Appliance Recognition in an OSGi-based Home Energy Management Gateway

The rational use and management of energy is considered a key societal and technological challenge. Home energy management systems (HEMS) have been introduced especially in private home domains to support users in managing and controlling energy consuming devices. Recent studies have shown that informing users about their habits with appliances as well as their usage pattern can help to achieve energy reduction in private households. This requires instruments able to monitor energy consumption at fine grain level and provide this information to consumers. While the most existing approaches for load disaggregation and classification require high-frequency monitoring data, in this paper we propose an approach that exploits low-frequency monitoring data gathered by meters (i.e., Smart Plugs) displaced in the home. Moreover, while the most existing works dealing with appliance classification delegate the classification task to a remote central server, we propose a distributed approach where data processing and appliance recognition are performed locally in the Home Gateway. Our approach is based on a distributed load monitoring system made of Smart Plugs attached to devices and connected to a Home Gateway via the ZigBee protocol. The Home Gateway is based on the OSGi platform, collects data from home devices, and hosts both data processing and user interaction logic

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Outlier Detection in Energy Datasets

In the past decade, numerous datasets have been released with the explicit goal of furthering non-intrusive load monitoring research (NILM). NILM is an energy measurement strategy that seeks to disaggregate building-scale loads. Disaggregation attempts to turn the energy consumption of a building into its constituent appliances. NILM algorithms require representative real-world measurements which has led institutions to publish and share their own datasets. NILM algorithms are designed, trained, and tested using the data presented in a small number of these NILM datasets. Many of the datasets contain arbitrarily selected devices. Likewise, the datasets themselves report aggregate load information from building(s) which are similarly selected arbitrarily. This raises the question of the representativeness of the datasets themselves as well as the algorithms based on their reports. One way to judge the representativeness of NILM datasets is to look for the presence of outliers in these datasets. This paper presents a novel method of identifying outlier devices from NILM datasets. With this identification process, it becomes possible to mitigate and measure the impact of outliers. This represents an important consideration to the long-term deployment of NILM algorithms

Deep Learning on Smart Meter Data: Non-Intrusive Load Monitoring and Stealthy Black-Box Attacks

Climate change and environmental concerns are instigating widespread changes in modern electricity sectors due to energy policy initiatives and advances in sustainable technologies. To raise awareness of sustainable energy usage and capitalize on advanced metering infrastructure (AMI), a novel deep learning non-intrusive load monitoring (NILM) model is proposed to disaggregate smart meter readings and identify the operation of individual appliances. This model can be used by Electric power utility (EPU) companies and third party entities, and then utilized to perform active or passive consumer power demand management. Although machine learning (ML) algorithms are powerful, these remain vulnerable to adversarial attacks. In this thesis, a novel stealthy black-box attack that targets NILM models is proposed. This work sheds light on both effectiveness and vulnerabilities of ML models in the smart grid context and provides valuable insights for maintaining security especially with increasing proliferation of artificial intelligence in the power system

Scalable Data-driven Modeling and Analytics for Smart Buildings

Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches — that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis — applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that au- tomatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to re- port city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs

Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders. The strain this places on services means providing 24-hour monitoring is not sustainable. No solution exists to non-intrusively monitor the wellbeing of patients with dementia, resulting in delayed intervention. Using machine learning and signal processing, domestic energy supplies can be disaggregated to detect appliance usage. This enables Activities of Daily Living (ADLs) to be assessed. The aim is to facilitate early intervention and enable patients to stay in their homes for longer. A Support Vector Machine (SVM) and Random Decision Forest classifier are modelled using data from three test homes. The trained models are then used to monitor two patients with dementia during a six-month clinical trial undertaken in partnership with Mersey Care NHS Foundation Trust. In the case of load disaggregation, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are also analysed to identify the behavioural patterns of the occupant while detecting alterations in routine. The approach is sensitive in identifying behavioural routines and detecting anomalies in patient behaviour