Stephen Makonin

AMPds: A public dataset for load disaggregation and eco-feedback research

A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.

A Smarter Smart Home: Case Studies of Ambient Intelligence

Technological support for sustainable home use lies in more subtle and contextually appropriate interventions that integrate informative models of occupant behavior, provide hybrid levels of automated control, and use ambient sensing for localized decisions.

Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring

Understanding how appliances in a house consume power is important when making intelligent and informed decisions about conserving energy. Appliances can turn ON and OFF either by the actions of occupants or by automatic sensing and actuation (e.g., thermostat). It is also difficult to understand how much a load consumes at any given operational state. Occupants could buy sensors that would help, but this comes at a high financial cost. Power utility companies around the world are now replacing old electro-mechanical meters with digital meters (smart meters) that have enhanced communication capabilities. These smart meters are essentially free sensors that offer an opportunity to use computation to infer what loads are running and how much each load is consuming (i.e., load disaggregation). We present a new load disaggregation algorithm that uses a super-state hidden Markov model and a new Viterbi algorithm variant which preserves dependencies between loads and can disaggregate multi-state loads, all while performing computationally efficient exact inference. Our sparse Viterbi algorithm can efficiently compute sparse matrices with a large number of super-states. Additionally, our disaggregator can run in real-time on an inexpensive embedded processor using low sampling rates.

Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014.

With the cost of consuming resources increasing (both economically and ecologically), homeowners need to find ways to curb consumption. The Almanac of Minutely Power dataset Version 2 (AMPds2) has been released to help computational sustainability researchers, power and energy engineers, building scientists and technologists, utility companies, and eco-feedback researchers test their models, systems, algorithms, or prototypes on real house data. In the vast majority of cases, real-world datasets lead to more accurate models and algorithms. AMPds2 is the first dataset to capture all three main types of consumption (electricity, water, and natural gas) over a long period of time (2 years) and provide 11 measurement characteristics for electricity. No other such datasets from Canada exist. Each meter has 730 days of captured data. We also include environmental and utility billing data for cost analysis. AMPds2 data has been pre-cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms.

The cognitive power meter: Looking beyond the smart meter

The smart meter is often heralded as the key component supporting energy displays that can notify home occupants of their energy usage. But, a smart meter is only a digital power meter with enhanced communications capabilities - it is not actually smart. We need to look beyond the smart meter and define what intelligence is needed to actually make a meter smart. One area with promise is load disaggregation. Load disaggregation can be used to determine what loads contributing to the consumption reading at the smart meter. A smart meter incorporating load disaggregation intelligence can be seen as going beyond the traditional smart meter - what we call a cognitive power meter (c-meter). However, using load disaggregation, in its current form, is not feasible. We critically review the requirements for a c-meter and provide insights as to how load disaggregation research needs to change to make the c-meters a reality.

Elements of consumption: an abstract visualization of household consumption

To promote sustainability consumers must be informed about their consumption behaviours. Ambient displays can be used as an eco-feedback technology to convey household consumption information. Elements of Consumption (EoC) demonstrates this by visualizing electricity, water, and natural gas consumption. EoC delivers three key components: (1) an abstract art piece, (2) a visual way to display data, and (3) to use an abstract art piece as a visual way to display data in order to persuade homeowners to conserve.

An intelligent agent for determining home occupancy using power monitors and light sensors

Smart homes of the future will have a number of different types of sensors. What types of sensors and how they will be used depends on the behaviour needed from the smart home. Using the sensors to automatically determine if a home is occupied can lead to a wide range of benefits. For example, it could trigger a change in the thermostat setting to save money, or even a change in security monitoring systems. Our prototype Home Occupancy Agent (HOA), which we present in this paper, uses a rule based system that monitors power consumption from meters and ambient light sensor readings in order to determine occupancy.

A Consumer Bill of Rights for Energy Conservation

Sustainable energy supply and demand can partially be solved by the conservation of energy, which is a personal and self-driven action. However, energy conservation currently requires the purchase of third-party products. The upfront cost of purchasing these products to monitor energy consumption in a home is a barrier that further cements the divide of those that have and those that have not. Detailed appliance power consumption reporting should be made available for free as part of the homes smart meter. Governments and power utilities must improve and expand policies that promote a socio-economic balance allowing everyone to participate in energy conservation regardless of their economic situation in a sustained way. We critically look at what economics and government polices exist and need to exist. We also demonstrate the computational means to achieve this - nonintrusive load monitoring (NILM) - and discuss how manufacturing and standards organizations need to work together to provide the essential information that describes how appliances consume energy. This paper proposes a Consumer Bill of Rights for Energy Conservation.

Inspiring energy conservation through open source power monitoring and in-home display

Many homeowners and occupants are interested in energy conservation for economical and/or ecological reasons. A number of commercial energy conservation solutions exist on the market today. However, these products contain closed systems and do not provide easy access to much of the raw data needed for more sophisticated analysis. An open source solution would be a great benefit for homeowners and occupants, allowing access to (and custom analysis of) raw power readings. We present a complete open source solution that monitors power, stores raw power readings, and makes provision for an in-home display, that informs stakeholders about energy consumption through a real-time ambient feedback effectively becoming an eco-feedback device.

RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis

Datasets are important for researchers to build models and test how well their machine learning algorithms perform. This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms which make use of smart meter data. This initial release of RAE contains 1Hz data (mains and sub-meters) from two a residential house. In addition to power data, environmental and sensor data from the houses thermostat is included. Sub-meter data from one of the houses includes heat pump and rental suite captures which is of interest to power utilities. We also show and energy breakdown of each house and show (by example) how RAE can be used to test non-intrusive load monitoring (NILM) algorithms.