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An electronic system for predicting air temperature and wind speed patterns

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  • Njau, Ernest C.

Abstract

Simulations of long-term performances of many solar and wind energy systems require inputs of correspondingly simulated or predicted air temperature data and/or wind speed data. For systems that are expected to be in use for several years to come, predicted future temperature data and/or wind speed data are essential, not only for optimizing the respective designs, but also for evaluating future operating and maintenance costs. In view of the implied importance of predicted future temperature and wind speed data in the solar and wind energy industry, we present a detailed description of an electronic system that can be used flexibly to generate the latter data with reasonable accuracy. A series of successful tests have been conducted upon the electronic system, and samples of results therefrom are presented in the text. It is shown that with further extensions, the electronic system just mentioned can also be used to predict environmental parameters, such as annual or seasonal rainfall, air vapour pressure, relative humidity and dew point, which can be of use to designers of devices such as solar stills.

Suggested Citation

  • Njau, Ernest C., 1994. "An electronic system for predicting air temperature and wind speed patterns," Renewable Energy, Elsevier, vol. 4(7), pages 793-805.
    • Handle: RePEc:eee:renene:v:4:y:1994:i:7:p:793-805
    DOI: 10.1016/0960-1481(94)90232-1
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    References listed on IDEAS

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    1. Njau, Ernest C., 1994. "Predictability of wind speed patterns," Renewable Energy, Elsevier, vol. 4(2), pages 261-263.
    2. Egarievwe, S.U. & Animalu, A.O.E. & Okeke, C.E., 1991. "Harmattan performance of a solar still in the Guinea Savannah," Renewable Energy, Elsevier, vol. 1(5), pages 799-801.
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    Cited by:

    1. Njau, Ernest C., 1997. "Climatic switches," Renewable Energy, Elsevier, vol. 10(1), pages 1-9.
    2. Mohandes, Mohamed A. & Rehman, Shafiqur & Halawani, Talal O., 1998. "A neural networks approach for wind speed prediction," Renewable Energy, Elsevier, vol. 13(3), pages 345-354.
    3. Tasadduq, Imran & Rehman, Shafiqur & Bubshait, Khaled, 2002. "Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia," Renewable Energy, Elsevier, vol. 25(4), pages 545-554.

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