A Cluster-Based Method for Calculating Baselines for Residential Loads

Zhang, Yi; Chen, Weiwei; Xu, Rui; Black, Jason

doi:10.1109/tsg.2015.2463755

Cited by 85 publications

(46 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, a smoothing spline is fitted to create the baseline. A similar approach, using the consumption of non-DR participants to estimate the baseline of DR participants is proposed in [33] and in [34]. The problem with this practical approach is that always a reference population with similar characteristics needs to exist, which does not participate in DR activations.…”

Section: Baselinesmentioning

confidence: 99%

Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data

et al. 2019

View full text Add to dashboard Cite

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract

Section: Baselinesmentioning

confidence: 99%

Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The data cleaning includes discontinuous data completion and abnormal data correction. Unlike the general studies [7,12], since the data used in clustering are loads of each customer instead of a group of customers, we do not need normalization here.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The data-mining model for CBL calculation is based on clustering. Clustering is a useful tool for data analysis and it has been widely used in the study of the modern power system, such as electrical consumption analysis [7] and electrical load prediction [16,17]. Various clustering algorithms have been studied in previous work, including hierarchical clustering, affinity propagation clustering, and fuzzy c-means clustering.…”

Section: Data-mining Approach Based On Clustering Analysismentioning

confidence: 99%

“…In order to ensure the benefits of both sides in DR programs, it is necessary to calculate customer baseline load (CBL). CBL attempts to predict what customer load would have been under 'normal' (non-DR event) circumstances, so that the amount of customer load reduction could be obtained [6,7]. A properly designed baseline methodology is perhaps the most important determinant of the success of any DR program-it enables grid operators and utilities to measure the performance of DR resources [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer

Song

Zhang

et al. 2018

Energies

View full text Add to dashboard Cite

Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified.

show abstract

“…Knowledge of the current DR value will enable the system operator to calculate the economic dispatch [32] solution while maintaining minimum energy reserve services. Further, through the predicted DR profile (one day ahead), unit commitment can be planned appropriately [28,[33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive load monitoring under residential solar power influx

et al. 2017

View full text Add to dashboard Cite

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) -USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day's total load that can be shed. * Corresponding AuthorEmail addresses: hchintha@sfu.ca; dineshhgcp@gmail.com (Chinthaka Dinesh), shirantha.welikala.2015@IEEE.org; shirantha27@gmail.com (Shirantha Welikala), yasithashehanliyanage@gmail.com; wlysliyanage@IEEE.org (Yasitha Liyanage), mpb.ekanayake@ee.pdn.ac.lk (Mervyn Parakrama B. Ekanayake), roshangodd@ee.pdn.ac.lk (Roshan Indika Godaliyadda), jbe@ee.pdn.ac.lk; EkanayakeJ@cardiff.ac.uk (Janaka Ekanayake) Proposed

show abstract

A Cluster-Based Method for Calculating Baselines for Residential Loads

Cited by 85 publications

References 16 publications

Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data

Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data

A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer

Non-intrusive load monitoring under residential solar power influx

Contact Info

Product

Resources

About