Photovoltaic power plant outputs depend on the daily global solar radiation (DGSR). The main issue with DGSR data is its lack of precision. The potential unavailability of DGSR data for several sites can be attributed to the high cost of measuring instruments and the intermittent nature of time series data due to equipment malfunctions. Therefore, DGSR prediction research is crucial nowadays to produce photovoltaic power. Different artificial neural network (ANN) models will give different DGSR predictions with varying levels of accuracy, so it is essential to compare the different ANN model inputs with various sets of meteorological stochastic variables. In this study, radial basis function neural network (RBFNN), long short-term memory neural network (LSTMNN), modular neural network (MNN), and transformer model (TM) are developed to investigate the performances of these algorithms for the DGSR prediction using different combinations of meteorological stochastic variables. These models employ five stochastic variables: wind speed, relative humidity, minimum, maximum, and average temperatures. The mean absolute relative error for the transformer model with input variables as average, maximum, and minimum temperatures is 1.98. ANN models outperform traditional models in predictive accuracy.

Wind energy production is rapidly expanding worldwide, yet studies on wind energy potential in India remain limited. This study evaluates the wind power potential and conducts an economic cost analysis of wind turbine generator installations at varying hub heights (10m to 150 m) across 21 locations in India, representing a novel contribution to the field. The selected locations include 11 sites in Gujarat (Location-1), 10 sites in Tamil Nadu (Location-2), and one site in Ravangla, Sikkim (Location-3). Cubic factors methods are implemented to estimate Weibull parameters. Results reveal that at 150 m hub height, wind power density ranges from 123.17 to 308.86 W/m² in Gujarat, 80.64 to 427.12 W/m² in Tamil Nadu, and 183.24 W/m² in Sikkim. Kaluneerkulam in Tamil Nadu demonstrates excellent wind category potential, with energy costs ranging from $0.0165 to $0.0076 per kWh, decreasing as hub height increases. Sites across all three locations exhibit moderate to steady wind speeds, making them suitable for wind energy exploitation. An economic analysis of nine wind turbine types shows that Tamil Nadu achieves the lowest energy cost variation, followed by Gujarat and Sikkim. This study provides valuable insights for optimizing wind energy utilization in India.