To benefit from big data and analytics, utility companies need to regularly invest in enterprise analytics, grid analytic, and consumer analytics to obtain insights from the data to make intelligent decisions and tackle the issues this industry faces constantly.
Fremont, CA: The energy and utility industry is transforming significantly with the implementation of technologies like the predictive analysis. Grids are becoming smarter, electric power sources are getting cleaners, and customers have more options to receive power with big data and analytics.
Big data are large data sets that comprise of structured and unstructured data. Companies use these data sets by analyzing for insights to make informed decisions and predictions and achieve strategic business objectives.
Analytics utilizes different methods such as statistics, predictive modeling and analysis, mathematics, and machine learning to find meaningful patterns in vast volumes of data sets.
Energy and utility companies implement smart technology like cloud computing technologies, wireless, network communication, and more, which generates a vast volume of data sets regularly and gets collected over time.
There are different sources of Big Data in an energy and utility company such as weather data, GIS data, smart meters, storm data, asset management data, among others. These data are used to improve operational efficiencies, lower costs, reduce carbon emissions, and handle energy demand for end consumers.
Dynamic Energy Management in Smart Grids
Big data and analytics allow dynamic energy management in smart grids. It provides a two-way flow of data and power between consumers and suppliers. This enhances reliability, energy efficiency, and power sustainability enable energy consumers and energy producers to participate in a more active role in the electricity market. Dynamic power management relies on load predicting and renewables productions, requiring intelligent approaches and solutions. Thus, efficient data network management, powerful data analytics, high-performance computing, and cloud computing technology are vital for improved smart grid operation.
Model Failure Probability
The machine learning algorithm can predict the failure model of a distribution feeder element to optimize the quality of service and minimize operational cost on maintenance.