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Wind loading calculations are currently performed according to the ASCE 7 standard. Values in this standard were estimated from simplified models that do not necessarily take into account relevant flow characteristics. Thus, the standard does not have provisions to handle the majority of rooftop photovoltaic (PV) systems. Accurate solutions for this problem can be produced using a full-fledged three-dimensional computational fluid dynamics (CFD) analysis. Unfortunately, CFD requires enormous computation times, and its use would be unsuitable for this application which requires real-time solutions. To this end, a real-time response framework based on the proper orthogonal decomposition (POD) method is proposed.

A real-time response framework based on the POD method was used. This framework used beforehand and off-line CFD solutions from an extensive data set developed using a predefined design space. Solutions were organized to form the basis snapshots of a POD matrix. The interpolation network using a radial-basis function (RBF) was used to predict the solution from the POD method given a set of values of the design variables. The results presented assume varying design variables for wind speed and direction on typical PV roof installations.

The trained POD–RBF interpolation network was tested and validated by performing the fast-algebraic interpolation to obtain the pressure distribution on the PV system surface and they were compared to actual grid-converged fully turbulent 3D CFD solutions at the specified values of the design variables. The POD network was validated and proved that large-scale CFD problems can be parametrized and simplified by using this framework.

The solar power industry, engineering design firms and the society as a whole could realize significant savings with the availability of a real-time in situ wind-load calculator that can prove essential for plug-and-play installation of PV systems. Additionally, this technology allows for automated parametric design optimization to arrive at the best fit for a set of given operating conditions. All these tasks are currently prohibited because of the massive computational resources and time required to address large-scale CFD analysis problems, all made possible by a simple but robust technology that can yield massive savings for the solar industry.