Large Language Model Interface for Home Energy Management Systems

• URL: http://arxiv.org/abs/2501.07919v1
• Date: Tue, 14 Jan 2025 08:10:43 GMT
• Title: Large Language Model Interface for Home Energy Management Systems
• Authors: François Michelon, Yihong Zhou, Thomas Morstyn,
• Abstract summary: Home Energy Management Systems (HEMSs) help households tailor their electricity usage based on power system signals such as energy prices.<n>HEMSs require well-formatted parameterization that reflects the characteristics of the energy resources, houses, and users' needs.<n>We propose an interface that interacts with users to understand and parameterize their badly-formatted answers'', and then outputs well-formatted parameters to implement an HEMS.
• Score: 0.7373617024876725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Home Energy Management Systems (HEMSs) help households tailor their electricity usage based on power system signals such as energy prices. This technology helps to reduce energy bills and offers greater demand-side flexibility that supports the power system stability. However, residents who lack a technical background may find it difficult to use HEMSs effectively, because HEMSs require well-formatted parameterization that reflects the characteristics of the energy resources, houses, and users' needs. Recently, Large-Language Models (LLMs) have demonstrated an outstanding ability in language understanding. Motivated by this, we propose an LLM-based interface that interacts with users to understand and parameterize their ``badly-formatted answers'', and then outputs well-formatted parameters to implement an HEMS. We further use Reason and Act method (ReAct) and few-shot prompting to enhance the LLM performance. Evaluating the interface performance requires multiple user--LLM interactions. To avoid the efforts in finding volunteer users and reduce the evaluation time, we additionally propose a method that uses another LLM to simulate users with varying expertise, ranging from knowledgeable to non-technical. By comprehensive evaluation, the proposed LLM-based HEMS interface achieves an average parameter retrieval accuracy of 88\%, outperforming benchmark models without ReAct and/or few-shot prompting.

Related papers

• Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning [0.0]
  Home Energy Management Systems (HEMS) have emerged as a pivotal tool in the smart home ecosystem.<n>This paper introduces a multi-mode Deep Reinforcement Learning-based HEMS framework, meticulously designed to optimize based on dynamic, consumer-defined preferences.<n>Our results show that the model performs exceptionally well in optimizing energy consumption within different preference modes.
  arXiv  Detail & Related papers  (2025-05-02T15:05:29Z)
• Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency [6.306413686006502]
  We conduct a comprehensive analysis of 28 quantized Large Language Models (LLMs) from the Ollama library. We evaluate energy efficiency, inference performance, and output accuracy across multiple quantization levels and task types. Our findings reveal the trade-offs between energy efficiency, inference speed, and accuracy in different quantization settings.
  arXiv  Detail & Related papers  (2025-04-04T11:29:30Z)
• Towards Green AI-Native Networks: Evaluation of Neural Circuit Policy for Estimating Energy Consumption of Base Stations [5.466248014150832]
  Optimization of radio hardware and AI-based network management software yield significant energy savings in radio access networks. executing underlying Machine Learning (ML) models may require additional compute and energy. This work evaluates the novel use of sparsely structured Neural Circuit Policies (NCPs) in a use case to estimate the energy consumption of base stations.
  arXiv  Detail & Related papers  (2025-04-03T17:22:39Z)
• Energy-Aware LLMs: A step towards sustainable AI for downstream applications [0.9012198585960441]
  Advanced Large Language Models (LLMs) have revolutionized various fields, including communication networks. LLMs typically require huge computational resources, resulting in terribly high energy consumption. This research study proposes an end-to-end pipeline that investigates the trade-off between energy efficiency and model performance.
  arXiv  Detail & Related papers  (2025-03-22T14:28:29Z)
• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge. Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles [52.43886862287498]
  This paper explores the integration of large language models (LLMs) into energy management. We propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. We present a case study to demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles.
  arXiv  Detail & Related papers  (2025-01-26T14:31:03Z)
• Investigating Energy Efficiency and Performance Trade-offs in LLM Inference Across Tasks and DVFS Settings [1.5749416770494706]
  Large language models (LLMs) have shown significant improvements in many natural language processing (NLP) tasks.<n>LLMs are resource-intensive, requiring extensive computational resources both during training and inference.<n>As their adoption accelerates, the sustainability of LLMs has become a critical issue.
  arXiv  Detail & Related papers  (2025-01-14T16:02:33Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
  We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications. FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
  arXiv  Detail & Related papers  (2024-08-15T16:45:16Z)
• DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency [7.073435885680335]
  We propose DynamoLLM, the first energy-management framework for generative large language models. At a service-level, DynamoLLM conserves 53% energy and 38% operational carbon emissions, and reduces 61% cost to the customer.
  arXiv  Detail & Related papers  (2024-08-01T17:40:45Z)
• Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads [0.2389598109913753]
  Training and using Large Language Models (LLMs) require large amounts of energy. This paper addresses the challenge of reducing energy consumption in data centers running LLMs. We propose a hybrid data center model that uses a cost-based scheduling framework to dynamically allocate tasks across hardware accelerators.
  arXiv  Detail & Related papers  (2024-04-25T11:24:08Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability [27.70596933019959]
  This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs) We put forward a strategy that augments an LLM with an optimization solver, enhancing its proficiency in understanding and responding to user specifications and preferences. Our approach pioneers the novel concept of human-guided optimization autoformalism, translating a natural language task specification automatically into an optimization instance.
  arXiv  Detail & Related papers  (2023-08-20T22:42:04Z)
• Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System [65.93577256431125]
  We propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller task-oriented dialogue model. This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models. Our approach outperforms previous state-of-the-art (SOTA) results.
  arXiv  Detail & Related papers  (2023-06-16T13:04:56Z)
• Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
  We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM) NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.