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Prediction of monthly-mean hourly relative humidity, ambient temperature, and wind velocity for India

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  • Parishwad, G.V.
  • Bhardwaj, R.K.
  • Nema, V.K.

Abstract

This paper presents a procedure to predict monthly-mean hourly values of relative humidity, ambient temperature and wind velocity for an Indian location. Three maps, showing distribution of annual-average hourly values of humidity, temperature and wind velocities, are prepared from the analysis of available meteorological data of 205 Indian cities. An equation is obtained for annual-average temperature as a function of altitude of the location. Sets of equations are then developed to predict the said weather parameters by the least square regression analysis of the data of 14 cities, taken from different regions, out of 19 cities for which detailed weather data was available. A ratio of monthly-mean to the yearly-mean value of variable is correlated with month and then hourly to the monthly-mean value is correlated with day-hours. On comparison of the computed results with the measured data of the remaining 5 cities, yearly-average relative standard deviations are 14.6, 10.5 and 26.7% for monthly-mean hourly relative humidity, ambient temperature and wind velocities, respectively.

Suggested Citation

  • Parishwad, G.V. & Bhardwaj, R.K. & Nema, V.K., 1998. "Prediction of monthly-mean hourly relative humidity, ambient temperature, and wind velocity for India," Renewable Energy, Elsevier, vol. 13(3), pages 363-380.
    • Handle: RePEc:eee:renene:v:13:y:1998:i:3:p:363-380
    DOI: 10.1016/S0960-1481(98)00010-X
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    Cited by:

    1. Paniagua-Tineo, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Ortiz-García, E.G. & Cony, M.A. & Hernández-Martín, E., 2011. "Prediction of daily maximum temperature using a support vector regression algorithm," Renewable Energy, Elsevier, vol. 36(11), pages 3054-3060.
    2. Altan Dombaycı, Ömer & Gölcü, Mustafa, 2009. "Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey," Renewable Energy, Elsevier, vol. 34(4), pages 1158-1161.

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