Current research into electricity consumption forecasting for General Hospital still has considerable scope for further development, particularly in its failure to incorporate hospital-specific energy usage characteristics as input variables. This study explores the impact of the usage frequency of sizeable medical equipment on the electricity demand of general hospitals. It proposes a hybrid forecasting algorithm that integrates the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Variational Mode Decomposition (VMD) for signal decomposition with the Hyperband-LSTM deep learning algorithm to enhance prediction accuracy. ICEEMDAN is employed for preprocessing the power consumption series, while VMD is used for the secondary decomposition of high-frequency signals within the series. The Hyperband Pruner is utilized to efficiently adjust the hyperparameters of the LSTM, which is then used for electricity consumption forecasting. The predictive performance of the developed method is assessed by comparing it with 15 different forecasting models. The results indicate that the proposed method demonstrates superior forecasting performance. Applying the model to a real-case scenario, it has reduced the hospital’s electricity consumption by about 15%, providing a referable energy management solution for other medical institutions.

中文翻译：

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基于混合信号分解的综合医院用电量混合预测模型


目前对综合医院用电量预测的研究仍有相当大的进一步发展空间，特别是它未能将医院特定的能源使用特征作为输入变量。本研究探讨了大型医疗设备的使用频率对综合医院电力需求的影响。它提出了一种混合预测算法，该算法将改进的完全集成经验模态分解与自适应噪声 （ICEEMDAN） 和变分模态分解 （VMD） 集成在一起，用于信号分解，并与 Hyperband-LSTM 深度学习算法集成在一起，以提高预测精度。ICEEMDAN 用于对功耗序列进行预处理，而 VMD 用于序列内高频信号的二次分解。Hyperband Pruner 用于有效地调整 LSTM 的超参数，然后将其用于用电量预测。通过与 15 种不同的预测模型进行比较来评估所开发方法的预测性能。结果表明，所提出的方法表现出优异的预测性能。将模型应用于实际场景，使医院的用电量减少了约 15%，为其他医疗机构提供了可参考的能源管理解决方案。