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

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  • Rakesh K. Sarin

    (University of California, Los Angeles)

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.

Suggested Citation

  • Rakesh K. Sarin, 1979. "An Approach for Long Term Forecasting with an Application to Solar Electric Energy," Management Science, INFORMS, vol. 25(6), pages 543-554, June.
  • Handle: RePEc:inm:ormnsc:v:25:y:1979:i:6:p:543-554
    DOI: 10.1287/mnsc.25.6.543
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    Cited by:

    1. Zanakis, Stelios H. & Mandakovic, Tomislav & Gupta, Sushil K. & Sahay, Sundeep & Hong, Sungwan, 1995. "A review of program evaluation and fund allocation methods within the service and government sectors," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 59-79, March.
    2. Tianyang Wang & James S. Dyer & John C. Butler, 2016. "Modeling Correlated Discrete Uncertainties in Event Trees with Copulas," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 396-410, February.
    3. Guk-Hyun Moon & Rakkyung Ko & Sung-Kwan Joo, 2020. "Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model," Energies, MDPI, vol. 13(7), pages 1-12, April.
    4. Luis V. Montiel & J. Eric Bickel, 2013. "Approximating Joint Probability Distributions Given Partial Information," Decision Analysis, INFORMS, vol. 10(1), pages 26-41, March.

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