Forecasting technology costs via the Learning Curve – Myth or Magic?
AbstractTo further our understanding of the effectiveness of learning or experience curves to forecast technology costs, a statistical analysis using historical data has been carried out. Three hypotheses have been tested using available data sets that together shed light on the ability of experience curves to forecast future technology costs. The results indicate that the Single Factor Learning Curve is a highly effective estimator of future costs with little bias when errors were viewed in their log format. However it was also found that due to the convexity of the log curve an overestimation of potential cost reductions arises when returned to their monetary units. Furthermore the effectiveness of increasing weights for more recent data was tested using Weighted Least Squares with exponentially increasing weights. This resulted in forecasts that were typically less biased than when using Ordinary Least Square and highlighted the potential benefits of this method.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0710.
Date of creation: Feb 2007
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Forecasting; Learning curves; Renewable energy;
Find related papers by JEL classification:
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- O30 - Economic Development, Technological Change, and Growth - - Technological Change; Research and Development; Intellectual Property Rights - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-06-11 (All new papers)
- NEP-ENE-2007-06-11 (Energy Economics)
- NEP-FOR-2007-06-11 (Forecasting)
- NEP-TID-2007-06-11 (Technology & Industrial Dynamics)
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- Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
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