A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications

Rajabi, Amin; Eskandari, Mohsen; Ghadi, Mojtaba Jabbari; Ghavidel, Sahand; Li, Li; Zhang, Jiangfeng; Siano, Pierluigi

doi:10.1016/j.enbuild.2019.109455

Cited by 45 publications

(16 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A method proposed by [Liang et al, 2019] to detect power constantly consumed by some appliances that are never turned off [baseload] segments from daily consumption profiles using Sliding Window Linear Regression and shown the high accuracy performance of the proposed method and its adaptiveness to the heterogeneity in energy consumptions across customers also discussed how the projected method can be employed to classify customers with high baseload energy saving capacities and low effectual refrigerator-freezer. A combination of Symbolic aggregate approximation technique as a data size reduction process and hierarchical clustering algorithm employed to find daily load curve of customers and segregated into certain clusters based on similarity [Rajabi et al, 2019]. [Rinku and Sohil, 2018] have presented a literature survey for algorithms and technologies used in smart electricity meter data analytics which focuses on electricity consumption usage, pattern, profile and load forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Consumption Patterns Using Som-Based Twolevel Clustering of Residential Households

Chavda¹,

Pandya²,

Kotwal³

2022

INDJCSE

View full text Add to dashboard Cite

Electricity consumption in residential households' accounts for a huge share of energy use, which encourages researchers, policy makers towards energy conservation of residential household. The goal of Clustering using Unsupervised Machine Learning algorithms is to find likenesses in the data point of D-EC and cluster similar data points of D-EC together. This paper emphasis on application of machine learning techniques for pattern identification with the implementation of dimension reduction technique Self-Organizing Map algorithm trailed by unsupervised clustering algorithms such as K-means, Gaussian Mixture clustering, MiniBatchKMeans, Agglomerative Clustering, Spectral Clustering on D-EC data of electrical usage. Consumers classified into 4 clusters and labelled as per the usage pattern, also identified as a consistent and non-consistent consumer based on their electricity usage. A proposed methodology concluded using SOM followed by Kmeans algorithm which helpful for awareness and alerts to consumers based on consumption patterns participates in demand-supply decision making with timely recommendation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Electricity Consumption Patterns Using Som-Based Twolevel Clustering of Residential Households

Chavda¹,

Pandya²,

Kotwal³

2022

INDJCSE

View full text Add to dashboard Cite

show abstract

“…Ryu et al (2016) investigated suitable customers for demand response management and pattern recognition modelling. Rajabi et al (2019) used hierarchical algorithm to cluster 27,900 daily load curves of residential dwellings. The 30 clusters patterns are visible such as morning peak, mid-day peak, late night peak, night peak and dual peak and stability of load curve identified.…”

Section: Literature Surveymentioning

confidence: 99%

“…The clustering performance of the proposed method is evaluated by diverse clustering validity indices including Davies-Bouldin index (DB), Dunn validity index (DVI) and silhouette width criterion (SWC) (Rajabi et al (2019). Table 2 shows the clustering performance comparison of the proposed algorithm with the predefined clustering algorithm on the same data.…”

Section: Model Validationmentioning

confidence: 99%

Unleashing analytics to reduce electricity consumption using incremental clustering algorithm

Chaudhari

Mulay

2021

IJESM

View full text Add to dashboard Cite

Purpose To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch. Design/methodology/approach This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering. Findings Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors. Originality/value ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.

show abstract

“…In recent years, with the rapid increase in the share of renewable energy, a new need for demand flexibility program (DFP) as a solution to rapid oversupply or peak load has been emphasized. For example, DFP can mitigate spikes in demand caused by a sharp drop in PV generation that usually occurs during the evening hours [8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach

2020

View full text Add to dashboard Cite

The rapid increase in renewable energy resources has resulted in the increasing need for a demand flexibility program (DFP) from industrial load resources as a solution to oversupply and peak load spikes. However, to reasonably estimate the DR potential flexibility, the load characteristics must be analyzed and potential assessment formulas must be validated. Thus, in this study, a novel method is proposed to evaluate the DR potential flexibility of industrial loads according to a process of related load-characteristic data analysis. The proposed potential-estimation model considers frequency, consistency, and DR event operation scores during designated ramp-up and ramp-down time intervals separately. A case study was conducted by considering typical cement industry process with actual power-consumption data analysis for demonstrating the test system. The results confirm that load reduction of more than half of the usual power consumption is possible if a potential score is about 0.27 in cement industry cases. Thus, the proposed method can be used as an indicator to determine how an industrial load is adequate for obtaining a DFP while suggesting meaningful implications through industrial load-resource data analysis.

show abstract

A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications

Cited by 45 publications

References 40 publications

Electricity Consumption Patterns Using Som-Based Twolevel Clustering of Residential Households

Electricity Consumption Patterns Using Som-Based Twolevel Clustering of Residential Households

Unleashing analytics to reduce electricity consumption using incremental clustering algorithm

Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach

Contact Info

Product

Resources

About