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Top 4 Machine Learning Use Cases for Energy Forecasting

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This article explores the top 4 machine learning use cases for energy forecasting. Please feel free to comment/suggest if I forgot to mention one or more important points.

Machine Learning Use Cases for Energy Forecasting

Following are different use cases in relation to 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 make predictions about future events. Some of the other areas apart from energy forecasting where probabilistic forecasting is used are weather forecasting and sports betting.

  • Electric Load Forecasting: The primary objective is to come up with the probability distribution of hourly loads on a continuous basis.
  • Electricity Price Forecasting: The primary objective is to forecast the probability distribution of the electricity price for one or more zones on a continuous basis.
  • Wind Power Forecasting: The primary objective is to forecast the probability distribution of the wind power generation for one or more wind farms.
  • Solar Power Forecasting: The primary objective is to forecast the probability distribution of 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 used for doing data mining or forecasting for energy-related use cases.

  • Artificial neural networks
  • Regression models, which 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-provoking articles out there. I'm glad that I found this blog. It's worth a look.

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Published at DZone with permission of Ajitesh Kumar, DZone MVB. See the original article here.

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