Driving sustainable energy transitions with a multi-source RAG-LLM system

Arslan, Muhammad; Mahdjoubi, Lamine; Munawar, Saba

Abstract

By 2035, the UK aims to upgrade all homes to achieve a net-zero economy by 2050, thereby reducing energy consumption, household costs, and improving living conditions. Small and Medium-sized Enterprises (SMEs) play a crucial role in this transition. However, many SME contractors lack essential information on Sustainable
Energy Initiatives (SEIs) and the relevant Energy landscape necessary for driving Sustainable Energy Transitions (SETs). This knowledge gap poses risks to SME interventions, potentially leading to increased costs and inefficiencies. Accessing timely information on SEIs including government policies, funding, technologies, and environmental impacts from various media sources is essential for guiding effective SETs and understanding the relevant Energy landscape, thereby facilitating informed decision-making. Currently, SMEs lack an integrated system that consolidates data from diverse media sources into a centralized Information System (IS), limiting
their ability to effectively navigate SEIs. To address this gap, this research introduces an Energy Chatbot, a sustainable IS that utilizes Large Language Models (LLMs) integrated with multi-source Retrieval Augmented Generation (RAG). This system encompasses diverse media sources, including news articles, government reports,
industry publications, academic research, and social media. The Energy Chatbot is designed to enhance decision making for SMEs by providing comprehensive Energy sector insights through a Question Answering (QA) system. Key findings emphasize that this approach reduces costs by utilizing open-source models. Moreover, the Energy
Chatbot provides SMEs with access to up-to-date information, enabling them to identify long-term sustainability strategies and maintain a competitive edge in the evolving Energy landscape.

• Journal Article Type: Article
• Acceptance Date: Sep 19, 2024
• Online Publication Date: Oct 11, 2024
• Publication Date: Dec 1, 2024
• Deposit Date: Jan 18, 2025
• Publicly Available Date: Jan 21, 2025
• Journal: Energy and Buildings
• Print ISSN: 0378-7788
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 324
• Article Number: 114827
• DOI: https://doi.org/10.1016/j.enbuild.2024.114827
• Public URL: https://uwe-repository.worktribe.com/output/13625587
• Additional Information: This article is maintained by: Elsevier; Article Title: Driving sustainable energy transitions with a multi-source RAG-LLM system; Journal Title: Energy and Buildings; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.enbuild.2024.114827; Content Type: article; Copyright: © 2024 The Author(s). Published by Elsevier B.V.