A preliminary study towards conceptualization and implementation of a load learning model for smart automated demand response

Singh, Ajay Kumar; Vyas, Shashank; Kumar, Rajesh

doi:10.1109/poweri.2016.8077212

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Most of the studies [77], [78], [79], [80], [81], [82], [83], [84], [85], [86] focus on estimating demand for the next day or two, while others [87] look forward a week. Also, load forecasting has been done at many other aggregation levels, including for single-family homes [78], [88], [89], [90], commercial buildings [80], [83], [91] and individual appliances [92], [93] such as chillers, ice banks, and lights. The load forecasting for single consumer or a group of consumers for day ahead prediction depends on previous load profile and weather conditions as presented in [92] using ANN-based method for home load forecasting.…”

Section: ) Load Forecastingmentioning

confidence: 99%

“…Technique of conveying messages. Recent advances in communication technology [89] have made it possible for modern SGs to transfer data and information swiftly and reliably in both directions. To increase power dependability and quality and stop electrical blackouts, DR's marketing and emergency signals may be sent utilizing a two-way communication system (wireless, wire, GSM, and the internet).…”

Section: Blockchain Based Demand Response In Smart Gridmentioning

confidence: 99%

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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 Forecastingmentioning

confidence: 99%

Section: Blockchain Based Demand Response In Smart Gridmentioning

confidence: 99%

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

et al. 2023

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“…The feed-forward ANN, which has only one hidden layer, is the most widely employed model in the NDR domain. In addition, auto regressive feed-forward models are used in a variety of contexts [114]. A convolutional neural network and an Elman neural network were the only two RNNs that could discover, both in works by [115] and [116] (nonlinear auto regressive with external inputs, RNN).…”

Section: ) Single Hidden Layer Annmentioning

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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Blockchain-based management of demand response in electric energy grids: A systematic review

et al. 2023

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A preliminary study towards conceptualization and implementation of a load learning model for smart automated demand response

Cited by 6 publications

References 4 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

Blockchain-based management of demand response in electric energy grids: A systematic review

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