CRS4, Center for Advanced Studies, Research and Development in Sardinia, Loc. Piscina Manna Ed. 1, 09050 Pula, CA, Italy.
Sensors (Basel). 2022 Jun 14;22(12):4481. doi: 10.3390/s22124481.
Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants' habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.
估计家庭能源使用模式和用户消费习惯是需求响应计划管理和控制技术的基本要求，这导致人们对非侵入式负荷分解方法越来越感兴趣。在这项工作中，我们提出了一种从智能电表获取的低频电消耗测量值和上下文环境信息中对家庭的电力负荷进行非侵入式分解的新方法。所提出的方法允许采用无监督和非侵入式的方法，将负载分为与环境条件和居住者习惯相关的两个分量。我们使用贝叶斯方法，通过利用实际的电力负荷信息来更新用户消费习惯的先验估计，以获得每小时分辨率的两个分量的概率预测。我们在基准数据集上获得了非常好的准确性，优于其他无监督方法的准确性，并且与基于深度学习的监督算法的结果相当。所提出的方法在应用上具有很大的兴趣，因为仅从电力消耗的时间序列知识，就可以识别出可以从中提取能源需求灵活性的家庭，并实现各自负载分量的预测。
