A Methodology for Energy Usage Prediction in Long-Lasting Abnormal Events
A Methodology for Energy Usage Prediction in Long-Lasting Abnormal Events
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DOI：
10.1109/cogmi56440.2022.00023
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Gabriele Maurina;Hajar Homayouni;Sudipto Ghosh;I. Ray;G. Duggan
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作者：
Gabriele Maurina;Hajar Homayouni;Sudipto Ghosh;I. Ray;G. Duggan
准确的能源消耗预测对于合理配置资源、满足能源需求和能源供应安全至关重要。这项工作旨在开发一种方法，用于准确建模和预测异常长期事件（例如 COVID-19 大流行）期间的用电量，这些事件极大地影响了不同类型场所的消费模式。所提出的方法包括三个步骤：(A) 在多个模型中选择正常条件下能耗预测最准确的模型，(B) 使用所选模型分析特定异常事件对各类场所能耗的影响，(C) 研究哪些特征对异常条件下的能耗预测贡献最大，以及可以添加哪些特征来改进此类预测。我们使用从 Fort Collins Utilities 获得的数据集来使用 COVID-19 作为案例研究，其中包含住宅和不同规模的商业和建筑的能源消耗数据。位于美国科罗拉多州柯林斯堡市的工业厂房。我们还使用 NOAA 的温度记录和拉里默县的 COVID-19 公共命令。我们通过证明该方法可以帮助使用代表性特征设计适合大流行情况的模型来验证该方法，从而准确预测能源消耗。我们的结果表明，我们的方法选择的 MLP 模型比其他模型表现更好，即使它们都使用了与 COVID 相关的特征。我们还证明该方法可以帮助衡量大流行对能源消耗的影响。
Accurate energy consumption prediction is critical for proper resource allocation, meeting energy demand, and energy supply security. This work aims at developing a methodology for accurately modeling and predicting electricity consumption during abnormal long-lasting events, such as COVID-19 pandemic, which considerably affect consumption patterns in different types of premises. The proposed methodology involves three steps: (A) selects among multiple models the most accurate one in energy consumption prediction under normal conditions, (B) uses the selected model to analyze the impact of a specific abnormal event on energy consumption for various classes of premises, and (C) investigates which features contribute most to energy consumption prediction for abnormal conditions and which features can be added to improve such predictions.We use COVID-19 as a case study with datasets obtained from Fort Collins Utilities, which contain energy consumption data for residential and different sizes of commercial and industrial premises in the city of Fort Collins, Colorado, USA. We also use temperature records from NOAA and COVID-19 public orders from Larimer County.We validate the methodology by demonstrating that the methodology can help design a model suited for the pandemic situation using representative features, and as a result, accurately predict the energy consumption. Our results show that the MLP model selected by our methodology performs better than the other models even when they all use the COVID-related features. We also demonstrate that the methodology can help measure the impacts of the pandemic on the energy consumption.