State of the art in big data applications in microgrid: A review

Moharm, Karim

doi:10.1016/j.aei.2019.100945

Cited by 68 publications

(32 citation statements)

References 57 publications

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“…In such cases, various circumstances can be taken into account, for example, when insuring a car, the risks of theft or accident can be calculated, and Big Data technology takes into account many factors, from the identity of the owner to the quality of roads on which the car owner is likely to drive. Such insurance is already used in Western Europe and the United States and is defi ned as " usage-based insurance" [9]. When insuring life and health insurance companies can use data from "smart wristbands" that take into account motor activity, sleep quality, measure heart rate, pressure, number of steps.…”

Section: Resultsmentioning

confidence: 99%

Problems of personal data protection when using Big Data technologies

Владимировна¹,

Anatolyevna²

2020

J Appl Eng Science

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The study analyzes Big Data technologies and considers the role, features and signifi cance of these technologies. The relevance of the research is important because of the large-scale use of Big Data technologies in many areas and the weak legal regulation of the employment of Big Data when using personal data. The purpose of this study is to determine the legal status of Big Data technology, differentiate the concepts of "personal data" and "Big Data technology" and offer practical recommendations on the use of this technology. The methodological basis of the research is legal, statistical, private scientifi c methods, as well as the methods of case analysis and "snowball". Based on the results of the study, the authors defi ne the "Big Data" technology and "personal data in electronic form". The principles for using Big Data technologies that can be applied in the international legal acts and judicial practice were developed. They are: the legality of collecting personal data in electronic form; electronic data processing in limited ways; prohibition of combining various databases with personal information in electronic form; limited period of storage of personal data in electronic form; free use of information; commercial usage. It is proposed to classify personal information provided electronically on the Internet by citizens into the following categories: personal information open to public; special information on actions; biometric data; confi dential personal information.

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Section: Resultsmentioning

confidence: 99%

Problems of personal data protection when using Big Data technologies

Владимировна¹,

Anatolyevna²

2020

J Appl Eng Science

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“…Also, there are several other issues to be addressed in big data technology, like data security and privacy. 60 Artificial intelligence (AI) can be combined with big data technology to solve complex problems in smart energy management and forecasting. A hybrid model that combines physical and statistical methods can be used to improve efficiency.…”

Section: Big Data For Energy Forecastingmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An indepth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

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“…w P is the output of the w -th typhoon power station, w P is the predicted output, w C is the unit wind abandonment cost, W is the number of wind farms. 4 1…”

Section: A Economic Dispatching Model Of Microgridmentioning

confidence: 99%

“…In recent years, as a kind of comprehensive integrated lowvoltage and medium-voltage power distribution system including renewable energy and other distributed power sources, microgrid has received extensive attention [4]. Therefore, it is of great significance to study the economic scheduling problem of microgrid [5].…”

Section: Introductionmentioning

confidence: 99%

Decentralized Economic Dispatching of Multi-Micro Grid Considering Wind Power and Photovoltaic Output Uncertainty

Zhang

Wenbo

2021

IEEE Access

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In this paper, the decentralized economic scheduling problem of multi-micro grid is studied on the basis of considering the uncertainty of wind power and photovoltaic output. Firstly, in order to meet the independent and autonomous requirements of decentralized scheduling, the decoupling method of public connection points, which is more suitable for the multi-micro grid structure, is adopted to partition the multimicro grid regions. Then, considering the uncertainty of wind-wind output, a decentralized economic dispatching model of multi-micro grid with robust optimization participation is proposed to minimize the economic cost. Finally, the improved Alternating direction method of multipliers is used to solve the decentralized economic scheduling problem of multi-micro grid. The feasibility and superiority of the improved algorithm are verified by simulation test and comparative analysis. The decentralized autonomy of multi-micro grid scheduling can be realized, and the random uncertainty of landscape can be effectively dealt with.INDEX TERMS Alternating direction method of multipliers (ADMM), decentralized economic scheduling, multi-micro grid, wind power generation.

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State of the art in big data applications in microgrid: A review

Cited by 68 publications

References 57 publications

Problems of personal data protection when using Big Data technologies

Problems of personal data protection when using Big Data technologies

A holistic review on energy forecasting using big data and deep learning models

Decentralized Economic Dispatching of Multi-Micro Grid Considering Wind Power and Photovoltaic Output Uncertainty

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