Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

This paper investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product’s market success. A case study of residential photovoltaic panels is performed in the context of the California, USA market within the 2007-2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial Neural Networks, Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional non-technical attributes are identified: panel manufacturer’s reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and non-technical attributes to guide design priorities. Nomenclature k Attribute number from 1-34 MSE Mean Squared Error PTC PV-USA Test Conditions: Air temperature 20◦C, 10m above ground level, 1m/s wind speed, AM1.5 solar spectrum, 1000W/m2 irradiance. R Correlation coefficient R2 Coefficient of determination STC Standard Test Conditions: Cell temperature 25◦C, AM1.5 solar spectrum, 1000W/m2 irradiance. σMS Standard deviation of market share ∗Address all correspondence to this author.