# Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles

arXiv: https://arxiv.org/abs/2501.15544 · HTML: https://mlsanity.com/paper/2501.15544/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 84 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
- [A Survey on Retrieval-Augmented Text Generation for Large Language Models](https://mlsanity.com/paper/2404.10981/) (2024)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Interactive AI with Retrieval-Augmented Generation for Next Generation Networking](https://mlsanity.com/paper/2401.11391/) (2024)
- [GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching](https://mlsanity.com/paper/2411.05276/) (2024)
- [Warm Start of Mixed-Integer Programs for Model Predictive Control of Hybrid Systems](https://mlsanity.com/paper/1910.08251/) (2019)
- [From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks](https://mlsanity.com/paper/2310.18382/) (2023)
- [Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning](https://mlsanity.com/paper/2305.08885/) (2023)
- [6G comprehensive intelligence: network operations and optimization based on Large Language Models](https://mlsanity.com/paper/2404.18373/) (2024)
- [Can Large Language Models Solve Robot Routing?](https://mlsanity.com/paper/2403.10795/) (2024)
- [MSADM: Large Language Model (LLM) Assisted End-to-End Network Health Management Based on Multi-Scale Semanticization](https://mlsanity.com/paper/2406.08305/) (2024)
- [Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models](https://mlsanity.com/paper/2404.18077/) (2024)
- [Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach](https://mlsanity.com/paper/2408.05233/) (2024)
- [AutoIoT: Automated IoT Platform Using Large Language Models](https://mlsanity.com/paper/2411.10665/) (2024)
- [Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey](https://mlsanity.com/paper/2101.08013/) (2021)
- [Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study](https://mlsanity.com/paper/2409.09343/) (2024)

## Built on by (most cited first, 2 of 2)

- [Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling](https://mlsanity.com/paper/2510.26603/) (2025)
- [An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing](https://mlsanity.com/paper/2605.13221/) (2026)

## Abstract (arXiv)

The energy optimization and demand side management (DSM) of Internet of Things (IoT)-enabled microgrids are being transformed by generative artificial intelligence, such as large language models (LLMs). This paper explores the integration of LLMs into energy management, and emphasizes their roles in automating the optimization of DSM strategies with Internet of Electric Vehicles (IoEV) as a representative example of the Internet of Vehicles (IoV). We investigate challenges and solutions associated with DSM and explore the new opportunities presented by leveraging LLMs. Then, we propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. The results demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles, and highlight our solution's significant advancements in energy efficiency and user adaptability. This work shows LLMs' potential in energy optimization of the IoT-enabled microgrids and promotes intelligent DSM solutions.