This paper presents a novel approach to reduce the post-processing of Numerical Weather Prediction (NWP) grid models to extract solar irradiance predictions for "sub-grid" locations. This technique estimates solar irradiance observations from a NWP model using a novel bank of 24 Kalman Filters (KFs) which operate in tandem to accurately predict the Solar irradiance Z(t), 24 hours ahead for a desired node point 51.8830N, 8.5353W, at a campus based Photovoltaic (PV) solar energy resource at Cork Institute of Technology (CIT). The NWP model outputs irradiance values m(t) for Cork Airport 51.8466N, 8.4891W (2.5km from CIT). The 24 KF bank, acting as a post-processing tool with a moving time averaging window, derives a 24 hour look ahead irradiance schedule for CIT by applying a polynomial to map and filter the irradiance bias offset between the two grid points. A 3rd order polynomial irradiance model was employed in the 24 KF bank state estimator and a 3 point average was used in the smoothing of the error in the state and measurement covariance matrices - Q(t) and R-t. This procedure accurately predicts irradiance with significant reduction in the Root Mean Square Error (RMSE) over existing methods [9] for a site specific location through manipulation of a NWP grid model output with fixed grid points. Since many system operators use a day-ahead commitment of electricity generators to meet the next day's forecasted load demand, the accuracy of the 24 hour look ahead irradiance estimates are crucial in PV electricity power generation. This is of strategic importance where installed solar scheduling is an issue for cost effective electrical network operation with a consequent beneficial economic return on solar array installation capital investment. Moving closer to real time, updated conditions and forecasts are used to dispatch generators, secure energy reserves and lock in electric energy imports/exports [1].