero-energy building
• carbone neutral
• sustainable solar power system
• prediction-based ESG operational policy
• automatic machine learning
• EV charging platform