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Power generation through solar photovoltaics has shown significant growth in recent years. However, high penetration of solar PV creates power system operational issues as a result of solar PV variability and uncertainty. Short-term PV variability mainly occurs due to the intermittency of cloud cover. Therefore, to mitigate the effects of PV variability, a sky-image-based, localized, global horizontal irradiance forecasting model was introduced considering the individual cloud motion, cloud thicknesses, and the elevations of clouds above the ground level. The proposed forecasting model works independently of any historical irradiance measurements. Two inexpensive sky camera systems were developed and placed in two different locations to obtain sky images for cloud tracking and cloud-based heights. Then, irradiance values for onsite and for a PV site located with a distance of 2 km from the main camera were forecasted for 1 minute, 5 minutes, and 15 minutes ahead of real-time. Results show that the three-level cloud categorization and the individual cloud movement tracking method introduced in this paper increase the forecasting accuracy. For partially cloudy and sunny days, the forecasting model for 15 min forecasting time interval achieved a positive skill factor concerning the persistent model. The accuracy of determining the correct irradiance state for a 1 min forecasting time interval using the proposed model is 81%. The average measures of RMSE, MAE, and SF obtained using the proposed method for 15 min forecasting time horizon are 101 Wm-2, 64 Wm-2, and 0.26, respectively. These forecasting accuracy levels are much higher than the other benchmarks considered in this paper. [ABSTRACT FROM AUTHOR]