Dataport and NILMTK: A building data set designed for non-intrusive load monitoring

Parson, Oliver; Fisher, G. Burch; Hersey, April; Batra, Nipun; Kelly, Jack; Singh, Amarjeet; Knottenbelt, William J.; Rogers, Alex

doi:10.1109/globalsip.2015.7418187

Cited by 103 publications

(51 citation statements)

References 10 publications

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“…Such data set composition can be seen, for example, in the REDD dataset used in this study. The Nilm toolkit (NilmTK) was developed to assist such a task and has been used in different studies [14,15].…”

Section: Non-intrusive Approachmentioning

confidence: 99%

“…Third, if the NILM system is able monitor the time of use and pattern of each appliance, implicit demand response signals can be advised by the system to shift the load to a time of day when electricity pricing is more convenient [13]. However, as mentioned before, collecting data sets is expensive, time consuming, and for a time step of 1 Hz (1 s) may result in large files that are difficult to store or process [14].…”

Section: Non-intrusive Approachmentioning

confidence: 99%

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Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector

et al. 2019

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Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.

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Section: Non-intrusive Approachmentioning

confidence: 99%

Section: Non-intrusive Approachmentioning

confidence: 99%

Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector

et al. 2019

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“…This leaves us with the dataset from Pecan Street Inc's Dataport [36]. Thankfully, Dataport is the largest provider of disaggregated (i.e., appliance-level) customer energy data [35], as seen from Table 2. In Fig.…”

Section: Appliance Usagementioning

confidence: 99%

“…In Fig. 1, we show the number of occurrences of individual appliances on Dataport [35]. Therefore, we collect the time-series appliance-level data from Dataport for the period of a year.…”

Section: Appliance Usagementioning

confidence: 99%

Solving the fair electric load shedding problem in developing countries

Oluwasuji

Malik

Zhang

et al. 2019

Auton Agent Multi-Agent Syst

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Often because of limitations in generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. This leaves households in disconnected parts without electricity, causing them inconvenience and discomfort. Without fairness being taken into due consideration during load shedding, some households may suffer more than others. In this paper, we solve the fair load shedding problem (FLSP) by creating solutions which connect households to supply based on some fairness criteria (i.e., to fairly connect homes to supply in terms of duration, their electricity needs, and their demand), which we model as their utilities. First, we briefly describe some state-of-art household-level load shedding heuristics which meet the first criteria. Second, we model the FLSP as a resource allocation problem, which we formulate into two Mixed Integer Programming (MIP) problems based on the Multiple Knapsack Problem. In so doing, we use the utilitarian, egalitarian and envy-freeness social welfare metrics to develop objectives and constraints that ensure our FLSP solutions results in fair allocations that consider the utilities of agents. Then, we solve the FLSP and show that our MIP models maximize the groupwise and individual utilities of agents, and minimize the differences between their pairwise utilities under a number of experiments. When taken together, our endeavour establishes a set of benchmarks for fair load shedding schemes, and provide insights for designing fair allocation solutions for other scarce resources.

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“…In other words, it requires less sensor deployment to obtain the same level of reconstruction accuracy compared to any other sensor installation. We evaluate ActSense on a publicly available dataset called Dataport [30], which is the largest public energy dataset collected in U.S. With a fixed budget of sensors, our model achieves better energy breakdown performance compared to three baseline approaches. Besides, extensive analysis of the experiments shows that integrating the temporal seasonal information can help to foresee the energy usage trends and prepare the sensor installation in advance.…”

Section: Introductionmentioning

confidence: 99%

Active Collaborative Sensing for Energy Breakdown

Jia

Batra

Wang

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed submeters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising.In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.

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Dataport and NILMTK: A building data set designed for non-intrusive load monitoring

Cited by 103 publications

References 10 publications

Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector

Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector

Solving the fair electric load shedding problem in developing countries

Active Collaborative Sensing for Energy Breakdown

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