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Energy transition: how to gain a competitive advantage with data analytics

As the total share of sustainable energy sources is increasing, the total supply is becoming more and more volatile, increasing the cost significantly. This volatility and subsequent peak loads increases the stress on the energy network, consequently increasing the cost of maintenance.

Possible AI-powered Applications for the Utilities industry

Predicting the energy demand

With higher precision and at shorter time interval it is possible to increase energy efficiency and lower consumer costs.

Predicting the (renewable) energy supply

Examples of possible applications: planning energy day-to-day supply, predicting periods of high uncertainty, optimizing conventional energy supply, making energy transfer decisions with neighbouring TSOs, intraday energy trading.

Predictive maintenance

Possible applications: condition monitoring of asset via sensor data, identifying failure patterns in historical data, identifying periods of high stress just before failure, mapping logistics/ costs of failure.

Combining IoT and data science to predict demand and outage.

The Utility sector is heavily dependent on the performance of their assets: energy cables. Using smart sensors it is possible to better forecast maintenance.

Key components

Main area's: large power producer, TSO, DSO's. Energy suppliers and commercial companies who sell energy.

Threats

Governmental policy and regulation, updating grid infrastructure, aging workforce.

What value can bring Data Science & AI to your Utilities company?

Data capturing

Using smart cameras, nodes and IoT sensors, you can gather more information about the developments of your goods.

Machine Learning

Using deep learning techniques, you automatically convert your data to actionable insights.

The data ecosystem

Connecting data sources from different stakeholders in the value chain, you can create an ecosystem and profit from large scale data analysis.

Discover our freelancers in this field