9 reasons why green energy production and consumption can benefit from AI and ML

1. Analyzing past, optimizing present, predicting future

- Improving efficiency and offering the best possible service.

2. Improved Safety, Reliability and Efficiency

- Recognizing patterns, energy leakages, and health of the devices - predictive analytics for predictive maintenance.

3. Smoothing out unexpected changes in energy flow due to weather unpredictability

- Advanced optimization of AI grids in combination with AI storage units and making timely decisions for energy allocation.

4. Accommodating the diversity

- Eliminating the challenge of balancing different renewable energy sources due to their diversity.

5. Improved integration into traditional energy sources.

- Due to the high expenses of innovative energy grids, the transition will be lengthily, and therefore the need for improved integration during the transition period will be imperative.

6. Quality and congestion solution.

- Better integration and management of distributed energy from microgrids in order to balance the energy flow within the main power grid.

7. Greater control

- Flexibility in adjusting the supply with the demand. Also, automated switch off coupled with intelligent storage units can be adjusted based on supply.

8. Business expansion.

- Based on vast amounts of collected data, it is easier to gain insight into potential energy consumptions and target the new consumer market.

9. Transparency and data-driven decisions (For consumers and microgrid owners)

- Personal measurement against the provider measurement, so one can leverage their AI data against the provider, and improving efficiency in power exchange with the main grid.