Individual power profile estimation of residential appliances using low frequency smart meter data

Dinesh, H. G. C. P.; Perera, Pramuditha; Godaliyadda, G. M. R. I.; Ekanayake, M. P. B.; Ekanayake, Janaka

doi:10.1109/iciinfs.2015.7399000

Cited by 13 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several works have been carried out in the literature to identify how appliances are used by building occupants. For instance, Dinesh et al [49] used a simplified version of the Mean Shift Algorithm introduced by Cheng [75] to find representative load signatures in their load disaggregation study. They employed a Non-Intrusive Load Monitoring approach for appliances power profile/signal estimation based on Bayesian Classification to track appliance status in a building.…”

Section: Determination Of Appliance Load Profile In Buildingsmentioning

confidence: 99%

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

2022

View full text Add to dashboard Cite

There are two primary ways to save energy within a building: (1) through improving building engineering structures and adopting efficient appliance ownership, and (2) through changing occupants’ energy-consuming behaviors. Unfortunately the second way suffers from many challenges and limitations. Occupant behavior is, indeed, a complex and multi-disciplinary concept depending on several human factors. Although its importance is recognized by the energy management community, it is often oversimplified and naively defined when used to study, analyze or model energy load. This paper aims at promoting the definition of occupant behavior as well as exploring the extent to which the latter is involved in research works, targeting directly or indirectly energy savings. Hence, in this work, we propose an overview of interdisciplinary research approaches that consider occupants’ energy-saving behaviors, while we present the big picture and evaluate how occupant behavior is defined, we also propose a categorization of the major works that consider energy-consuming occupant behavior. Our findings via a literature review methodology, based on a bibliometric study, reveal a growth of the number of research works involving occupant behavior to model load forecasting and household segmentation. We have equally identified a research trend showing an increasing interest in studying how to successfully change occupant behaviors towards energy saving.

show abstract

Section: Determination Of Appliance Load Profile In Buildingsmentioning

confidence: 99%

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

2022

View full text Add to dashboard Cite

show abstract

“…These generated sets of appliance specific PMFs and appliance combination specific PMFs form the appliance level signature database (ALSD) and the combination level signature databases (CLSD) respectively [47]. Furthermore, in order to perform the power level disaggregation according to [48] after the active appliance combination is identified, the power consumption levels of each and every appliance were studied and a power consumption level signature database (PCLSD) was constructed [48].…”

Section: Signature Database Constructionmentioning

confidence: 99%

“…It should be noted that accuracy of the power level disaggregation has also been increased by around two percentage points from using this strategy. Since both priori unbiased and biased NILM algorithms had used the same power breakdown technique in [48], this slight improvement should be due to the increased appliance combination identification accuracy achieved by the proposed NILM method. Although AET has been increased by around 30% due to the introduced priori biasing step in the proposed NILM method, still the AET is well inside 1s or 3s sampling periods.…”

Section: B Case Studymentioning

confidence: 99%

“…7 shows such prestored curves for the Refrigerator (RF). Now, these pre-constructed sets of profiles were used along with the proposed NILM method's present solution and the power level disaggregation solution [48], to predict the individual appliance power profiles ahead from the current time. Through aggregation of these results for appliances in a number of houses, the proposed total power demand forecasting technique was successfully validated.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting

Welikala

Dinesh

Ekanayake

et al. 2019

IEEE Trans. Smart Grid

139

View full text Add to dashboard Cite

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identification and forecasting. In the first stage, the AUPs of a given residence were learnt using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, five minutes ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique.

show abstract

Non-intrusive load monitoring using multi-label classification methods

Ding

Dick

2020

Electr Eng

View full text Add to dashboard Cite

Individual power profile estimation of residential appliances using low frequency smart meter data

Cited by 13 publications

References 8 publications

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting

Non-intrusive load monitoring using multi-label classification methods

Contact Info

Product

Resources

About