The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power systems as one of the most feasible energy generation resources. Considering the influence of stochastic weather conditions over the output power of photovoltaic (PV) systems, the necessity of a sophisticated forecasting model is increased rapidly. A genetic algorithm-based support vector machine (GASVM) model for short-term power forecasting of residential scale PV system is proposed in this manuscript. The GASVM model classifies the historical weather data using an SVM classifier initially and later it is optimized by the genetic algorithm using an ensemble technique. In this research, a local weather station was installed along with the PV system at Deakin University for accurately monitoring the immediate surrounding environment avoiding the inaccuracy caused by the remote collection of weather parameters (Bureau of Meteorology). The forecasting accuracy of the proposed GASVM model is evaluated based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Experimental results demonstrated that the proposed GASVM model outperforms the conventional SVM model by the difference of about 669.624 W in the RMSE value and 98.7648% of the MAPE error. •We propose a stochastic GASVM model for short-term forecasting of PV output power.•We use historical weather data collected from a local weather station for accurately modeling the system.•We evaluate the forecasting accuracy of the proposed technique based on RMSE and MAPE.•The results are compared with conventional SVM technique.•The proposed forecasting technique stands out for features like being robust, accurate, fast and needing less memory.