Researchers have defined a new machine learning-based methodology that reportedly reduces customer acquisition costs by about 15% or $0.07/Watt. It is based on an adapted version of the XGBoost algorithm and considers factors such as summer bills, household income, and homeowner's age, among others.An international research team has utilized a machine learning algorithm known as XGBoost (eXtreme Gradient Boosting) to predict PV adoption among homeowners. This algorithm consists of a distributed gradient-boosted decision tree (GBDT) machine learning library that can help accurately predict a target ...Den vollständigen Artikel lesen ...