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Machine learning method to identify residential PV adopters, reduce soft costs – pv magazine International


The researchers described a brand new technique primarily based on machine studying that reportedly diminished buyer acquisition prices by about 15% or $0.07/Watt. It’s primarily based on an tailored model of the XGBoost algorithm and takes under consideration elements equivalent to summer season payments, family earnings, and age of the house proprietor, amongst others.

A global analysis group used a machine studying algorithm often known as XGBoost (eXtreme Gradient Boosting) to foretell PV adoption amongst owners. This algorithm consists of a distributed gradient-boosted resolution tree (GBDT) machine studying library that helps precisely predict a goal variable by combining an ensemble of estimates from a set of extra easy and weak fashions.

“We analyzed the element of XGBoost modeling and broke down the improved prediction efficiency of logistic regression into two elements: variable interplay and nonlinearity,” the scientists stated. “We lastly demonstrated the potential of XGBoost to scale back buyer acquisition prices, after which the flexibility to establish new market alternatives for PV firms.”

Based on them, this new method will assist photo voltaic firms cut back buyer acquisition prices and different smooth prices related to the residential PV enterprise.

They in contrast the efficiency of the proposed algorithm with the logistic regression method, which the researchers described as essentially the most generally used technique to investigate the variations between PV adopters and non-adopters. “Our logistic regression mannequin with 9 unique and extremely seen family options efficiently predicted 71% of out-of-sample PV adoption conditions,” they additional defined. “The mannequin appropriately recognized 66% of adopters and 75% of non-adopters.”

The tailored algorithm, in line with the analysis group, was capable of provide higher outcomes than logistic regression in predictive efficiency. “The predictive mannequin appropriately predicted 87% of each PV adoption statuses, in comparison with 71% for logistic regression,” they added. “The right adopter price elevated from 66 to 87% and the proper non-adopter price elevated from 75 to 88%.”

They attribute the superior efficiency of the machine learning-based method to the truth that it combines advanced nonlinearity and variable interplay and takes under consideration elements equivalent to summer season payments, family earnings, and age. of the proprietor of the home, and many others.

“The benefit of utilizing these variables is that they’re simply accessible in order that PV firms can acquire information on them at low price,” in addition they stated. “Another excuse to clarify the higher efficiency of XGBoost is that it’s attainable to get better the important thing hidden data embedded within the information. “For instance, together with geographic data such because the state or province of the respondent will increase the accuracy in logistic regression prediction on a scale.”

The analysis group estimates that the brand new method will assist PV firms cut back buyer acquisition prices by about 15% or $0.07/Watt. It additionally explains that information mining and machine studying may also assist cut back smooth prices for contract cancellation, provide chain administration, labor task, and allowing and inspection points.

It describes the brand new technique within the examine “Machine studying reduces smooth prices for residential photo voltaic photovoltaics,” printed in scientific stories. The analysis group was shaped by scientists from the US Division of Power’s Nationwide Renewable Power Laboratory (NREL), the Lawrence Berkeley Nationwide Laboratory, the Florida State College, the College of Wisconsin-Madison, and the Renmin College of China.

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