Optimizing Generative AI Chatbots for Net-Zero Emissions Energy Internet-of-Things Infrastructure

Abstract

Internet-of-Things (IoT) technologies have been steadily adopted and embedded into energy infrastructure following the rapid transformation of energy grids through distributed consumption, renewables generation, and battery storage. The data streams produced by such energy IoT infrastructure can be extracted, processed, analyzed, and synthesized for informed decision-making that delivers optimized grid operations, reduced costs, and net-zero carbon emissions. However, the voluminous nature of such data streams leads to an equally large number of analysis outcomes that have proven ineffective in decision-making by energy grid operators. This gap can be addressed by introducing artificial intelligence (AI) chatbots, or more formally conversational agents, to proactively assist human operators in analyzing and identifying decision opportunities in energy grids. In this research, we draw upon the recent success of generative AI for optimized AI chatbots with natural language understanding and generation capabilities for the complex information needs of energy IoT infrastructure and net-zero emissions. The proposed approach for optimized generative AI chatbots is composed of six core modules: Intent Classifier, Knowledge Extractor, Database Retriever, Cached Hierarchical Vector Storage, Secure Prompting, and Conversational Interface with Language Generator. We empirically evaluate the proposed approach and the optimized generative AI chatbot in the real-world setting of an energy IoT infrastructure deployed at a large, multi-campus tertiary education institution. The results of these experiments confirm the contribution of generative AI chatbots in simplifying the complexity of energy IoT infrastructure for optimized grid operations and net-zero carbon emissions.

The Human-Centred Design of a Universal Module for Artificial Intelligence Literacy in Tertiary Education Institutions

“…The first generation of models were trained on image data and specialised in computer vision-related tasks, such as face detection, object detection [30], object localisation, and image captioning, followed by the second generation focusing on language and text. Commonly known as Large Language Models (LLMs), these models have found a wider audience due to the simplicity of interaction through conversation, as demonstrated by the success of ChatGPT [13,31]. The combination of images and text has led to the further development of multimodal models and specialised scientific, programming, and robotics models.…”