Electric power systems are getting drastic advances during the implementation of information and communication networks. Several integrated measurement devices are developed and implemented as distributed energy resources, monitoring systems, smart energy meters, and synchronization systems to attain high data efficiency and better processing speed. There is still a question about today's power system data on how the structures and tools applied 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 energy production and consumption efficiently and achieve energy balance in the grid.
With a fast-growing economy, the requirement for 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 daily requirements. It is also a better strategy to reduce carbon emissions, be eco-friendly, and achieve a sustainable environment for the future.
Since 2015, AI technologies have been initiated in almost all the business and industrial sectors. Numerous machine-learning techniques have been developed, an integral part of AI. Incorporating AI technologies with machine learning support achieves effective results to boost the employees' performance and guidance to meet the maximum peak demands with proper load shedding.
Smart grid development with AI and big data ensures efficient usage and storage of renewable energy. Moreover, the development of machine learning-based predictive algorithms allows for analyzing the demand management of small-scale and large-scale producers. Other metrics like optimal utilization, wonderful customer experience, and equipment efficiency help effectively meet asset management needs and customer demands.
