Machine Learning for Solar Technology Portfolio Management
- SRI International, Menlo Park, CA (United States)
- George Mason Univ., Fairfax, VA (United States)
Technology readiness level (TRL) labeling and forecasting today is done entirely manually. A computationally-assisted model of a TRL-like classification for estimation and tracking could enable a research and development (R&D) portfolio manager to more accurately estimate the relative level of development for technologies in that portfolio. This report describes the work to understand readiness level systems, train a machine learning algorithm to recognize textual indicators of particular readiness levels within written material from carefully chosen case studies in renewable technology development, and apply that algorithm to predict the readiness levels of other technology development cases. We account for the results of each step of the work conducted, including the predictive accuracy of the trained model: 54% correct, within a +/-1 readiness level error threshold and a root mean squared error of 1.88. Great potential remains to improve and apply this model for management of individual projects and across portfolios.
- Research Organization:
- SRI International, Menlo Park, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- EE0007661
- OSTI ID:
- 1542861
- Report Number(s):
- Final-Report-7661
- Resource Relation:
- Related Information: Seeds Manual
- Country of Publication:
- United States
- Language:
- English
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