Eirinaki Magdalini, Varlamis Iraklis, Dahihande Janhavi, Jaiswal Akshay, Pagar Akshay Anil, Thakare Ajinkya
Computer Engineering Department, San José State University, San Jose, CA, United States.
Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece.
Front Big Data. 2022 Sep 20;5:972206. doi: 10.3389/fdata.2022.972206. eCollection 2022.
According to several studies, the most influencing factor in a household's energy consumption is user behavior. Changing user behavior to improve energy usage leads to efficient energy consumption, saving money for the consumer and being more friendly for the environment. In this work we propose a framework that aims at assisting households in improving their energy usage by providing real-time recommendations for efficient appliance use. The framework allows for the creation of household-specific and appliance-specific energy consumption profiles by analyzing appliance usage patterns. Based on the household profile and the actual electricity use, real-time recommendations notify users on the appliances that can be switched off in order to reduce consumption. For instance, if a consumer forgets their A/C on at a time that it is usually off (e.g., when there is no one at home), the system will detect this as an outlier and notify the consumer. In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems. However, in the general case, the household may only have one main meter reading. In this case, non-intrusive load monitoring (NILM) techniques, which monitor a house's energy consumption using only one meter, and data mining algorithms that disaggregate the consumption into appliance level, can be employed. In this paper, we propose an end-to-end solution to this problem, starting with the energy disaggregation process, and the creation of user profiles that are then fed to the pattern mining and recommendation process, that through an intuitive UI allows users to further refine their energy consumption preferences and set goals. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset for our experimental evaluations and the proof-of-concept implementation. The results show that the proposed framework accurately captures the energy consumption profiles of each household and thus the generated recommendations are matching the actual household energy habits and can help reduce their energy consumption by 2-17%.
根据多项研究，家庭能源消耗中最具影响力的因素是用户行为。改变用户行为以改善能源使用可实现高效能源消耗，为消费者省钱并对环境更友好。在这项工作中，我们提出了一个框架，旨在通过提供高效电器使用的实时建议来帮助家庭改善其能源使用。该框架通过分析电器使用模式来创建特定于家庭和特定于电器的能源消耗概况。基于家庭概况和实际用电量，实时建议会通知用户哪些电器可以关闭以减少消耗。例如，如果消费者在通常关闭空调的时间（例如，家中无人时）忘记关闭，系统会将此检测为异常值并通知消费者。在理想情况下，家庭安装了智能电表监测系统，该系统可在电器层面记录能源消耗。这也反映在可用于评估此类系统的数据集上。然而，一般情况下，家庭可能只有一个主电表读数。在这种情况下，可以采用非侵入式负载监测（NILM）技术，即仅使用一个电表监测房屋的能源消耗，以及将消耗分解到电器层面的数据挖掘算法。在本文中，我们针对此问题提出了一种端到端的解决方案，从能源分解过程开始，创建用户概况，然后将其输入到模式挖掘和推荐过程中，通过直观的用户界面，用户可以进一步细化他们的能源消耗偏好并设定目标。我们使用UK-DALE（英国国内电器级电力）数据集进行实验评估和概念验证实施。结果表明，所提出的框架准确地捕捉了每个家庭的能源消耗概况，因此生成的建议与实际家庭能源习惯相匹配，并可帮助他们将能源消耗降低2% - 17%。
