About Analysis of solar photovoltaic power generation output
Accurate forecasting of PV output power can help in planning and scheduling of power dispatch, improving system reliability and power quality, and reducing the impact of uncertainty of PV power generation. There are many research works that forecast PV output power for various time resolutions.
Accurate forecasting of PV output power can help in planning and scheduling of power dispatch, improving system reliability and power quality, and reducing the impact of uncertainty of PV power generation. There are many research works that forecast PV output power for various time resolutions.
Prediction of PV power output on an hourly basis is done using a hybrid model based on customized similar day analysis (SDA), genetic algorithm (GA), and extreme learning machine (ELM) in . Radial basis function (RBF) and genetic algorithm programming system (GAPS) were proposed for improving prediction accuracy [ 135 ].
Utilizing monthly input and output data, including four inputs (solar irradiation, temperature, number of modules, and PV array rated capacity) and one output (electricity generation) from utility-scale PV power plants, meta-frontier data envelopment analysis was employed in this study to identify factors contributing to power generation .
Given a historical data set on weather and PV solar power output, this research set out to identify the best ML algorithm for the forecasting of PV solar power output given recent promise in the DT, XGB, and LSTM algorithms shown by new research (Carrera & Kim, Citation 2020; Harrou et al., Citation 2020; Konstantinou et al., Citation 2021 .
Dimd et al. presented a comprehensive review of ML techniques employed for solar PV power generation forecasting, specifically focusing on the unique climate of the Nordic region, which is characterized by cold weather and snow. They studied how meteorological factors and the effects of snow on PV panels impact the performance of these .
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