1 / 1

Predicting Solar Generation from Weather Forecasts

Predicting Solar Generation from Weather Forecasts. Advisor: Professor Arye Nehorai Chenlin Wu, Yuhan Lou Department of Electrical and Systems Engineering. Principal Component Analysis (PCA). Kernel Trick for SVR. Some weather metrics correlate strongly

Télécharger la présentation

Predicting Solar Generation from Weather Forecasts

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.


Presentation Transcript

  1. Predicting Solar Generation from Weather Forecasts Advisor: Professor AryeNehorai Chenlin Wu, Yuhan Lou Department of Electrical and Systems Engineering Principal Component Analysis (PCA) Kernel Trick for SVR • Some weather metrics correlate strongly Such as: Temperature & Time of the day • Applying PCA to remove redundant information Methods Conclusions Background Data Source Experiments Goals The kernel trick is a way of mapping observations from a general set S (Input space) into an inner product space V (high dimensional feature space) • Smart grid: increasing the contribution of renewable in grid energy • Solar generation: intermittent and non-dispatchable The graph shows the MSE with different input dimensions. The feature set with 8 dimensions performs the best with the lowest test error. And as long as we keep more than 5 principle components, the errors are lower than linear regression • where. • Creating automatic prediction models • Predicting future solar power intensity given weather forecasts • Predictions are made by proposed methods • 20% of data is used to train & 10% of the data is used to test • NREL National Solar Radiation Database 1991-2010 • Hourly weather and solar intensity data for 20 years • Station: ST LOUIS LAMBERT INT’L ARPT, MO • Input: (combination of 9 weather metrics) • Date, Time , Opaque Sky Cover, Dry-bulb Temperature, Dew-point Temperature, Relative Humidity, Station Pressure,Wind Speed, Liquid Precipitation Depth • Output : • Amount of solar radiation (Wh/m2) received in a collimated beam on a surface normal to the sun Gaussian Processes (GP) • MSE is used to evaluate the result of regression. Followings are the prediction errorsof the 3 different methods: • Linear Regression • 215.7884 • SVR • 130.1537 • SPGP • 122.9167 Linear regression GP regression model: Assume a zero mean GP prior distribution over inference functions . In particular, To make predictions at test points , where :, It follows that where SVM regression • In our research, regression is used to learn a mapping from some input space of n-dimensional vectors to an output space of real-valued targets • We apply different regression methods including: • Linear least squares regression • Support vector regression (SVR) using multiple kernel functions • Gaussian processes • Followings are 24-hour prediction Sparse Pseudo-input GP (SPGP) GPs are prohibitive for large data sets due to the inversion of the covariance matrix. Consider a model parameterized by a pseudo data set of size , where n is the number of real data points. Reduce training cost from to , and prediction cost from to Linear Model SPGP regression where measurement (solar intensity) X each row is a p-dimensional input unknown coefficient random noise Loss function(Square error): = Pseudo data set : Prior on Pseudo targets: Likelihood: Posterior distribution over : where Support Vector Regression (SVR) Given training data,…, Linear SVR Model: minimize + subject to • Using machine learning to automatically model the function of predicting solar generation from weather forecast lead to a acceptable result • Gaussian processes achieved lowest error among all the methods Given new input , the predictive distribution: where Loss function: (epsilon intensive)

More Related