Electrical-end-use data from 23 houses sampled each minute for simulating micro-generation systems

Johnson, Geoffrey; Beausoleil-Morrison, Ian

doi:10.1016/j.applthermaleng.2016.07.133

Cited by 26 publications

(15 citation statements)

References 19 publications

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“…The performance of the heuristics were assessed in terms of connections and comfort in[32], as in a chore division setting. However, to conform with the results to be evaluated later in this paper, we reproduce the results in[32] in terms of connections and comfort, as in a cake-cutting setting 19. Note that the results presented inTable 3are obtained from solving the fair load shedding problem once 20.…”

mentioning

confidence: 90%

“…For this reason, any dataset adapted to the developing country context should be one from which the consumption data of appliances commonly used in developing countries can be extracted. Consequently, the HES [9], RBSA [51], OCTES [50], EEU (known as the Electrical-end-use data) [19] and REFIT [27] datasets are inadequate as they were collected at the submeter level. This leaves us with the dataset from Pecan Street Inc's Dataport [36].…”

Section: Appliance Usagementioning

confidence: 99%

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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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confidence: 90%

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

View full text Add to dashboard Cite

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“…They emphasized that most of the consumed energy in the residential building is used for heating and cooling. Johnson and Beausoleil‐Morrison conducted a study for obtaining electricity consumption data from 23 residential buildings in Canada in order to determine the electricity demand profile for entire house, non‐HVAC, air conditioning, and furnace. Kipping and Tromborg modeled and disaggregated hourly electricity consumption by using smart meter data in residential building in Norway.…”

Section: Introductionmentioning

confidence: 99%

Potential use of thermal energy storage for shifting cooling and heating load to off‐peak load: A case study for residential building in Canada

Erdemir

Dinçer

2020

Energy Storage

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This study presents an investigation of the potential use of thermal energy storage for shifting cooling and heating loads to off-peak hours in order to balance the electricity production and demand periods. In Canada, the main energy source for cooling and heating applications is generally electricity. Therefore, cooling and heating devices have a great impact on the electricity peak load. In this study, it is considered that heating and cooling loads on the electricity peak load periods are shifted to off-peak hours by thermal energy storage systems. At the end of this study, it is observed that the thermal energy storage has great potential for shifting electricity peak load depending on cooling and heating load to off-peak periods. The electricity peak loads can be reduced by 25% and 45% by shifting heating and cooling loads to off-peak hours and doing storage. Furthermore, the thermal energy storage systems can help reduce both cooling and heating costs in Canadian dwellings by 20% and 18%, respectively. K E Y W O R D Scooling, cost assessment, heating, thermal energy storage

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“…An equivalent metric was proposed in Kolter and Jaakkola (2012), but this time considering the individual appliance error (IATEAE) rather than the average between all the appliances, which reduces the chance of reporting large errors in certain time slices due to single appliances performing very poorly. (Picon et al, 2016) SustDataED (Ribeiro, Pereira, Quintal, & Nunes, 2016) EEUD (Johnson & Beausoleil-Morrison, 2017) RAE (Makonin, Wang, & Tumpach, 2018) ESHL (Kaibin Bao, 2016) BLOND (Kriechbaumer & Jacobsen, 2018) a In the original version (2012) there is data for three houses between 3 and 4 weeks, but only one of them contains submetered data. In the 2017 edition there is data for seven houses during 3 years.…”

Section: Performance Metricsmentioning

confidence: 99%

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Pereira¹,

Nunes

2018

WIREs Data Min & Knowl

139

103

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Non-intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large-scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross-datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology.

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Electrical-end-use data from 23 houses sampled each minute for simulating micro-generation systems

Cited by 26 publications

References 19 publications

Solving the fair electric load shedding problem in developing countries

Solving the fair electric load shedding problem in developing countries

Potential use of thermal energy storage for shifting cooling and heating load to off‐peak load: A case study for residential building in Canada

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

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