An ensemble-based approach for short-term load forecasting for buildings with high proportion of renewable energy sources

• Published in: Energy and buildings 2024-04, Vol.308, p.113996, Article 113996
• Main Authors: Pramanik, Abrar Shahriar, Sepasi, Saeed, Nguyen, Tung-Lam, Roose, Leon
• Format: Article
• Language: English
• Subjects: Buildings; Control; Ensemble-based method; Load forecasting; Neural network; Parallel-series load forecasting
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2024.113996

•Study proposes an ensemble STLF method merging parallel and series approaches for high-renewable energy loads.•Parallel method captures load trends; series uses deep learning for short-term variations.•Final forecast combines parallel/series methods with coefficients for accurate, robust modeling.•The method was evaluated using real-world load data and compared with two other methods, demonstrating its superior performance. The increasing integration of renewable energy sources into large buildings and structures has emphasized the importance of effective control systems to optimize resource use, grid stability, and reliability. In this context, load forecasting is critical for predicting future energy demand and managing the intermittency and variability of renewable energy resources to ensure a stable performance. This study proposes an ensemble-based short-term load forecasting (STLF) method tailored for buildings. It combines parallel and series approaches for a 24-hour forecasting horizon. The parallel approach uses similar historical days to capture the general load trend, holidays, and user behavior, while the series component employs a deep learning-based neural network to address short-term changes. The final ensemble-based forecast is the culmination of these two methods. Evaluated using real load profile data sets with 15-minute resolution, the proposed method demonstrates more than 23% and 24% improvements in MAE and RMSE, respectively, compared to other methods. These results demonstrated the superior performance of the proposed method in terms of both accuracy and robustness, making it an effective solution for short-term load forecasting in buildings heavily reliant on renewable energy.