The trend towards decentralized energy generation, particularly evident at the final consumer level, is influencing the electric industry. Although this transformation is more tailored to the needs of individual prosumers and microgrids than large consumers and industries, it remains a significant element in the evolving landscape of energy systems, where prosumers can obtain financial benefits through specific strategies. However, existing literature largely perceives prosumers as being linked to photovoltaic-home-battery energy storage systems using the average historical time-interval load profiles of various countries without analyzing load patterns. Moreover, the existing literature shows that deterministic approaches are unsuitable for analyzing load patterns due to possible biased results. This study identifies and analyzes load patterns through clustering and demand curve analysis, using K-means clustering and Gaussian mixture modeling techniques to address limitations. New strategies of prosumer dispatch models for six load patterns are proposed for improved revenue generation from excess energy: (1) dispatch model with optimum self-consumption and (2) dispatch model with a peak-shaving algorithm. Model (1) provides moderate profits, whereas model (2) provides a higher profit and self-sufficiency rate. The model proposed by this study optimizes energy usage, while self-consumption is shown to vary considerably between load patterns.