Multi-granular electricity consumer load profiling for smart homes using a scalable big data algorithm

Bedingfield, Susan; Alahakoon, Damminda; Genegedera, Hiran; Chilamkurti, Naveen

doi:10.1016/j.scs.2018.04.006

Cited by 21 publications

(14 citation statements)

References 15 publications

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“…It is valuable to group the prosumers for better energy sharing outcomes. Despite the various clustering algorithms [109], most of them simply bundles prosumers with similar utility functions or load patterns which may not fully realize the potential of sharing. Especially, if the prosumers in a group are identical, sharing would not create much benefit.…”

Section: B Optimization Modelsmentioning

confidence: 99%

Peer-to-Peer Energy Sharing: A New Business Model Towards a Low-Carbon Future

Chen,

Zhao

2021

Preprint

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The development of distributed generation technology is endowing consumers the ability to produce energy and transforming them into "prosumers". This transformation shall improve energy efficiency and pave the way to a low-carbon future. However, it also exerts critical challenges on system operations, such as the wasted backups for volatile renewable generation and the difficulty to predict behavior of prosumers with conflicting interests and privacy concerns. An emerging business model to tackle these challenges is peer-to-peer energy sharing, whose concepts, structures, applications, models, and designs are thoroughly reviewed in this paper, with an outlook of future research to better realize its potentials.

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Section: B Optimization Modelsmentioning

confidence: 99%

Peer-to-Peer Energy Sharing: A New Business Model Towards a Low-Carbon Future

Chen,

Zhao

2021

Preprint

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Section: Big Data Analyticsmentioning

confidence: 99%

“…Research focuses on time-varying energy consumption data to generate consumption or load patterns identified as typical load profiles (Bedingfield et al, 2018). Typical load profiles are used for load forecasting, load estimation, load control, load disaggregation, abnormal electricity consumption detection, designing electricity tariff offers, developing market strategies, or demand-side response policy (Bedingfield et al, 2018). Also, transient stability analysis, electric device state estimation, power quality monitoring, topology identification, renewable energy forecasting, and non-technical loss detection are data analytics applications in big data (Zhang, Huang, and Bompard, 2018).…”

Section: Big Data Analyticsmentioning

confidence: 99%

Big Data in Smart Energy Systems: A Critical Review

Codal

Ari

Ilter

2020

AJIT-e

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Climate change is an undeniable fact. Considering that two-thirds of greenhouse gas emissions originate from the energy sector, it is expected that the world's energy system will be transformed with renewable energy sources. Energy efficiency will be continuously increased. Reducing energy-related carbon dioxide emissions is the heart of the energy transition. Big data in energy systems play a crucial role in evaluating the adaptive capacity and investing more smartly to manage energy demand and supply. Indeed, the impact of the smart energy grid and meters on smart energy systems provide and assist decision-makers in transforming energy production, consumption, and communities. This study reviews the literature for aligning big data and smart energy systems and criticized according to regional perspective, period, disciplines, big data characteristics, and used data analytics. The critical review has been categorized into present themes. The results address issues, including scientific studies using data analysis techniques that take into account the characteristics of big data in the smart energy literature and the future of smart energy approaches. The manuscripts on big data in smart energy systems are a promising issue, albeit it is essential to expand subjects through comprehensive interdisciplinary studies

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“…Wang et al [7] present a systematic review on the research and development status of the residential tiered electricity price policy in China. By using the electricity consumption data of smart meters for 10,000 Australian households for a year, Bedingfield et al [22] present a new adaptable and scalable algorithm to understand electricity usage behaviors and provide customized electricity billing. Rathod et al [23] carry out a K-means clustering algorithm on data from 20,000 consumer meters in the city of Sangli to form different clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

et al. 2019

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With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackoutsensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers. INDEX TERMS Blackout sensitivity, big data, decision tree, electricity market, Internet of Things, longtailed, Pareto effect. NOMENCLATURE Notations Description Y The dependent variable, or target variable. It can be ordinal categorical, nominal categorical or continuous. If Y is categorical with J classes, its class takes values in C = {1,. .. , }.

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Multi-granular electricity consumer load profiling for smart homes using a scalable big data algorithm

Cited by 21 publications

References 15 publications

Peer-to-Peer Energy Sharing: A New Business Model Towards a Low-Carbon Future

Peer-to-Peer Energy Sharing: A New Business Model Towards a Low-Carbon Future

Big Data in Smart Energy Systems: A Critical Review

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

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