02685nas a2200241   4500000000100000000000100001008004100002260001200043653001800055653002200073653002700095653003400122653001900156100001800175700001600193700001700209245010500226856005800331300000900389490001300398520201800411022001402429       9998                            d  c02/202510aDeep Learning10aEnergy Prediction10aLong Short-Term Memory10aNext-Gen Consumer Electronics10aSustainability1 aGwanggil Jeon1 aImran Ahmed1 aSanghoon Han00aA Sustainable Deep Learning Paradigm for Reliable Energy Prediction in Next-Gen Consumer Electronics  uhttps://www.ijimai.org/journal/bibcite/reference/3541  a1-110 vIn press3 aIn the rapidly evolving consumer electronics landscape, the imperative for sustainable energy solutions necessitates the development of accurate energy prediction methodologies. Traditional energy prediction models often fall short in accounting for the dynamic characteristics of renewable energy sources, particularly wind and solar. This limitation is pronounced in consumer electronics, where precise energy forecasting is pivotal for achieving optimal device performance and energy efficiency. To address this gap, we present a sustainable deep learning paradigm using Long Short-Term Memory (LSTM) networks to capture the complex temporal patterns inherent in renewable energy data. This paper introduces a novel and sustainable deep learning approach that significantly enhances energy prediction accuracy within the context of next-generation consumer electronics. By leveraging the capabilities of an LSTM-based model, we utilize an extensive dataset comprising hourly records of wind and solar energy production from the French grid since 2020. Our research addresses the inherent challenges in precise energy prediction, a cornerstone for efficient energy management and consumption optimization in emerging technology ecosystems. Through comprehensive data preprocessing, feature engineering, and rigorous training, the LSTM model demonstrates exceptional proficiency, achieving an impressive 82% accuracy in predicting energy production. This underscores its efficacy in capturing intricate temporal relationships and patterns within renewable energy data, facilitating its integration into next-generation consumer electronics. Our proposed paradigm addresses a critical need and paves the way for a future where accurate energy prediction fuels eco-conscious technology. In conclusion, this study contributes to a more sustainable energy landscape by advancing the development of reliable and efficient energy prediction methodologies for the evolving realm of next-generation consumer electronics.  a1989-1660