Machine Learning Method To Identify Residential Pv Adopters, Reduce Soft Costs

An international research team has developed a machine learning algorithm known as XGBoost to predict PV adoption among homeowners. The algorithm consists of a distributed gradient-boosted decision tree (GBDT) machine learning library that can accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. The researchers compared the performance of the proposed algorithm with the logistic regression approach, which is the most commonly used method to analyze differences between PV adopters and non-adopters. The adapted algorithm was able to offer better results than the logistic regression in predictive performance, with the correct adopter rate increasing from 66 to 87% and the correct non-adopter rate increasing from 75 to 88%.