AI-Driven Energy

The energy industry has recently started to adopt the survival analysis concept from the medical field," says Francisco Sanchez, president of Houston energy data science. This opportunity here is massive. Employing state-of-art data science tasks to build models keeps businesses agile and innovative. For Oil and Gas industry, the electric power management sector, and the renewable energy generation industry, data science can reduce downtime and improve productivity outcomes at a pace.

Renewable energy generation highly depends on unpredictable weather conditions. However, Data Science backups this challenge by offering Smart grid management- a predictive decision-making model for effective planning of power generation management.

Whether on the field or in the mines, safety and surveillance is a vital practice. Considering the advanced applications of machine vision and Advanced Analytics can help in generating real-time insights for making reliable decisions.

Measuring the storage capacity and time-to-time capacitor supply monitoring are driving the potential data-driven decision-making -which maximizes the energy storage value to deliver additional revenue streams and helps in increasing ROI.

Predicting and maintaining the equipment's lifetime depreciation value such as compressors using monitoring and modeling techniques can reduce the chances of equipment failure by proactive action.

Integrating big data and AI-techniques energy production units can forecast and extract incisive insights on long-term energy demand, price fluctuations, power mix, energy efficiency, climate forecasts, geostrategic development, and more.