Joe Cheng presents Shiny
The Shiny framework is a web application framework for the R programming language, designed by Joe Cheng, to help R programmers turn their analysis into interactive web applications. Fortunately, it does without requiring that the R programmer know HTML, CSS or JavaScript – greatly widening the possible audience of users. At the January DataScience.LA R Meetup, we were fortunate to have Joe Cheng, Software Engineer at RStudio and original architect of Shiny present an introduction to Shiny.

Top 4 Machine Learning Use Cases for Energy Forecasting
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.
• 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.

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