Short-term solar power forecasting- An approach using JAYA based recurrent network model

Solar power generation is variable and weather-dependent, making accurate forecasting essential for power system operation and planning. In this work, we use multiple deep neural network models and different weather parameters to forecast solar power generation. We compare the performance of the distinct models and test the anticipated forecast model using 100 kW solar data from a specific region in Asia in the year 2021. Here, the proposed work uses a deep recurrent network model that is Long Short Memory Network (LSMN) for solar power forecasting. In this work, we used the JAYA algorithm for hyperparameters optimization to demonstrate their forecast strength related to standard analysis. The forecasted results using JAYA-based LSMN show superior predicting performance compared to the conventional techniques.

Automated summary

In this study, multiple deep neural network models and different weather parameters are used to forecast solar power generation. Through comparing the performance of these models and testing with actual data, it is found that the forecast model based on JAYA-based LSMN demonstrates superior predicting performance compared to conventional techniques.