An Approach for Long Term Forecasting with an Application to Solar Electric Energy

Rakesh K. Sarin
Additional contact information
Rakesh K. Sarin: University of California, Los Angeles

Management Science, 1979, vol. 25, issue 6, 543-554

Abstract: An approach is proposed that is useful for long term forecasting of market penetration of new technologies, fuel price and availability, business performance, etc. The central idea is to systematically solicit experts' opinion in the form of subjective probability distributions in making future projections. The approach has two basic ingredients. One is the decomposition of the problem so that each expert is dealing with a relatively simple situation. The other is to use modeling so as to minimize the information required of the experts. The likelihood of occurrence of the relevant future scenarios is computed by eliciting from experts the single event and the conditional probabilities. The probability distributions for the variable of interest are assessed by specifying some scenarios. A model is used to predict the probability distributions for a general set of scenarios. A study using the approach to forecast solar electric energy market penetration by the year 2000 is discussed. In this study several experts from utility companies, governmental agencies, research laboratories, and universities were interviewed. The implications of our findings to long range planning for solar electric energy are discussed. The results of this study should be useful to the planners in the utility companies and the governmental agencies.

Keywords: forecasting: applications; forecasting: Delphi process; decision analysis: applications (search for similar items in EconPapers)
Date: 1979
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.25.6.543 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:25:y:1979:i:6:p:543-554

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().