Optimization of solar and wind power plants production through a parallel fusion approach with modified hybrid machine and deep learning models

Abstract

Artificial Intelligence (AI) is becoming increasingly indispensable across diverse domains as technology rapidly advances. As traditional energy sources dwindle, there's a noticeable pivot towards renewable energy sources (RES). However, to effectively meet energy demands, integrating these RES into smart grids to bolster efficiency is imperative. Despite the transition, ongoing technical challenges persist, specifically in accurately predicting and optimizing smart grid parameters. To tackle these hurdles and enhance smart grid efficiency, various AI techniques are being harnessed. This study leverages real-time energy generation data (MWh) from solar and wind plants over a year, dependent on parameters such as POA and wind speed, respectively. Prediction outcomes are derived using three machine learning (ML) models (XGBoost, CatBoost, and LightGBM) and three deep learning (DL) models (LSTM, BiLSTM, and GRU). From these individual models, two hybrid ML and DL models are developed, yielding promising results. Subsequently, these outcomes are further refined through a parallel fusion approach (PFA), resulting in heightened accuracy and reliability. The implementation of this technique notably reduces error rates to 15.05% for hybrid ML, 19.18% for hybrid DL, and 8.1432% for PFA. This methodology holds substantial potential for future research endeavors, supplementing existing AI models for enhanced efficiency.