Recently, wireless sensing nodes are being integrated with ambient energy harvesting capability to overcome limited battery power budget constraint and extending effective operational time of sensor network. Solar panels are more frequently used to collect light energy for wireless sensing node. In order to efficiently utilize solar harvested energy in design, precise solar harvested energy prediction is a challenging task due to irregularity in solar energy patterens because of continually changing weather conditions. In this paper, we are presenting efficient algorithm for solar energy prediction based on additive decomposition (SEPAD) model. In this model, we are individually considering both seasonal and daily trends along with Sun's diurnal cycle. The performance of this algorithm is compared with existing solar energy prediction approaches and results show that our algorithm performance is better than existing approaches.