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Machine Learning 1: Linear Regression

Machine Learning 1: Linear RegressionStefano ErmonMarch 31, 2016 Stefano ErmonMachine Learning 1: Linear RegressionMarch 31, 20161 / 25 Plan for todayPlan for today:Supervised Machine Learning : Linear regressionStefano ErmonMachine Learning 1: Linear RegressionMarch 31, 20162 / 25 Renewable electricity generation in the : Renewable energy data book, NRELS tefano ErmonMachine Learning 1: Linear RegressionMarch 31, 20163 / 25 Challenges for the gridWind and solar are intermittentWe will need traditional power plants when the wind stopsMany power plants ( , nuclear) cannot be easily turned on/off orquickly ramped up/downWith more accurate forecasts, wind and solar power become moreefficient alternativesA few years ago, Xcel Energy (Colorado) ran ads opposing a proposalthat it use 10% of renewable sourcesThanks to wind forecasting (ML) algorithms developed at NCAR, theynow aim for 30 percent.

Stefano Ermon Machine Learning 1: Linear Regression March 31, 2016 7 / 25. A simple model A linear model that predicts demand: predicted peak demand = 1 (high temperature) + 2 60 65 70 75 80 85 90 95 1.5 2 2.5 3 High Temperature (F) Peak Hourly Demand (GW) Observed data Linear regression prediction Parameters of model: 1;

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