Electricity forecasting on the individual household level enhanced based on activity patterns.
Electricity forecasting on the individual household level enhanced based on activity patterns.
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DOI：
10.1371/journal.pone.0174098
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Ząbkowski T
中科院分区：
文献类型：
--
作者：
Gajowniczek K;Ząbkowski T
利用智能计量解决方案来支持单个家庭层面的能源效率，在监测使用情况和提供准确的负荷预测方面提出了新的研究挑战。预测用电量是为智能电表提供智能的一个特别重要的组成部分。在本文中，我们提出了一种在家庭层面进行负荷预测的改进方法。为了提高预测模型的准确性，考虑了居民日常活动和家电使用对整个家庭用电量的影响。本文的贡献有三个方面：(1)我们解决了未来24小时的短期电力负荷预测，而不是在总体上，而是在单个家庭层面上，这符合居民电力负荷预测（RPLF）方法；(2)在预测方面，我们使用了一个家庭特定的影响电力消耗的行为数据集，该数据集使用分割和序列挖掘算法导出；(3)采用不同的预测算法，结合家庭用电活动模式进行负荷预测研究。
Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents’ daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.