Solar energy is one of the key sources of green energy because it is free of emissions, pure, limitless, and plentiful. Solar power has gained tremendous interest across the globe, and one of its key duties is to produce as much solar power as possible in varying weather conditions. Photovoltaic generation's intermittent and unregulated characteristics have a major effect on power stability. To minimize these factors, the predictability of photovoltaic generation needs to be increased. As photovoltaics are increasingly evolving, the share of PV generation in the electricity trading industry is rising. However, the model's accuracy is often low in conventional modeling because of unnecessary initial data noise or inappropriate parameter configuration. The key factor analysis in this article and the K-means algorithm are clustered and the random forest (RF) algorithm. Core components and K-means are used to get the hourly points' characteristics close to the projected time points, and then the input data are filtered to mitigate interaction with the noise data. © 2022 Elsevier B.V., All rights reserved.