Machine Learning Usecases for Energy Forecasting
Following are different usecases in relation with energy management where machine learning could be used for probabilistic energy forecasting. For those who are new to probabilistic forecasting, here is the definition from Wikipedia: Probabilistic forecasting summarises what is known, or opinions about, future events. In contrast to a single-valued forecasts (such as forecasting that the maximum temperature at given site on a given day will be 23 degrees Celsius or that the result in a given football match will be a no-score draw), probabilistic forecasts assign a probability to each of a number of different outcomes, and the complete set of probabilities represents a probability forecast. In simpler words, the idea behind forecasting is to predict about future events. Some of the other areas apart from energy forecasting where probabilistic forecasting is used are weather forecasting, sprots betting etc.
- Electric Load Forecasting: The primary objective is to come up with probabilty distribution of hourly loads on the continuous basis.
- Electricity Price Forecasting: The primary objective is to forecast the probabilty distribution of the electricity price for one or more zones on a continuous basis.
- Wind Power Forecasting: The primary objective is to forecast the probabilty distribution of the wind power generation for one or more wind farms.
- Solar Power Forecasting: The primary objective is to forecast the probabilty distribution of the solar power generation for one or more solar farms on a continuous basis.
Machine Learning Models for Energy Data Mining & Forecasting
Following are different machine learning algorithms that could be applied for doing data mining or forecasting for energy-related usecases.
- Artificial neural networks
- Regression models: Could be used for electricity price forecasting
- Clustering algorithms
Top Blog for Energy Analytics/Forecasting
One could find some real great blogs on A blog by Dr. Tao Hong. There are several informative and thought proviking articles out there. I am glad that I found this blog. Worth a check out.
He has also authored the book, Building Web Apps with Spring 5 and Angular.
Latest posts by Ajitesh Kumar (see all)
- Unit Tests & Data Coverage for Machine Learning Models - May 11, 2019
- ML Models Confusion Matrix Explained with Examples - March 30, 2019
- Machine Learning Cheat sheet (Stanford) - March 23, 2019