Approaches to Non-Intrusive Load Monitoring (NILM) in the Home

When designing and implementing an intelligent energy conservation system for the home, it is essential to have insight into the activities and actions of the occupants. In particular, it is important to understand what appliances are being used and when. In the computational sustainability research community this is known as load disaggregation or Non-Intrusive Load Monitoring (NILM). NILM is a foundational algorithm that can disaggregate a home’s power usage into the individual appliances that are running, identify energy conservation opportunities. This depth report will focus on NILM algorithms, their use and evaluation. We will examine and evaluate the anatomy of NILM, looking at techniques using load monitoring, event detection, feature ex- traction, classification, and accuracy measurement.&nbsp

HUE: The Hourly Usage of Energy Dataset for Buildings in British Columbia

The HUE dataset contains donated data from residential customers of BCHydro, a provincial power utility. There are currently twenty-two houses contain within the dataset with most houses having three years of consumption history. Data is downloaded from BCHydro’s customer web porthole by each customer how donated the data. The porthole only allows customers to download a maximum of three years worth of data. Only BCHydro customers were asked to donate to keep the data quality consistent. Weather data from the nearest weather station is also included

Home Occupancy Agent: Occupancy and Sleep Detection

Smart homes of the future will have a numberof different types of sensors. What types of sensors and howthey will be used depends on the behaviour needed from thesmart home. Using the sensors to automatically determine ifa home is occupied can lead to a wide range of benefits. Forexample, it could trigger a change in the thermostat setting tosave money, or even a change in security monitoring systems.Our prototype Home Occupancy Agent (HOA), which we presentin this paper, uses a rule based system that monitors powerconsumption from meters and ambient light sensor readings inorder to determine occupancy. The agent is also able to determinewhen the occupants are asleep, and thus provide the potentialfor further energy saving opportunities

Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps

Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption to help save the environment all while saving money. Non-intrusive load monitoring (NILM) tries to do just that. One of the hardest problems NILM faces is the ability to run unsupervised -- discovering appliances without prior knowledge -- and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. We propose a solution that can do this with the use of an advanced filter pipeline to preprocess the data, a Gaussian appliance model with a probabilistic knapsack algorithm to disaggregate the aggregate smart meter signal, and partition maps to label which appliances were found and how much energy they use no matter the country/region. Experimental results show that relatively complex appliance signals can be tracked accounting for 93.7% of the total aggregate energy consumed

Nonintrusive Load Monitoring (NILM) Performance Evaluation A unified approach for accuracy reporting

Abstract Nonintrusive load monitoring (NILM), sometimes referred to as load disaggregation, is the process of determining what loads or appliances are running in a house from analysis of the power signal of the whole-house power meter. As the popularity of NILM grows, we find there is no consistent way researchers are measuring and reporting accuracies. In this short communication, we present a unified approach that would allow for consistent accuracy testing

Load Disaggregation Based on Aided Linear Integer Programming

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP) based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures, and hence works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation