Estimation of Hourly Temperature at a Site and its Impact on Energy Yield of a PV Module

Abstract

This paper discusses a method for estimating the hourly temperature at a location, if the ambient maximum and minimum temperatures of a particular day, along with the maximum temperature on the previous day and the minimum temperature of the following day, are known. The impact of estimated temperature on the energy yield of a photovoltaic (PV) module is also discussed with the help of a mathematical model. Algorithms have been developed for relevant computations and implemented using MATLAB. Hourly meteorological data of Bhopal and Trivandrum, India, have been considered for studying, analyzing, and validating the results. A PV module (BP 3215) manufactured by BP Solar Ltd. has been considered for analyzing the results in terms of energy generation. The statistical error parameters calculated for the estimated hourly temperature indicate a reasonable degree of accuracy of estimation. The values of mean absolute error (MAE) and root mean square error (RMSE) are superior to those of other models and that of R 2 is comparable. The estimated values of yearly and monthly average of hourly temperature are invariably found to be more than the corresponding measured values. At these two locations, there is a reasonable linear fit between monthly averages of hourly temperature, calculated from the estimated and measured values of hourly temperature. The deviation between the two values of annual energy generated, one calculated from the estimated values and the other from the measured values of hourly temperature, is nominal. Thus, the estimated values of hourly temperature can be used for energy estimation and feasibility analysis of PV system, with a fair degree of accuracy.

Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data

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Models to predict the operating temperature of different photovoltaic modules in outdoor conditions

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