| Abstract |
Energy consumption, which works as the physical engine of economic development, significantly influences the environment; using renewable energy, which originates from naturally filled resources, helps mitigate these adverse effects. The high price of fossil fuels, carbon dioxide emissions, and electricity generation are the most difficult aspects of this kind of renewable energy, which is often regarded as one of the main factors holding back economic progress. An artificial neural network-enabled economic growth model (ANN-EGM) has been constructed in this research to predict the restraining and pushing energy variables that impede economic growth. ANN-EGM optimizes the limiting and driving energy forces, which helps to improve the use of renewable energy and assist the economy's growth. The prominent enhancement in driving economic activity and employment rates may result in cost-effective improvement for the effective production of energy from renewable resources for green computing. The proposed article constructs an ANN-EGM and examines its topological structure and the effect of training errors the network allows on its performance to address issues in green computing technology and sustainable social and economic development. Conventional approaches based on liming and driving energy forces are predicted to be less successful than an ANN-EGM in predicting the increase of the renewable energy industry for green computing and its correlation with quicker economic growth. The study's findings suggest that the ANN-EGM can accurately forecast and verify the limiting and driving factors in renewable energy generation. The experimental outcome demonstrates that the proposed ANN-EGM model increases the prediction ratio by 85.6% and the performance ratio by 86.4% and has a reduced mean square error rate of 10.1% compared to other existing methods. |
