Solar radiation along with other weather variables are commonly processed on typical meteorological years (TMYs) to be applied in the design of various energy systems. However, in several regions of ...the world, solar radiation data usually lacks a suitable and/or representative measurement, which leads to its modeling and prediction to properly fill this information in the databases. Consequently, the accuracy of these models can influence the viability and proper design of such energy systems. Within this context, the present contribution aims to assess the quality of solar radiation data included in the most recent TMY databases with Brazilian data and how that quality can influence the selection of months that create TMYs as well as the building performance simulation (BPS) results. Because two different approaches to generate the solar radiation data are used, we evaluate the global horizontal irradiation data in the two latest versions of recent Brazilian TMY databases against the corresponding satellite-derived ones obtained from the POWER database (NASA). Simultaneously, as another alternative approach, global solar radiation data are calculated for the same studied locations and period through the modeling method used to generate the current version of the International Weather for Energy Calculations (IWEC2), and its performance is also compared against the corresponding reanalysis data (POWER). Finally, a set of case studies applying the local building performance regulations are exhaustively analyzed to quantify the impact of the uncertainty of solar radiation models on BPS results throughout Brazil. The results indicate that the accuracy of solar radiation models can highly influence the resulting TMY configurations. These changes can drive differences up to 40% on the prediction of the ideal annual loads of the residential buildings while, regardless of design performance, differences lower than 10% are found for the commercial case studies in most locations. Conversely, the prediction of peak loads for cooling shows to be more sensitive to the climate data changes in the commercial buildings than in the residential ones.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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