Using Machine Learning and Data Analysis to Improve Customer Acquisition and Marketing in Residential Solar

Program Document · Wed Oct 18 00:00:00 EDT 2017

OSTI ID:1402560

Sigrin, Benjamin O ^[1]

---

1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

High customer acquisition costs remain a persistent challenge in the U.S. residential solar industry. Effective customer acquisition in the residential solar market is increasingly achieved with the help of data analysis and machine learning, whether that means more targeted advertising, understanding customer motivations, or responding to competitors. New research by the National Renewable Energy Laboratory, Sandia National Laboratories, Vanderbilt University, University of Pennsylvania, and the California Center for Sustainable Energy and funded through the U.S. Department of Energy's Solar Energy Evolution and Diffusion (SEEDS) program demonstrates novel computational methods that can help drive down costs in the residential solar industry.

• Research Organization: National Renewable Energy Lab. (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1402560
• Report Number(s): NREL/FS-6A20-70077
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
SEEDS
Solar Energy Evolution and Diffusion
machine learning
data analysis
soft costs
solar soft costs
balance of system
solar balance of system