Bayesian model of electrical heating disaggregation

Culière, François; Leduc, Lætitia; Belikov, Alexander V.

doi:10.1145/3427771.3427848

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…At this resolution, most approaches are device-specific. For instance, the authors of [17] use the statistical relationship between daily energy consumption and exterior temperature to disaggregate the electrical heating component.…”

Section: Related Workmentioning

confidence: 99%

Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

Belikov,

Matheron,

Sassi

2022

Preprint

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In this article we present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes. Our model circumvents the inherent difficulty of unsupervised signal disaggregation by using both the shape of a power spike and its time of occurrence to identify the contribution of CWH reliably. Indeed, many CHWs in France are configured to turn on automatically during off-peak hours only, and we are able to use this domain knowledge to aid peak identification despite the low sampling frequency.In order to test our model, we equipped a home with sensors to record the ground-truth consumption of a water heater. We then apply the model to a larger dataset of energy consumption of Hello Watt users consisting of one month of consumption data for 5k homes at 30-minute resolution. In this dataset we successfully identified CWHs in the majority of cases where consumers declared using them. The remaining part is likely due to possible misconfiguration of CWHs, since triggering them during off-peak hours requires specific wiring in the electrical panel of the house. Our model, despite its simplicity, offers promising applications: detection of mis-configured CWHs on off-peak contracts and slow performance degradation.

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Section: Related Workmentioning

confidence: 99%

Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

Belikov,

Matheron,

Sassi

2022

Preprint

Self Cite

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“…In this regard, analyzing heating systems’ relationships with external factors and time-series patterns have been favored. In France, the Hello Watt Company has carried out the heating demand disaggregation according to its relationship with outdoor temperature by means of the piece-wise linear regression method [ 45 ]. However, such techniques cannot efficiently explain the heating load in Quebec residences due to its complex behavior.…”

Section: An Introductory Nilm Practice In Quebec Residencesmentioning

confidence: 99%

Towards Feasible Solutions for Load Monitoring in Quebec Residences

Hosseini,

Delcroix,

Henao

et al. 2023

Sensors

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For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses.

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“…The same algorithm was used to disaggregate consumption based only on monthly cumulated values by comparison with disaggregated consumption of sample users equipped with appliance-level sub-metering [35]. Recently, Culière et al proposed a Bayesian model of temperature-conditioned electricity consumption that allows disaggregating the heating component from the electric load curve in an unsupervised manner, based on daily consumption detected by smart meters [36].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation

Massidda

Marrocu

2022

Sensors

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Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.

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Bayesian model of electrical heating disaggregation

Cited by 11 publications

References 6 publications

Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

Towards Feasible Solutions for Load Monitoring in Quebec Residences

A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation

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