A novel CALA-STL algorithm for optimizing prediction of building energy heat load

Guo, Yan; Jia, Mengjing; Su, Chang; Darkwa, Jo; Hou, Songsong; pan, Fei; Wang, Hui; Liu, Ping

Abstract

Energy heat load forecasting plays a crucial role in the low-energy management of buildings. With the growing demand for energy and increasing environmental pressures, accurately predicting building heat loads can provide reliable data support for energy management. This enables the optimization of energy dispatch plans, improves energy utilization efficiency, and helps achieve the goals of energy conservation and emission reduction. However, traditional forecasting methods often struggle with low accuracy when dealing with complex external factors and are susceptible to inappropriate hyperparameter selection. To address these challenges, this study proposes an innovative energy heat load forecasting algorithm that enhances the Long Short-Term Memory (LSTM) model using an improved Artificial Rabbit Optimization (ARO) technique to boost both prediction accuracy and efficiency. First, the Seasonal and Trend decomposition using Loess (STL) algorithm is employed to decompose energy heat load data into trend, seasonal, and residual components, reducing the impact of data fluctuations on model prediction. Next, the ARO algorithm is improved with a Cauchy Mutation and Adaptive Crossover Strategy (CMACS) to optimize the hyperparameters of the LSTM model. To validate the effectiveness of the proposed model, experiments were conducted using real-world data from Byron Apartments at Nottingham Trent University, UK. Due to the unique living patterns of student apartments, energy consumption in these buildings exhibits significant fluctuations and complexity, making the data highly representative. Experimental results show that the proposed CMACS-ARO-LSTM-Attention (CALA)-STL method achieves a coefficient of determination of 98.30%, significantly outperforming traditional methods. This method provides an efficient and reliable solution for energy heat load forecasting, offering robust data support for the optimized management of building energy systems. It enables precise energy management, thereby reducing energy waste and operational costs.

Citation

Guo, Y., Jia, M., Su, C., Darkwa, J., Hou, S., pan, F., Wang, H., & Liu, P. (2025). A novel CALA-STL algorithm for optimizing prediction of building energy heat load. Energy and Buildings, 328, Article 115207. https://doi.org/10.1016/j.enbuild.2024.115207

• Journal Article Type: Article
• Acceptance Date: Dec 14, 2024
• Online Publication Date: Dec 16, 2024
• Publication Date: Feb 1, 2025
• Deposit Date: Jan 2, 2025
• Publicly Available Date: Dec 17, 2025
• Journal: Energy and Buildings
• Print ISSN: 0378-7788
• Electronic ISSN: 1872-6178
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 328
• Article Number: 115207
• DOI: https://doi.org/10.1016/j.enbuild.2024.115207
• Keywords: Heat load prediction, Energy consumption in buildings, Artificial rabbit optimization algorithm, Long and short-term memory networks
• Public URL: https://nottingham-repository.worktribe.com/output/43681699
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0378778824013239?via%3Dihub