Category Archives: Modeling

Plotting Forecast Data Objects Using ggplot

Robert Hyndman is the author of the forecast package in R. I’ve been using the package for long-term time series forecasts. The package comes with some built in methods for plotting forecast data objects in R that Ive wanted to customize for improved clarity and presentation.  The following article achieves that goal and shares two scripts for plotting forecast data objects using ggplot.

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From Least Squares to k-Nearest Neighbor (kNN)

The linear model is one of the most widely used data science tools and one of the most important.  In contrast, there is another basic tool:  the k nearest neighbor method (kNN).  Prediction and classification are two uses for these models.  In practice, classification results (ie. feature classes) are used by machines in many ways: to recognize faces in a crowd, to “read” road signs by distinguishing one letter from another and to set voter registration districts by separating population groups.  This article applies and compares linear and non-linear classification methods

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Predicting Technology Progress and Solar Growth

Technology progress is a key to solar growth and pricing.  By extension, the ability to model technology progress is essential to understanding future energy supply and demand.

Solar innovation is widespread. Examples include  solar cell efficiency, module manufacturing, and learning innovations with solar system installation and operation. Solar pricing and growth are also supported by innovations in enabling technology, such as battery storage, smart grids and electric vehicles.

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R Functions for Best Subset Regression

Best subset regression is an technique for model building and variable selection. The method looks at all combinations of independent predictor variables for use in a multiple regression model. Model developers and analysts will often struggle with variable selection, especially when the number of predictors is high.  Ideally, each set of predictors is run and the best set is selected using a criteria for model performance. The following article provides custom functions for best subset selection that are fast and easy to use.

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Aerosol Animation

Aerosol Optical Depth (AOD) defines the degree to which aerosols prevent the transmission of sunlight by absorption or scattering.  AOD is measured using an integrated extinction coefficient over a vertical column of air.  The extinction coefficient can be used to analyze solar extinction and the performance of solar power systems as a function of location and time.

GOCART

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Standard Atmosphere in R

Air Mass Coefficient

Click to enlarge

Click to enlarge

The air mass coefficient defines the path length (or column depth) of sunlight through the atmosphere.  The air mass for dry air, wet air and dust are key inputs for estimating solar extinction and the irradiance intensity on the Earth’s surface.  The air mass coefficient is a ratio between the path length for a specific zenith angle \theta_{Z} and the column depth when the zenith angle equals zero.

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