Accurately predicting building energy consumption is essential for enhancing energy utilization efficiency in buildings. However, the inherent volatility and noise in building energy data, caused by diverse user behaviors and potential sensor errors, make significant challenges to energy consumption prediction. To address these issues, a dual-optimization framework (IDBO-VMD-IDBO-BiLSTM) for building energy consumption prediction, which incorporates improved dung beetle optimization algorithm (IDBO), variational mode decomposition (VMD), and bidirectional long short-term memory network (BiLSTM), was proposed. In this framework, IDBO firstly optimizes the VMD by adaptively determining its optimal parameters to decompose the original building energy consumption series into multiple intrinsic modal functions (IMFs) with smoother characteristics, thereby the effect of mitigating data noise. Then, each IMF component is predicted using the BiLSTM model, with IDBO selecting the optimal hyperparameters for BiLSTM. Finally, the individual predictions of each IMF are superimposed and reconstructed to yield the final predictions. To verify the framework’s effectiveness, real energy consumption data from an office building in Shanghai was collected and analyzed in a comprehensive comparison with seven other comparative models. Experimental results suggested that the proposed framework outperformed the comparative models in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2), showing both high predictive accuracy and strong robustness. Therefore, the proposed framework can be an effective tool for predicting building energy consumption.

中文翻译：

---

基于改进蜣螂算法、变分模态分解和深度学习的双重优化建筑能量预测框架


准确预测建筑物能耗对于提高建筑物的能源利用效率至关重要。然而，由不同的用户行为和潜在的传感器错误引起的建筑能源数据中固有的波动性和噪声，给能耗预测带来了重大挑战。为了解决这些问题，提出了一种用于建筑能耗预测的双重优化框架 （IDBO-VMD-IDBO-BiLSTM），该框架结合了改进的蜣螂优化算法 （IDBO）、变分模态分解 （VMD） 和双向长短期记忆网络 （BiLSTM）。在此框架中，IDBO 首先通过自适应确定其最优参数来优化 VMD，将原始建筑能耗序列分解为多个具有更平滑特性的本征模态函数 （IMF），从而达到缓解数据噪声的效果。然后，使用 BiLSTM 模型预测每个 IMF 分量，IDBO 为 BiLSTM 选择最佳超参数。最后，将每个 IMF 的单个预测叠加并重建，以产生最终预测。为了验证该框架的有效性，从上海的一栋写字楼收集了真实的能源消耗数据，并与其他七个比较模型进行了全面比较。实验结果表明，所提出的框架在均方根误差 （RMSE） 、平均绝对百分比误差 （MAPE） 、平均绝对误差 （MAE） 和决定系数 （R2） 方面优于比较模型，表现出较高的预测准确性和较强的稳健性。因此，所提出的框架可以成为预测建筑能耗的有效工具。