Data Intelligence for Renewable Energy Systems

By Energy CIO Insights | Monday, November 19, 2018

Electric power systems are gaining drastic advances during the implementation of information and communication networks. Numerous integrated measurement devices are developed and implemented as distributed energy resources, monitoring systems, smart energy meters, and synchronization systems to achieve high data efficiency along with better processing speed. However, there is still a question about today’s power system data on how the structures and tools implemented can be integrated with big data technologies.

Several advanced tools such as big data analytics, artificial intelligence, and machine learning are deployed in real-time scenarios to interpret the past data, capture the present, and predict the future with efficient and optimal results. In the energy sector, the primary concern is to enhance the rate of energy production, consumption, and achieve energy balance in the grid in an efficient manner.

With a fast-growing economy, the requirement of electrical energy is also increasing, and it may reach the extent of three to four times of daily average energy consumption. Renewable energy can be an option to meet the daily requirements. It is also a better strategy to reduce carbon emissions, eco-friendly and to achieve a sustainable environment for the future.

Since 2015, AI technologies have been introduced in almost all the business and industrial sectors.  Numerous machine-learning techniques have been developed, which is found to be an integral part of AI. Integrating AI technologies with machine learning help achieve effective results to boost the performance of the employees, along with guidance to meet the maximum peak demands with proper load shedding.

Smart grids development with AI and big data ensures efficient usage and storage of renewable energy.  Furthermore, the development of machine learning-based predictive algorithms allows to analyze the demand management of small-scale as well as large-scale producers. Other metrics such as optimal utilization, excellent customer experience along with equipment efficiency helps to meet asset management needs and customer demands in an effective manner.

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