TY  - EJOU
AU  - Yan, Tao 
AU  - Rashid, Javed 
AU  - Saleem, Muhammad Shoaib 
AU  - Ahmad, Sajjad 
AU  - Faheem, Muhammad 

TI  - A Hybrid Deep Learning Approach for Green Energy Forecasting in Asian Countries
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 2
SN  - 1546-2226

AB  - Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and feedforward neural networks (FNNs). The model promises to improve prediction accuracy. The 1965–2023 dataset covers green energy generation statistics from ten Asian countries. Due to the rising energy supply-demand mismatch, the primary goal is to develop the best model for predicting future power production. The GP-Ensemble deep learning model outperforms individual models (GRU, FNN, and CNN) and alternative approaches such as fully convolutional networks (FCN) and other ensemble models in mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE) metrics. This study enhances our ability to predict green electricity production over time, with MSE of 0.0631, MAE of 0.1754, and RMSE of 0.2383. It may influence laws and enhance energy management.
KW  - Green energy; advanced predictive techniques; convolutional neural networks (CNNs); gated recurrent units (GRUs); deep learning for electricity prediction; green-electrical production ensemble technique

DO  - 10.32604/cmc.2024.058186