Accurate building load forecasting plays a critical role in facilitating demand response aggregation and optimizing energy management. However, the complex temporal dependencies and high volatility of building loads limit the improvement of prediction accuracy. To this end, we propose a novel end-to-end building load forecasting framework. Specifically, the framework can be divided into two main stages. In the two-stage data preprocessing module enhanced by interpretable feature selection, we utilize the Local Outlier Factor (LOF) algorithm to accurately detect and correct anomalies in the original building load series. Furthermore, we employ SVM-SHAP feature analysis to quantify the impact of environmental variables, filtering out critical feature combinations to mitigate redundancy. In the building load forecasting module, we propose the patch-based information fusion network (PIF-Net). This model applies patching technology to process input series into local blocks, extracting temporal features through a shared Gated Recurrent Unit (GRU) network with residual connections. Subsequently, an information fusion module based on a customized gating mechanism integrates the ensemble hidden states to weight the importance of different temporal patches dynamically. Additionally, the framework is trained using a novel Error-weighted Adaptive Loss (EWAL) function. By combining a rational quadratic function and logarithmic loss to dynamically adjust penalty weights based on real-time prediction error distributions, EWAL significantly enhances the model's robustness under extreme load conditions. Finally, extensive experiments demonstrate the effectiveness and superiority of our proposed framework.

翻译：精确的建筑负荷预测在促进需求响应聚合和优化能源管理中起着关键作用。然而，建筑负荷的复杂时间依赖性和高波动性限制了预测精度的提升。为此，我们提出了一种新颖的端到端建筑负荷预测框架。具体而言，该框架可分为两个主要阶段。在基于可解释特征选择增强的两阶段数据预处理模块中，我们利用局部异常因子（LOF）算法准确检测并修正原始建筑负荷序列中的异常值。此外，我们采用SVM-SHAP特征分析量化环境变量的影响，筛选出关键特征组合以减少冗余。在建筑负荷预测模块中，我们提出了基于补丁的信息融合网络（PIF-Net）。该模型应用补丁技术将输入序列处理为局部块，通过带有残差连接的共享门控循环单元（GRU）网络提取时间特征。随后，基于定制化门控机制的信息融合模块集成整体隐藏状态，动态加权不同时间补丁的重要性。此外，框架使用一种新颖的误差加权自适应损失（EWAL）函数进行训练。通过结合有理二次函数和对数损失，根据实时预测误差分布动态调整惩罚权重，EWAL显著增强了模型在极端负荷条件下的鲁棒性。最后，大量实验证明了所提出框架的有效性和优越性。