Solar cooling performance predictions via stochastic weather algorithms

D.K. Anand and I.N. Deif

Energy, 1979, vol. 4, issue 4, 537-548

Abstract: System simulations for sizing and performance predictions of various solar systems require some form of weather input to act as a system stimulus. When actual weather data is used, hourly simulations are expensive and require considerable data handling. For many design procedures, however, hourly information is not needed, and simpler methods are desirable. One such method employs a probabilistic approach. This method involves the use of an algorithm that generates a probabilistic matrix, and an analytical formulation which is used to generate synthetic weather data. The approach has been found to be satisfactory. This work uses the stochastic (probablistic) method to produce representative weather for five geographic regions in the U.S. for the summer months. Parallel runs are conducted with real and stochastic weather. A comparison of the results clearly shows that the probabilistic approach can satisfactorily substitute for real weather for long-term system performance.

Date: 1979
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:4:y:1979:i:4:p:537-548

DOI: 10.1016/0360-5442(79)90082-3

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