Non-intrusive load identification for smart outlets

An increasing interest in energy-efficiency combined with the decreasing cost of embedded networked sensors is lowering the cost of outlet-level metering. If these trends continue, new buildings in the near future will be able to install \u27smart\u27 outlets, which monitor and transmit an outlets power usage in real time, for nearly the same cost as conventional outlets. One problem with the pervasive deployment of smart outlets is that users must currently identify the specific device plugged into each meter, and then manually update the outlets meta-data in software whenever a new device is plugged into the outlet. Correct meta-data is important in both interpreting historical outlet energy data and using the data for building management. To address this problem, we propose Non-Intrusive Load Identification (NILI), which automatically identifies the device attached to a smart outlet without any human intervention. In particular, in our approach to NILI, we identify an intuitive and simple-to-compute set of features from time-series energy data and then employ well-known classifiers. Our results achieve accuracy of over 90% across 15 device types on outlet-level energy traces collected from multiple real homes

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsiveness

Time-based pricing programs for domestic electricity users have been effective in reducing peak demand and facilitating renewables integration. Nevertheless, high cost, price non-responsiveness and adverse selection may create the possible challenges. To overcome these challenges, it can be fruitful to investigate the ‘high-potential’ users, which are more responsive to price changes and apply time-based pricing to these users. Few studies have investigated how to identify which users are more price-responsive. We aim to fill this gap by comprehensively identifying the drivers of domestic users’ price responsiveness, in order to facilitate the selection of the high-potential users. We adopt a novel data-driven approach, first by a feed forward neural network model to accurately determine the baseline monthly peak consumption of individual households, followed by an integrated machine-learning variable selection methodology to identify the drivers of price responsiveness applied to Irish smart meter data from 2009-10 as part of a national Time of Use trial. This methodology substantially outperforms traditional variable selection methods by combining three advanced machine-learning techniques. Our results show that the response of energy users to price change is affected by a number of factors, ranging from demographic and dwelling characteristics, psychological factors, historical electricity consumption, to appliance ownership. In particular, historical electricity consumption, income, the number of occupants, perceived behavioural control, and adoption of specific appliances, including immersion water heater and dishwasher, are found to be significant drivers of price responsiveness. We also observe that continual price increase within a moderate range does not drive additional peak demand reduction, and that there is an intention-behaviour gap, whereby stated intention does not lead to actual peak reduction behavior. Based on our findings, we have conducted scenario analysis to demonstrate the feasibility of selecting the high potential users to achieve significant peak reduction

Design and Implementation of Integrated Smart Home Energy Management Systems for Clusters of Buildings

To ensure the balance between power generation and demand at the peak hours, power utilities need to keep additional power generation capacity on standby which usually causes higher operational cost. This will result in variations in hourly to seasonal electricity prices. With a focus on the physical layer integration, we developed a methodology which includes a prototype of a cluster of smart homes with energy management systems (SHEMS) to study and harvest the flexibility in the demand side in the residential building sector by monitoring and controlling the loads at appliances level. For this goal, we developed and fabricated the electronic circuit of five custom-designed smart plugs (DC, MQTT based) and integrated a vendor-based smart plug (AC) to the system. The devices were equally allocated to three home hubs. As opposed to a standalone SHEMS, this methodology is applied at a cluster scale: through awareness of the electricity consumption of all houses, under certain assumptions optimized load patterns can be generated not only to decrease consumers’ electricity bills, but also to meet the grid’s constraints. We crafted 3 scenarios as showcases of the methodology performance, with two electricity price plans and different load configurations. The results show both smart plug types were successful in measuring the loads and communication with other layers resulting in decreased electricity cost. Additionally, using a hybrid cloud-fog based architecture, a function was designed for saving the smart plugs records during cloud service or internet disconnection to enable later synchronization of the local and cloud database