A review of machine learning for new generation smart dispatch in power systems

Yin, Linfei; Gao, Qi; Zhao, Lulin; Zhang, Bin; Wang, Tao; Li, Shengyuan; Liu, Hui

doi:10.1016/j.engappai.2019.103372

Cited by 62 publications

(24 citation statements)

References 112 publications

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“…The Energy Management System (EMS) is crucial in controlling the ratio of the amount of power needed that is generated from the renewables to that from the non-renewables all around the clock and EMS plays a key role in dispatching the power based on the demand and power generation ( Ajeigbe, Munda, & Hamam, 2020 ). The role of digitalization, powered by AI, ML, and deep-learning algorithms can effectively forecast the load demand for the given model with societal energy data together with many influencing factors such as seasonal changes ( de Queiroz et al, 2019 ) and, accordingly, can optimize the generation plants output ( Yin et al, 2020 ). The actual load demand is also fed to the system as feedback data.…”

Section: Technology – a Weapon To End The Pandemicmentioning

confidence: 99%

A hover view over effectual approaches on pandemic management for sustainable cities – The endowment of prospective technologies with revitalization strategies

Elavarasan

Pugazhendhi

Shafiullah

et al. 2021

Sustainable Cities and Society

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Section: Technology – a Weapon To End The Pandemicmentioning

confidence: 99%

A hover view over effectual approaches on pandemic management for sustainable cities – The endowment of prospective technologies with revitalization strategies

Elavarasan

Pugazhendhi

Shafiullah

et al. 2021

Sustainable Cities and Society

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“…Several milestones have been reached in presenting Machine Learning (ML) techniques for various sub-areas in SG [11]. However, shallow neural networks and sample ML models pose many challenges that make them seldom employed for complex problems in EPSs [12], [13].…”

Section: B Emergence Of Deep Learningmentioning

confidence: 99%

“…Furthermore, the existing body of knowledge reported so far in availability published papers lacks a critical standpoint overview for the recent methodologies that perfectly tailor DL to EPSs such as distributed DL models and edge intelligence. Pioneering relevant review articles for DL & SG applications are reported in Table 1 [1], [10], [11], [13], [21], [23].…”

Section: Related Work and Motivationmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

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“…Mobile monitoring devices have many research challenges aimed at ultra-low power consumption demands [19][20][21][22]. To address these constraints, there is a significant need for smart software control algorithms using machine learning principles for automated and intelligent device management [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Prauzek¹,

Paterova²,

Konecny³

et al. 2022

Computers, Materials &Amp; Continua

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Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices. The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values, leading to effective operation of power-aware systems. The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm. The root mean square error (RMSE) and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria (the performance score parameter and normalized geometric distance) were used for overall evaluation and comparison. The experiments showed that the novel DDSL method reaches significantly lower RMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm. The overall criteria performance score is 40% higher than the reference algorithm base on static confirmation settings.

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A review of machine learning for new generation smart dispatch in power systems

Cited by 62 publications

References 112 publications

A hover view over effectual approaches on pandemic management for sustainable cities – The endowment of prospective technologies with revitalization strategies

A hover view over effectual approaches on pandemic management for sustainable cities – The endowment of prospective technologies with revitalization strategies

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

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