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Aggregated short-term load forecasting for heterogeneous buildings using machine learning with peak estimation
Energy and Buildings ( IF 6.7 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.enbuild.2021.110742
Amine Bellahsen , Hanane Dagdougui

System operations and planning are crucial aspects of power system management. They aim to maintain the equilibrium of electricity supply and demand while ensuring reliable and secure power system operation. Consumers have to pay more for electricity during periods of high peak demand in various sectors. If consumers have knowledge about expected peak load ahead of time, such extra charges could potentially be avoided. Accurate energy demand forecasting, and therefore expected peak load information, not only will help to provide a reliable supply of electricity, but also can be useful in reducing the cost of electricity at the consumer level. In this paper, we develop a comparative study for aggregated short-term load forecasting using different data strategies and compare two prediction levels: predicting the aggregated load using a district-level data set, and performing predictions on a lower level and then aggregating them at the district level. After finding the best forecasting model and strategy, these accurate predictions will help to predict the percentage of peak over a certain subscribed power in the entire district. The results showed that the mean absolute percentage error is between 1.67% and 4.80% depending on the machine learning algorithm and the prediction horizon used.



中文翻译:

使用峰值估计的机器学习对异构建筑物的短期负荷总和进行预测

系统操作和计划是电力系统管理的关键方面。他们旨在维持电力供需平衡,同时确保可靠和安全的电力系统运行。在各个部门的高峰需求期间,消费者必须为电费支付更高的费用。如果消费者提前了解预期的峰值负载,则可以避免这种额外的费用。准确的能源需求预测,以及因此的预期峰值负荷信息,不仅将有助于提供可靠的电力供应,而且还有助于降低用电方的电力成本。在本文中,我们针对使用不同数据策略的汇总短期负荷预测进行了比较研究,并比较了两个预测级别:使用地区级别的数据集预测汇总负载,然后在较低级别执行预测,然后在地区级别进行汇总。在找到最佳的预测模型和策略之后,这些准确的预测将有助于预测整个区域中特定订购功率上的峰值百分比。结果表明,取决于机器学习算法和所使用的预测范围,平均绝对百分比误差在1.67%和4.80%之间。

更新日期:2021-02-28
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