1st Data Set: Predicting Solar Plant Energy Output
-Problem: Because solar power plants are at the mercy of weather forecasts, it is difficult for a solar energy plant to accurately calculate how much energy it can generate.
-Solution: Using predictive analytics, we can predict how much energy we can produce more efficiently.
-Results: By being able to predict how much energy a solar plant can generate in the future, the company can work more efficiently.
2nd Data Set - Predictive Maintenance for Wind Turbines
-Problem: Wind turbines have many factors that affect the health of the entire system, such as air temperature, torque, or wind speed. Because these factors are out of our control, there's a chance that wind turbines can break. When they break, repairs can be costly.
-Solution: To avoid downtime costs, a wind power company can use predictive analytics to predict the probability of machine failure and proactively maintain their wind turbines.
-Results: By using predictive analytics, a company can have a concrete maintenance plan to reduce downtime and eliminate costly repairs.