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Internet of Things and big data recommender systems to support Smart Grid

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Since its appearance, the Internet of Things (IoT) has completely revolutionized almost all aspects of our lives. Among present and potential numerous and diverse applications of IoT, its utilization in the energy sector is of particular interest. The IoT inclusion in the power industry and Smart Grid (SG) evolution opens a whole world of high-potential opportunities to optimize the grid operation. The realization of SGs utilizing smart metering technology or advanced metering infrastructure with bidirectional IoT-based communication between demand and utility could improve existing energy balancing procedures. Keeping energy consumption and supply in balance with minimal operating costs and optimal grid conditions is not an easy task, especially in presence of renewable energy sources. As the IoT is established on the utilization of a large number of smart things/devices that generate a prodigious amount of data on a daily basis, successfully managing big data represents a key issue. In order to obtain valuable insights and knowledge from data gathered, the appliance of big data analytics is demanded. Hence, effective analysis and utilization of a massive amount of diversity of data that arrive at high speed and can be of uncertain provenance are mandatory in the process of obtaining valuable insights and enable the creation of knowledge-based recommender systems. Big data analytics applied to data gathered from smart meters could be used to make valuable recommendations regarding consumption prediction, demand response and management programs, voltage and frequency control, state estimation, and power quality. The overall operation of SG could be certainly optimized in various aspects by using large-scale near real-time measurements. The general aim of this chapter is to provide an overview of ongoing scientific research, recent technological innovations and breakthroughs, and big data analytics role in making recommendation systems that will facilitate the development and evolution of future global energy systems.

Chapter Contents:

• 9.1 Introduction
• 9.2 IoT-supported SG—a communication perspective
• 9.3 Big data in SG
• 9.4 Making recommendations in SG
• 9.4.2 Renewable energy forecasting
• 9.4.3 DR and energy management program
• 9.4.3.1 Home demand management
• 9.4.4 SG state estimation
• 9.4.4.1 Communication architectures for PMU-based DSSE
• 9.4.4.2 Big data of dynamic Cloud- based DSSE
• 9.4.4.3 Application benefits of dynamic DSSE
• 9.5 Conclusion
• References

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