U.S. Department of Energy

Forecasting and Influencing Technological Progress in Solar Energy

Logos of the University of North Carolina at Charlotte, Arizona State University, and the University of Oxford. A diagram that symbolizes the interconnected nature of technological improvement in photovoltaic development.

An "ecosystem" of solar innovation: This network diagram symbolizes the interconnected nature of technological improvement. Each node represents a technology used in photovoltaic development, and each link signifies that co-occurrence in a patent. Progress in one area quickly spreads to connected technologies.

The University of North Carolina at Charlotte, along with their partners at Arizona State University and the University of Oxford, under theĀ Solar Energy Evolution and Diffusion Studies (SEEDS) program, is developing better models for predicting how R&D translates into improved performance and reduced costs for energy technologies.


Technological forecasts, which plot the anticipated performance and costs of high-tech products, are used by decision makers to select public and private technology investment portfolios. A diverse set of policy mechanisms, has R&D funding and financial incentives, have been established for renewable energy technologies based on estimates of future rates of progress and adoption. However, uncalculated error bounds, the unknown consequences of selected policy interventions, and the unaccounted for interdependence of different technologies stymie this strategy for accelerating the pace of technological change. This project will develop new data-driven methods that incorporate a large corpus of historical records to design optimized R&D investment decisions.


Within the last decade, digitized records have become widely available that can be used to quantify and model the processes underlying technological evolution. These "innovation databases"—such as patent records from 1790 to the present; industrial classifications and census data; price indices; R&D funding histories; and performance trajectories—form the basis for this project. First, the data will be analyzed in new ways to understand the intrinsic relationships between technologies, that is, how progress in one domain relate to other domains. Next, the databases will be conjoined to estimate how R&D funding has downstream effects on consumer prices. Finally, a theory of resource allocation will be developed to optimize the real-world impact of R&D funding in terms of acceleration of price reduction and deployment of renewable energy technologies.


This project aims to provide a foundational understanding of technology evolution that can be practically applied by public and private sector decision makers to speed the achievement of price-competitive solar energy technologies.