TY - GEN
T1 - The role of big data analytics in smart grid management
AU - Dhupia, Bhawna
AU - Usha Rani, M.
AU - Alameen, Abdalla
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - Data analytics is playing a vital role in the modern industrial era. Electricity is one of the industries that have adapted data analytics techniques to a great extent. The data collected in the smart grid through smart meters and other sensors installed is very huge. The processing of such a huge heterogeneous data is not possible without the use of big data analytics technique. Big data analytics and machine learning algorithms play a vital role in electricity transmission and distribution network for data collection, storage and analysis of the data, prediction for data forecasting, and maintenance of the system. These techniques can help to optimally deliver energy at a lower cost with high quality and can also improve the customer service as well as social welfare. This article will review the use of big data analysis techniques along with the machine learning for various applications that can be mapped for smart grid environment. This article will also discuss the various methods and algorithm to be used for the applications for smart grid. A comparative analysis will also be done to show the best methods and algorithm to be used for a particular application.
AB - Data analytics is playing a vital role in the modern industrial era. Electricity is one of the industries that have adapted data analytics techniques to a great extent. The data collected in the smart grid through smart meters and other sensors installed is very huge. The processing of such a huge heterogeneous data is not possible without the use of big data analytics technique. Big data analytics and machine learning algorithms play a vital role in electricity transmission and distribution network for data collection, storage and analysis of the data, prediction for data forecasting, and maintenance of the system. These techniques can help to optimally deliver energy at a lower cost with high quality and can also improve the customer service as well as social welfare. This article will review the use of big data analysis techniques along with the machine learning for various applications that can be mapped for smart grid environment. This article will also discuss the various methods and algorithm to be used for the applications for smart grid. A comparative analysis will also be done to show the best methods and algorithm to be used for a particular application.
KW - Applications in smart grid
KW - Data analytics algorithms and methods
KW - Machine learning
KW - Smart grid
UR - http://www.scopus.com/inward/record.url?scp=85079589171&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-0135-7_38
DO - 10.1007/978-981-15-0135-7_38
M3 - Conference contribution
AN - SCOPUS:85079589171
SN - 9789811501340
T3 - Advances in Intelligent Systems and Computing
SP - 403
EP - 412
BT - Emerging Research in Data Engineering Systems and Computer Communications - Proceedings of CCODE 2019
A2 - Venkata Krishna, P.
A2 - Obaidat, Mohammad S.
A2 - Obaidat, Mohammad S.
A2 - Obaidat, Mohammad S.
PB - Springer
T2 - 2nd International Conference on Computing, Communications and Data Engineering, CCODE 2019
Y2 - 1 February 2020 through 2 February 2020
ER -