QUANTIFYING THE VARIABILITY OF SOLAR PV PRODUCTION FORECASTS

The actual power produced by a solar photovoltaic power system varies according to the refraction, reflection, and absorption of radiation by the atmosphere. Standard production forecasts, however, do not address production uncertainty probabilistically. Using thirty years of historical data from the National Solar Radiation Database (“NSRDB”), we use stochastic simulation to evaluate the production uncertainty that is otherwise ignored by traditional production forecas ts. In the case documented herein, we find significant diff erences between the forecasts made by conventional production forecasting models and those designed explicitly to reflect the uncertainty found in actual historical experience. Having more accurate information about production uncertainty should facilitate more project-appropriate financial structures, reducing risk and increasing investor returns.

[1]  Paul J. Root,et al.  Risk Analysis in Drilling Investment Decisions , 1968 .

[2]  T. Bedford,et al.  Probabilistic Risk Analysis: Foundations and Methods , 2001 .

[3]  Clive Vaughan Jones Financial Risk Analysis of Infrastructure Debt: The Case of Water and Power Investments , 1991 .

[4]  E. Dutton,et al.  Do Satellites Detect Trends in Surface Solar Radiation? , 2004, Science.

[5]  John M. Reilly,et al.  Uncertainty in emissions projections for climate models , 2002 .

[6]  V. Tikhomirov On the Representation of Continuous Functions of Several Variables as Superpositions of Continuous Functions of one Variable and Addition , 1991 .

[7]  Gustav Feichtinger,et al.  Population, Natural Resources and Food Security Lessons from Comparing Full and Reduced Form Models , 2000 .

[8]  The application of risk analysis to the appraisal of optional investment in the electricity supply industry , 1986 .

[9]  W. Nordhaus,et al.  The Impact of Global Warming on Agriculture: A Ricardian Analysis: Reply , 1999 .

[10]  M. Sarofim,et al.  Uncertainty in emissions projections for climate models , 2002 .

[11]  Christoph Schillings,et al.  Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data , 2006 .

[12]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[13]  J. Mumpower,et al.  Risk Analysis and Risk Management: An Historical Perspective , 1985 .

[14]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[15]  Kenneth J. Singleton,et al.  Credit Risk: Pricing, Measurement, and Management , 2003 .

[16]  A. M. Economos,et al.  A Financial Simulation for Risk Analysis of a Proposed Subsidiary , 1969 .

[17]  Lawrence Kryzanowski,et al.  Monte Carlo Simulation and Capital Expenditure Decisions—A Case Study , 1972 .

[18]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[19]  Pamela K. Coats,et al.  Coping With Business Risk Through Probabilistic Financial Statements , 1982 .

[20]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[21]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..