Smart Grid Consumer Behavioral Model using Machine Learning

Varghese, Antos C.; Padmini, V.; Kumar, G.; Khaparde, S.A.

doi:10.1109/isgt-asia.2018.8467824

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…The produced groups of consumers are created in the study literature to carry out various tasks in the DR context. A significant portion of the evaluated works classify customers to find potential participants for DR programs [77], [169], [170], [171], [172] and identify the best group of consumers who are already enrolled in DR programs to contact in order to reduce demand during DR programs [173], [174]. According to [64], load profiles can be used to extract socio-demographic data, and the characteristics of these consumers can be used to choose potential DR participants.…”

Section: Load/customer Segmentationmentioning

confidence: 99%

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

et al. 2023

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Demand Response (DR) has gained popularity in recent years as a practical strategy to increase the sustainability of energy systems while reducing associated costs. Despite this, Artificial Intelligence (AI) and Machine Learning (ML), have recently developed as critical technologies for demand-side management and response due to the high complexity of tasks associated with DR, as well as huge amount of data management to take decisions very near to real time implications. Selecting the best group of users to respond, learning their attitude toward consumptions and their priorities, price optimization, monitoring and control of devices, learning to engage more and more consumers in the DR schemes, and learning how to remunerate them fairly and economically are all problems that can be tackled with the help of AI techniques. This study presents an overview of AI approaches used for DR applications. Both the Artificial Intelligence and Machine Learning algorithm(s) are employed while discussing commercial efforts (from both new and existing businesses) and large-scale innovation projects that have applied AI technologies for energy DR. Different kind of DR programs implemented in different countries are also discussed. Moreover, it also discusses the application of blockchain for DR schemes in smart grid paradigm. Discussion of the strengths and weaknesses of the evaluated AI methods for various DR tasks, as well as suggestions for further study, round out the work.INDEX TERMS Artificial intelligence, blockchain, demand response, demand side management, demand response, Internet of Things (IoT), smart grids, machine learning.

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Section: Load/customer Segmentationmentioning

confidence: 99%

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

et al. 2023

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“…In addition, for segmentation of customer and load, grouping electricity users into distinct categories is a major use case for DR. Service providers may use it to help build disaster recovery programmes, aggregate resources, and analyze the potential burden of participating in multiple DR programmes. Based on their load profiles, customers are categorized into demand response schemes [136] [137] [138] [139]. Peak loads of [98], the average load of five consecutive weekdays [71], and specified factors such the mean relative standard deviation and seasonal score.…”

Section: Issuesmentioning

confidence: 99%

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

et al. 2023

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In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demandside management solutions is also illustrated congruently.

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“…In smart grids, customers may also generate and store energy for better energy management (e.g., demand peak and overall cost reduction [38]). In this context, state-of-the-art works propose different methods to describe and optimize smart grid customers' energy management (e.g., mathematical models for optimal load shifting [39] or machine learning behavioral models [40]). These models integrate pricing data to enhance the consumers' energy consumption.…”

Section: Smart Gridsmentioning

confidence: 99%

Modeling and simulation of smart grid-aware edge computing federations

et al. 2022

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Compute-intensive Internet of Things (IoTs) applications have led to the edge computing paradigm. Edge computing decentralizes the IT infrastructure in multiple edge data centers (EDCs) across the access networks to reduce latency and network congestion. Edge computing can benefit significantly from different aspects of smart grids to achieve lower energy consumption and greater resilience to electricity price fluctuations. This paper presents a modeling, simulation, and optimization (M&S&O) framework for analyzing and dimensioning smart grid-aware edge computing federations. This tool integrates aspects of a consumer-centric smart grid model to the resource management policies of the EDCs. To illustrate the benefits of this tool, we show a realistic case study for optimizing the energy consumption and operational expenses of an edge computing federation that provides service to a driver assistance IoT application. Results show that this approach can reduce the daily energy consumption by 20.3% and the electricity budget by 30.3%.

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Smart Grid Consumer Behavioral Model using Machine Learning

Cited by 11 publications

References 12 publications

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

Modeling and simulation of smart grid-aware edge computing federations

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