LSTM-Based Renewable Energy Forecasting for Intelligent Grid Performance Optimization
Abstract
The use of renewable energy in sustainable power production is becoming ever more vital; yet, the unpredictability of renewable sources poses difficulties regarding grid stability and optimal energy use. Solar radiation, wind speed, temperature, and various other meteorological parameters contribute to the unpredictability of renewable energy production, making predictions difficult and affecting load balancing techniques. Predictive models currently available suffer from shortcomings in recognizing the temporal correlations in data related to energy production and the weather, resulting in predictive mistakes and ineffective usage of renewable energy sources. This paper aims to propose a renewable energy forecasting framework based on Transformer architecture using past generation data and meteorological variables to recognize long-range temporal correlations. A Renewable Integrated Forecast Dataset (RIFD) has been synthesized by considering data of renewable energy generation, weather, and grid demand information for the training and evaluation of models. Analysis showed that there are fewer forecast errors and better performance by using MAE, RMSE, MAPE, and R2 evaluation measures compared to traditional forecasting methods. Precise forecasts help in efficient grid management by providing better load balancing, energy scheduling, and usage of renewable energy sources. This work will lead towards effective integration of renewable energy through the precise prediction of renewable energy generation.