How Big Data and Analytics Impact Energy and Utilities?
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How Big Data and Analytics Impact Energy and Utilities?

By Energy CIO Insights | Wednesday, November 20, 2019

With surging energy demands, energy and utility companies have to evolve. Big data and analytics are playing a pivotal role in the transformation.

Fremont, CA: The traditional energy and utility industry typically comprise of power plants generating electricity transmitted over long-distance to commercial or residential complexes. However, the energy and utility industry is going through a transformation with technologies such as predictive analytics, which is making grids smarter. Power generating sources are getting cleaner, and the customers have more than one option to receive power. The emergence of Big Data and analytics play a pivotal role in such developments. 

Big data consists of large volumes of structured and unstructured data, which leads to insights and informed decision making. On the other hand, analytics uses various techniques like mathematics, statistics, predictive modeling, predictive analysis, and machine learning to determine patterns in large sets of data. Energy and utility companies use sensors, cloud computing technologies, power planning, and network communication. The amalgam of these technologies produces petabytes of data every hour from millions of households. With the increased use of intelligent devices such as sensors and thermostats, a large volume of data gets generated from power generation to customer consumption via substations.

Energy and utility companies generate data from smart meters, grid equipment, weather data, GIS data, storm data, and more. Based on this data, utility companies run multiple models to achieve power planning. Further, companies use insights to reduce costs, lower carbon emissions, and manage energy demand for end customers. Big data analytics assists in the accurate forecast of energy consumption, which affects power generation and, ultimately, pricing. The energy forecast affects power generation from renewable energy sources as they are based on changing weather conditions. Predictive analysis handles it from the data taken from weather systems.

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Smart grids allow a two-way flow of data and power between consumers and suppliers, and big data and analytics enable dynamic energy management in smart grids. This optimizes power in terms of energy efficiency, power sustainability, and reliability. Load forecasting and production of renewable dictate effective dynamic power management. Thus, the energy landscape requires intelligent methods and solutions such as machine algorithms for the analysis of a large amount of data gathered by smart meters. Therefore, for optimized smart grid operation, robust data analytics, efficient data network management, cloud computing, and high-performance computing are pivotal. The energy and utility industry is evolving, and with time, big data and analytics will be an integral part of it.

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