AI-Driven Text Analysis and Generation for Green Energy Applications - Ahmed, Saif Saad; Mahdi, Mohammed Fadhil; Hammad, Qudama Khamis; Mahdi, Ammar Falih; Alfalahi, Saad.T.Y.; Maidin, Siti Sarah (Malikussaleh University, Aceh, Indonesia, 2025)
The rapid growth of the green energy sector has produced a massive volume of textual data, creating significant challenges for information extraction and decision support. This study investigates the application of state-of-the-art Natural Language Processing (NLP) models, specifically BERT and GPT-4, to automate and enhance policy drafting, market analysis, and academic research clustering. We evaluated these models on a corpus of over 200,000 energy-related documents, using a structured computational workflow to measure performance on semantic coherence, factual reliability, and processing efficiency. The results demonstrate substantial improvements over manual methods. The AI-driven approach reduced policy drafting time by 39% and error rates by over 58%, while increasing semantic alignment to 93.5%. In market report synthesis, the models improved topic extraction accuracy by over 10% and reduced summary generation time by 38%. For academic literature, thematic clustering accuracy reached 92.3%, with a 44% reduction in processing time. These findings validate that fine-tuned NLP models can serve as powerful analytical tools in the sustainable energy domain, enabling institutions to navigate complex regulatory and technical information more effectively. By providing a practical demonstration of how automated NLP solutions can augment human expertise, this work contributes to the applied use of AI in achieving global green energy objectives, while also considering the associated methodological and ethical implications.