Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency

Abstract

Due to rising demand for energy-efficient buildings, advanced predictive models are needed to evaluate heating and cooling load requirements. This research presents a unified strategy that blends LSTM networks and GBM to improve building energy load estimates’ precision and reliability. Data on energy usage, weather conditions, occupancy trends, and building features is collected and prepared to start the process. GBM model attributes are created using sequential relationships and initial load projections using LSTM networks. Combining LSTM with GBM takes advantage of each model's strengths: LSTM's sequential data processing and GBM's complex nonlinear connection capture. Performance measures like RMSE and MAE are used to evaluate the hybrid model's validity. Compared to individual models, the integrated LSTM-GBM method improves prediction accuracy. This higher predictive capacity allows real-time energy management systems, improving building operations and reducing energy use. Implementing this integrated model in Building Management Systems (BMS) shows its practicality in achieving sustainable building energy efficiency.