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Evaluating predictive models for solar energy growth in the US states and identifying the key drivers

Evaluating predictive models for solar energy growth in the US states and identifying the key drivers

Full Title: Evaluating predictive models for solar energy growth in the US states and identifying the key drivers
Author(s): Joheen Chakraborty and Sugata Banerji
Publisher(s): IOP Publishing
Publication Date: March 1, 2018
Full Text: Download Resource
Description (excerpt):

Driven by a desire to control climate change and reduce the dependence on fossil
fuels, governments around the world are increasing the adoption of renewable energy sources.
However, among the US states, we observe a wide disparity in renewable penetration. In this
study, we have identified and cleaned over a dozen datasets representing solar energy
penetration in each US state, and the potentially relevant socioeconomic and other factors that
may be driving the growth in solar. We have applied a number of predictive modeling
approaches – including machine learning and regression – on these datasets over a 17-year
period and evaluated the relative performance of the models. Our goals were: (1) identify the
most important factors that are driving the growth in solar, (2) choose the most effective
predictive modeling technique for solar growth, and (3) develop a model for predicting next
year’s solar growth using this year’s data. We obtained very promising results with random
forests (about 90% efficacy) and varying degrees of success with support vector machines and
regression techniques (linear, polynomial, ridge). We also identified states with solar growth
slower than expected and representing a potential for stronger growth in future.

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