Utilities
Use cases: Machine Learning Solutions
The intersection of the data industry and the energy industry is crucial for maximizing safety and environmental protection. Machine Learning can bring additional benefits to the utilities sector, such as minimizing risks and improving customer experience. Machine Learning models can also monitor and improve operations in real time, analyze and predict patterns, while being scalable and self-improving.
Monitoring and proactively maintaining infrastructure like pipelines or electricity lines
Drone or satellite data is readily available and despite its complexity, Machine Learning models can quickly analyze it even for a large area. Comparing it to patterns learned from historical data, a Machine Learning model can identify deviations and create a real-time warning system that can catch damages early for minimizing loss.
Modeling renewable energy supply based on weather data and cloud coverage
Based on satellite imagery including cloud coverage and historical weather data, Machine Learning models can be used to model renewable energy supply with accurate predictions that can help customers prepare for low supply time periods for instance.
Modeling energy demand for most economic production capacity utilization
Sudden bursts in energy use can be problematic for the network. A Machine Learning model can learn patterns of demand, based on which assets can be divided in an optimal way. Analyzing the important features the model has learned could help companies identify potential ways to improve efficiency.
