<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Smart grid (SG) is the solution to solve existing problems of energy security from generation to utilization. Examples of such problems are disruptions in the electric grid and disturbances in the transmission. SG is a premium source of Big Data. The data should be processed to reveal hidden patterns and secret correlations to extrapolate the needed values. Such useful information obtained by the so-called data analytics is an essential element for energy management and control decision towards improving energy security, efficiency, and decreasing costs of energy use. For that reason, different techniques have been developed to process Big Data. This paper presents an overview of these techniques and discusses their advantages and challenges. The contribution of this paper is building a recommender system using different techniques to overcome the most obstacles encountering the Big Data processes in SG. The proposed system achieves the goals of the future SG by (i) analyzing data and executing values as accurately as possible, (ii) helping in decision-making to improve the efficiency of the grid, (iii) reducing cost and time, (iv) managing operating parameters, (v) allowing predicting and preventing equipment failures, and (vi) increasing customer satisfaction. Big Data process enables benefits that were never achieved for the SG application.</p></abstract>

Smart grid (SG) is the solution to solve existing problems of energy security from generation to utilization. Examples of such problems are disruptions in the electric grid and disturbances in the transmission. SG is a premium source of Big Data. The data should be processed to reveal hidden patterns and secret correlations to extrapolate the needed values. Such useful information obtained by the so-called data analytics is an essential element for energy management and control decision towards improving energy security, efficiency, and decreasing costs of energy use. For that reason, different techniques have been developed to process Big Data. This paper presents an overview of these techniques and discusses their advantages and challenges. The contribution of this paper is building a recommender system using different techniques to overcome the most obstacles encountering the Big Data processes in SG. The proposed system achieves the goals of the future SG by (i) analyzing data and executing values as accurately as possible, (ii) helping in decision-making to improve the efficiency of the grid, (iii) reducing cost and time, (iv) managing operating parameters, (v) allowing predicting and preventing equipment failures, and (vi) increasing customer satisfaction. Big Data process enables benefits that were never achieved for the SG application.

A Review on Big Data Management and Decision-Making in Smart Grid

Electrical and Computer Engineering Department

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Review on Big Data Management and Decision-Making in Smart Grid"}

Smart grid (SG) is the solution to solve existing problems of energy security from generation to utilization. Examples of such problems are disruptions in the...

A Review on Big Data Management and Decision-Making in Smart Grid

Published Online: Nov 26, 2019

Page range: 1 - 13

Received: May 08, 2019

Accepted: Jun 26, 2019

DOI: https://doi.org/10.2478/pead-2019-0011

Keywords
Big Data, energy management, Big Data analytics, smart grid, decision-making

© 2019 Amira Mohamed et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Review on Big Data Management and Decision-Making in Smart Grid

Published Online: Nov 26, 2019

Page range: 1 - 13

Received: May 08, 2019

Accepted: Jun 26, 2019

DOI: https://doi.org/10.2478/pead-2019-0011

KeywordsBig Data, energy management, Big Data analytics, smart grid, decision-making

© 2019 Amira Mohamed et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Big Data, energy management, Big Data analytics, smart grid, decision-making