用优化数据划分提升城市制冷需求预测精度，适合能源管理决策参考。
Machine Learning-based Regional Cooling Demand Prediction with Optimised Dataset Partitioning
- 采用四种数据划分策略结合贝叶斯优化调参，提升模型泛化能力。
- 最佳模型日间插值GRU在测试集上误差仅RMSE 2.22%，R²达0.9386。
- 适用于城市级建筑制冷负荷预测，尤其适合数据有限的气候区域。
在全球变暖背景下，即使是相对凉爽的英国也面临夏季制冷需求上升，尤以伦敦等南部地区为甚。准确预测城市住宅建筑的制冷需求对提升能效至关重要。本研究提出一种基于物理模型生成夏季制冷需求数据的通用框架，构建高分辨率长短期记忆（LSTM）与门控循环单元（GRU）网络。为在数据有限条件下最大化模型预测能力与泛化性能，设计了四种数据划分策略：外推法、月度插值、全局插值和日度插值。通过贝叶斯优化（BO）精细调整超参数，显著提升预测精度。结果表明，日度插值策略下的GRU模型表现最优，其在测试集上的均方根误差（RMSE）为2.22%，平均绝对误差（MAE）为0.87%，决定系数（R square）达到0.9386。框架的泛化能力经未来预测验证，表现出良好稳定性。
原文摘要 · Abstract (English)
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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