Forecasting techniques have gained considerable prominence within the electric energy sector. Many studies have been documented in the literature, addressing various facets of the energy grid, ranging from power generation to end-user consumption. However, it is noteworthy that the prediction of individual appliance demand has remained relatively unexplored despite its increasing significance, particularly in modern power grids characterized by a dominant presence of distributed energy resources. In light of this research gap, this work focuses on developing and evaluating methodologies for forecasting active power consumption at the device level in the context of industrial kitchens. Three post-processing algorithms are also proposed to improve the forecasting accuracy by leveraging historical predictions. A comprehensive case study employing sub-metered data from 15 industrial kitchen devices was conducted to validate the proposed methods, spanning both hour-ahead and day-ahead scenarios. The results demonstrate the effectiveness of the proposed methods in both forecasting horizons, particularly of the post-processing techniques that show average improvements of over 30% in day-ahead and 50% in hour-ahead, compared to the original predictions.

中文翻译：

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工业厨房用具消耗预测：通过后处理改进来预测未来一小时和一天的情况

预测技术在电力能源领域已经获得了相当的重视。文献中记录了许多研究，涉及能源网的各个方面，从发电到最终用户消费。然而，值得注意的是，尽管个人电器需求的预测越来越重要，但它仍然相对未经探索，特别是在以分布式能源占主导地位的现代电网中。鉴于这一研究空白，这项工作的重点是开发和评估工业厨房背景下设备级有功功耗预测的方法。还提出了三种后处理算法，以利用历史预测来提高预测精度。使用来自 15 个工业厨房设备的分表计量数据进行了一项全面的案例研究，以验证所提出的方法，涵盖提前一小时和提前一天的场景。结果证明了所提出的方法在两个预测范围内的有效性，特别是后处理技术，与原始预测相比，日前的平均改进超过 30%，提前一小时的平均改进超过 50%。