Entropy Binary Dragonfly Algorithm (EBDA) Based Feature Selection and Stacking Ensemble Model for Renewable Energy Demand (RED) Forecasting and Weather Prediction

Wind speed, solar radiation, and weather conditions are famous and extensively used RE sources in the global. As a result of their high carbon content and the processes used to produce them, fossil fuels like coal, natural gas, and petroleum cannot be replenished and are therefore not considered renewable energy sources. Demand forecasting heavily depends on irregular renewable sources, whose production is weather-dependent. It was carried out using Machine Learning (ML) techniques. However higher computational complexity and incapability are major important issues of ML methods. This study proposes a new algorithm to use weather forecasts and data on consumption and generation to generate energy demand. Utilizing a model that extends beyond the upcoming day-ahead auction, hourly electricity price forecasting is done. Initially data normalization is used to pre-process the dataset. Then, Entropy Binary Dragonfly Algorithm (EBDA) was introduced to select the most important features at the same time as enhancing the prediction accuracy. Finally, the Optimized Stacking Hermite Polynomial Neural Network Ensemble (OSHPNNE) model is introduced for RED forecasting. HPNN parameters are optimized using EBDA to increase prediction accuracy and enhance classification capacity. Kaggle is used to collect hourly energy demand generation and weather datasets, which have been employed in experiments. Determining the electrical components by extrapolating them based on the influence of weather forecasts on their time, location, and climate. Metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson Correlation Coefficient (r), and Nash Sutcliffe Efficiency (NSE) have been used to assess the results of forecasting approaches.