Machine Learning-based Regional Cooling Demand Prediction with Optimised Dataset Partitioning

• URL: http://arxiv.org/abs/2503.05813v1
• Date: Tue, 04 Mar 2025 12:43:33 GMT
• Title: Machine Learning-based Regional Cooling Demand Prediction with Optimised Dataset Partitioning
• Authors: Meng Zhang, Zhihui Li, Zhibin Yu,
• Abstract summary: Accurately predicting cooling demand in urban domestic buildings is essential for maintaining energy efficiency.<n>This study introduces a generalised framework for developing high-resolution Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks.
• Score: 7.745583292171836
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the context of global warming, even relatively cooler countries like the UK are experiencing a rise in cooling demand, particularly in southern regions such as London. This growing demand, especially during the summer months, presents significant challenges for energy management systems. Accurately predicting cooling demand in urban domestic buildings is essential for maintaining energy efficiency. This study introduces a generalised framework for developing high-resolution Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks using physical model-based summer cooling demand data. To maximise the predictive capability and generalisation ability of the models under limited data scenarios, four distinct data partitioning strategies were implemented, including the extrapolation, month-based interpolation, global interpolation, and day-based interpolation. Bayesian Optimisation (BO) was then applied to fine-tune the hyper-parameters, substantially improving the framework predictive accuracy. Results show that the day-based interpolation GRU model demonstrated the best performance due to its ability to retain both the data randomness and the time sequence continuity characteristics. This optimal model achieves a Root Mean Squared Error (RMSE) of 2.22%, a Mean Absolute Error (MAE) of 0.87%, and a coefficient of determination (R square) of 0.9386 on the test set. The generalisation ability of this framework was further evaluated by forecasting.

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