With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

随着屋顶太阳能光伏装置的激增，需要主动预测消费者对采用太阳能光伏的潜力，以改善电力公司的规划和运营。传统的分析建模方法仅限于某些调查功能，并且决策模型不会影响大部分调查。本文介绍了一种新颖的，数据驱动的建模方法，该方法使用机器学习框架来训练可预测潜在太阳能采用量的模型，从而从策略上修剪大量的消费者资料功能。该方法通过轻型梯度提升框架利用了梯度提升决策树模型，该框架大大改善了现有方法的较差的预测准确性。使用基于焦点损失的监督进行模型训练可克服传统数据驱动模型固有的识别潜在采用者的困难。此外，为了克服有限调查样本中可能的数据稀疏性，提出了一个生成对抗网络来创建综合用户样本，并评估了其对模型性能的有效性。贝叶斯优化方法用于系统地得出所提出模型的超参数。由弗吉尼亚州全国农村电力合作协会于2018年收集的调查数据对本文提出的方法进行了验证，证明了基于机器学习的方法对可靠的太阳能采用建模的出色预测能力。