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首页> 外文期刊>International journal of forecasting >A hybrid machine learning model for forecasting a billing period's peak electric load days
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A hybrid machine learning model for forecasting a billing period's peak electric load days

机译：用于预测计费周期的高峰用电天数的混合机器学习模型

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Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：已经研究了许多模型来预测峰值用电负荷，但很少有研究针对计费期的峰值用电负荷天数进行预测。该关注点与消费者密切相关，因为其用电成本不仅取决于总消耗量，还取决于一段时间内所需的峰值负载。准确预测这些高峰日可以有效地计划和执行需求响应操作，从而减轻这些高峰及其相关的成本。我们提出了一种基于ARIMA，逻辑回归和人工神经网络模型的混合模型。此混合模型评估这些统计模型和机器学习模型的各个结果，以便预测给定的一天是否将是计费期的高峰时段。拟议的模型可以准确预测实际峰值负荷天数的70％（40/57），并显示美国大学在一年的测试期间可能节省约80,000美元。 （C）2019国际预报员学会。由Elsevier B.V.发布。保留所有权利。

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