Cost-Optimal Building Energy System Scheduling Integrating Solar Irradiance Forecasting via LSTM-Attention-TCN Model

Abstract

Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration. Integrating electrical energy storage (EES) into these systems helps accommodate the increasing share of renewables; however, the stochastic and intermittent nature of solar power still poses challenges to supply reliability. This study proposes a photovoltaic (PV)-oriented storage scheduling strategy, in which short-term PV generation forecasts are applied to guide the operation of a building power supply network consisting of photovoltaic panels, the grid, and energy storage systems. The forecasting approach employs a hybrid framework combining a Long Short-Term Memory (LSTM) network to capture temporal dependencies, an attention mechanism to emphasise critical time steps, and a Temporal Convolutional Network (TCN) to map the enhanced features to PV outputs. Experimental evaluation using historical datasets under multiple weather conditions and time periods shows that the proposed LSTM-Attention-TCN model achieves a mean absolute error (MAE) of 20.45 W/m² and a Nash–Sutcliffe efficiency (NSE) of 0.94, outperforming both standalone LSTM and TCN models as well as their hybrid variants in terms of accuracy and robustness. By providing high-accuracy solar irradiance forecasts to guide energy storage operation and grid interaction, the proposed model enables more efficient and economical scheduling of building energy systems. Compared with an uncontrolled scenario, the LSTM-Attention-TCN-based scheduling reduces the total operating cost by approximately 52.1%, and achieves an additional 16.5% reduction compared to a conventional strategy without predictive coordination. In addition, compared to other hybrid forecasting models such as LSTM-TCN and TCN-Attention, the proposed model achieves the lowest total cost of CNY 14.83 and demonstrates superior scheduling efficiency, thereby enhancing the stability and flexibility of building energy utilization.

Keywords

cost optimization / deep learning / energy storage systems / optimal scheduling / solar irradiance forecasting

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant 62373266), the Qing Lan Project of Jiangsu Province, and the Open Foundation of the Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving (Grant IBES2025KF08).

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Research data are not shared.

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