Stephen Makonin

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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(More)
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.(More)
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(More)
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(More)
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(More)
Understanding appliance power consumption can help occupants optimize their power consumption behaviour. One popular class of methods for determining appliance power consumption is known as non-intrusive load monitoring (NILM). This paper shows how to incorporate time-of-day appliance usage patterns into a recent NILM method, resulting in both improved(More)
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. RAE contains 72 days of 1Hz data from a residential house’s mains and 24 submeters(More)
Non-Intrusive Load Monitoring (NILM) researchers have always assumed the switch continuity principle (SCP), which assumes that only one appliance ever changes state at any given point in time. However, SCP cannot be relied upon 100% of the time, especially when unsupervised NILM is used to guess what appliances might be in a house. This principle breaks(More)
Existing surveys in visual analytics focus on the importance of the topic. However, many do not discuss the increasingly critical area of mixed-initiative systems. In this survey we discuss the importance of research in mixed-initiative systems and how it is different from visual analytics and other research fields. We present the conceptual architecture of(More)
Ubiquitous technology platforms have been created to track and improve health and fitness; similar technologies can help individuals monitor and reduce their carbon footprints. This paper proposes CarbonKit – a platform combining technology, markets, and incentives to empower and reward people for reducing their carbon footprint. We argue that a(More)