Abstract
Data analytics is essential in today's development systems. Because of the growing demand for sustainable energy, Smart Grids have emerged as an innovative approach to power generation, transmission, and distribution that integrate communication, control, and data analytics technologies to deliver efficient, reliable, and secure energy. Data analytics is critical in the Smart Grids ecosystem because it provides insights into energy consumption patterns, predicts energy demand, identifies faults, and optimizes power distribution. These book chapter overviews data analytics techniques used in Smart Grids applications. It covers many topics for Smart Grids applications, such as data acquisition and pre-processing, data mining (DM), machine learning (ML), and deep learning (DL) techniques. The first section of the chapter introduces Smart Grids and the role of data analytics in Smart Grids applications. The second section focuses on Smart Grids data acquisition and pre-processing techniques, such as cleaning, normalization, and transformation. The third section discusses Smart Grid data mining techniques such as clustering, classification, and association rule mining. The fourth section examines machine learning techniques such as random forest (RF), decision trees (DT), K-Nearest Neighbor (KNN) and support vector machines (SVM) and their applications in Smart Grids. Deep learning (DL) techniques for Smart Grids applications, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), are covered in the fifth and final sections. The difficulties and advantages of using deep learning methods on the data from smart grids are also covered. Overall, this book chapter thoroughly examines the data analytics techniques used in Smart Grids applications, as well as their potential impact on the future of energy distribution. It is an invaluable resource for Smart Grids, data analytics, and sustainable energy researchers, practitioners, and students.
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Sekhar, J.C., Pratap, V.K., Veeranjaneyulu, R., Macherla, S., Manindhar, B., Rahman, S.Z. (2023). Data Analytics Techniques for Smart Grids Applications Using Machine Learning. In: Kumar Sharma, D., Sharma, R., Jeon, G., Kumar, R. (eds) Data Analytics for Smart Grids Applications—A Key to Smart City Development. Intelligent Systems Reference Library, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-031-46092-0_22
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DOI: https://doi.org/10.1007/978-3-031-46092-0_22
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