Two new machine learning-based models for forecasting the power generated by solar projects have been developed by researchers led by Dr Kalop of Urban Environmental Engineering and Professor Heo Jong-wan of Incheon National University in South Korea. The advanced models incorporating artificial intelligence, dubbed the adaptive neuro-fuzzy inference system (ANFIS), efficiently forecast the power generated by photovoltaic systems up to a full day ahead of time. Renewable and Sustainable Energy Reviews published the research paper. Integrating solar photovoltaic (PV) power into existing power grids is a difficult task because PV systems' power output is heavily influenced by environmental factors. According to the researchers, an accurate forecast of solar PV power generation is required for efficient power integration into existing power grids. The researchers combined two models with adaptive and time-varying acceleration coefficients: ANFIS and particle swarm optimization (PSO). According to the researchers, the two models are described as hybrid algorithms because they combine a novel hybrid approach of adaptive swarm intelligence techniques and ANFIS in forecasting solar PV project power generation over time horizons ranging from 0 to 24 hours. The models were designed and evaluated using climatic variables and historical PV power data from a 960 kW grid-connected PV system in south Italy. To assess the accuracy of the proposed models and the impact of variables on PV power values, several statistical analyses were conducted. At time horizons of 12 hours and 24 hours, the proposed ANFIS-APSO achieved the most accurate PV power forecast with R2 = 0.657 and 0.835, RMSE = 0.081 kW and 0.088 kW, and MAE = 0.079 kW and 0.077 kW , respectively. The newly constructed ANFIS-APSO outperformed the standard ANFIS-PSO model, as well as other hybrid models, according to the findings. According to the findings, the model could be a promising new tool for engineers to use in forecasting the power generation of solar projects over short and long time horizons. Renewable energy project developers face a difficult task in accurately forecasting power generation. To integrate renewable generation into the grid efficiently, utilities demand accurate forecasting and scheduling. Renewable energy forecasting technologies are in high demand from utilities as well as renewable energy generators. Image Source