Labeled Clearsky Period Data Set
Time series data set containing labeled clearsky periods.
14 SOLAR ENERGY↗
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Sensitivity analysis of upwelling thermal radiance in presence of clouds
Total upwelling radiance at the top of the atmosphere is evaluated theoretically in the presence of clouds. The influence of cloud heights, thicknesses and different cloud covers on the upwelling radiance is also investigated. The characteristics of the two cloud types considered in this study closely correspond to altocumulus and cirrus with the cloud emissivity as a function of its liquid water (or ice) content. For calculation of the integrated transmittance of atmospheric gases such as, H2O, CO2, O3, and N2O, the Quasi Random Band (QRB) model approach is adopted. Results are obtained in three different spectral ranges and are compared with the clearsky radiance results. It is found that the difference between the clearsky and cloudy radiance increases with increasing cloud height and liquid water content. This difference also decreases as the surface temperature approaches the value of the cloud top temperature.
Subramanian, S. V.↗
Generation and validation of comprehensive synthetic weather histories using auto-regressive moving-average models
As energy system design moves to more complex methods of optimization including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilized to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilizing both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. Here, it is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.
42 ENGINEERING↗