energycioinsights

Energy Utility Industry and Predictive Analytics

By Energy CIO Insights | Friday, November 16, 2018

Predictive analytics demand a successful strategy of design, creation, validation, and implementation of different applications in the industries. Now there is a need for predictive analytics to converge with energy demands and goals. Advancements in data analytics and connectivity are enabling a range of new digital applications particularly digitalized energy systems. These applications identify the needs of energy and deliver them at the right time. This will result in an impact of tremendous digital advances and rapid deployment across the energy landscapes. The pace of digitalization in energy is rapidly increasing. Energy companies are highly investing in digital technologies.

Amount of data collected daily on the supply and demand is quite staggering. Due to advancements in the technologies, the power giants are holding themselves on a gold mine of data. When this data is analyzed, the energy  demands are evaluated and the data is stored for the needs of the next generation. Data is collected or created during the complete process of power generation, transmission, and distribution. The energy sector is harnessing the power of data through big data analytics that optimizes power generation and improves the planning strategy. Power generation planning and economic load management are the two most crucial decision-making processes in the power industries. Balancing the energy supply and demand has become the toughest challenge. To overcome this challenge, analytics plays a crucial role. Many energy companies are utilizing the advantages of the collected data and leveraging the predictive analytics technologies to escalate the distribution of power and reducing production costs.

Predictive analytic models are not only deployed to non-renewable sources of energy but also in maneuvering wind power and solar systems to attain larger outputs significantly. With data analytics, renewable energy power generation has the highest potential of being more efficient and accurate. Geographical data of predictive analytics includes geographic information from satellite points that aid spatial planning (influencing the distribution of people and activities in spaces of different scales). Analysis of topography, location, and solar irradiation can improve the scope of power management. Data analytics techniques manage the process of energy demand. This process is essential as it is the core of energy consumption from an industry or a retail store. Many of the advancements that emerged recently stood on the demand side in the energy sector. Soon, energy efficiency will play a crucial role in reducing global greenhouse emissions. Aggregating both big data and predictive analytics might facilitate cheap energy at a point of time. This concept of less expensive energy starts with the empowerment of customers by saving the unused power and selling to the grid, to recycle energy itself. This is a probable reality to be witnessed soon.

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