Active Inference for Energy Control and Planning in Smart Buildings and Communities

• URL: http://arxiv.org/abs/2503.18161v1
• Date: Sun, 23 Mar 2025 18:03:01 GMT
• Title: Active Inference for Energy Control and Planning in Smart Buildings and Communities
• Abstract summary: Active Inference (AIF) is emerging as a powerful framework for decision-making under uncertainty.<n>We propose a novel dual-layer AIF architecture that addresses both building-level and community-level energy management.<n>This study is the first to show how a distributed AIF works in engineering.
• Score: 4.458307330781945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Active Inference (AIF) is emerging as a powerful framework for decision-making under uncertainty, yet its potential in engineering applications remains largely unexplored. In this work, we propose a novel dual-layer AIF architecture that addresses both building-level and community-level energy management. By leveraging the free energy principle, each layer adapts to evolving conditions and handles partial observability without extensive sensor information and respecting data privacy. We validate the continuous AIF model against both a perfect optimization baseline and a reinforcement learning-based approach. We also test the community AIF framework under extreme pricing scenarios. The results highlight the model's robustness in handling abrupt changes. This study is the first to show how a distributed AIF works in engineering. It also highlights new opportunities for privacy-preserving and uncertainty-aware control strategies in engineering applications.

Related papers

• MESA: Improving MoE Safety Alignment via Decentralized Expertise [55.297863000560596]
  MESA (MoE Safety Alignment) is a targeted alignment framework for MoE-based Large Language Models.<n>It strategically decentralizes safety responsibility to maximize coverage while minimizing interference with utility.<n>MESA achieves robust defensive performance against varied harmful benchmarks while preserving helpfulness.
  arXiv  Detail & Related papers  (2026-05-30T09:54:38Z)
• FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory [11.729108742234416]
  In resource-constrained environments, a well-designed forgetting mechanism is as crucial as remembering, delivering benefits across three dimensions.<n>Our framework establishes a taxonomy of forgetting mechanisms: passive decay-based, active deletion-based, safety-triggered, and adaptive reinforcement-based.<n>Results show significant improvements: access efficiency (+8.49%), content quality (+29.2% signal-to-noise ratio), and security performance (100% elimination of security risks)
  arXiv  Detail & Related papers  (2026-04-22T07:55:22Z)
• Active Inference for Physical AI Agents -- An Engineering Perspective [1.690447548344545]
  This paper argues that Active Inference (AIF) offers a principled foundation for closing that gap.<n>We show that AIF operationalizes VFE minimization by unifying perception, learning, planning, and control within a single computational objective.<n>Because reactive message passing is event-driven, interruptible, and locally adaptable, performance degrades gracefully under reduced resources.
  arXiv  Detail & Related papers  (2026-03-21T20:06:30Z)
• On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning [1.2599533416395765]
  Energy Demand Forecasting of Electric Vehicle Supply Equipments (EVSEs) is one of the most critical operations for ensuring efficient energy management and sustainability.<n>It enables utility providers to anticipate energy/power demand, optimize resource allocation, and implement proactive measures to improve grid reliability.<n>As concerns and restrictions about privacy and sustainability have grown, training data has become increasingly fragmented.
  arXiv  Detail & Related papers  (2026-02-24T11:21:45Z)
• Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy [64.39232788946173]
  Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth.<n>This paper investigates ensemble learning for secure LAE, covering research focuses, solutions, and a case study.
  arXiv  Detail & Related papers  (2026-02-07T23:15:58Z)
• Agentic AI Reasoning for Mobile Edge General Intelligence: Fundamentals, Approaches, and Directions [74.35421055079655]
  Large language models (LLMs) have enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.<n>Mobile Edge General Intelligence (MEGI) brings real-time, privacy-preserving reasoning to the network edge.<n>We propose a joint optimization framework for efficient LLM reasoning deployment in MEGI.
  arXiv  Detail & Related papers  (2025-09-27T10:53:48Z)
• Reinforced Reasoning for Embodied Planning [18.40186665383579]
  Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and natural language goals.<n>We introduce a reinforcement fine-tuning framework that brings R1-style reasoning enhancement into embodied planning.
  arXiv  Detail & Related papers  (2025-05-28T07:21:37Z)
• Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy [88.8665000676562]
  Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks.<n>To mitigate data scarcity, we introduce a novel simulation framework and benchmark grounded in reduced-order dynamics.<n>We propose Dynamics Informed Diffusion Policy (DIDP), a framework that integrates imitation pretraining with physics-informed test-time adaptation.
  arXiv  Detail & Related papers  (2025-05-23T03:28:25Z)
• Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control [0.6990493129893112]
  Industrial automation increasingly demands energy-efficient control strategies to balance performance with environmental and cost constraints.<n>We present a multi-objective reinforcement learning (MORL) framework for energy-efficient control of the Quanser Aero 2 testbed in its one-degree-of-freedom.<n>Preliminary experiments explore the influence of varying the Energy penalty weight, alpha, on the trade-off between pitch tracking and energy savings.
  arXiv  Detail & Related papers  (2025-05-12T14:28:42Z)
• Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding [49.973156959947346]
  Existing Video Temporal Grounding (VTG) models excel in accuracy but often overlook open-world challenges posed by open-vocabulary queries and untrimmed videos. We introduce a robust network module that benefits from a two-stage cross-modal alignment task. It integrates Deep Evidential Regression (DER) to explicitly and thoroughly quantify uncertainty during training. In response, we develop a simple yet effective Geom-regularizer that enhances the uncertainty learning framework from the ground up.
  arXiv  Detail & Related papers  (2024-08-29T05:32:03Z)
• On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study [7.0723456126359245]
  This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications. A case study is presented featuring a vehicle classification application using acoustic and seismic sensing.
  arXiv  Detail & Related papers  (2024-04-03T05:04:06Z)
• Generalized Policy Learning for Smart Grids: FL TRPO Approach [6.058785372434129]
  Federated Learning (FL) can train models on heterogeneous datasets while maintaining data privacy. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs.
  arXiv  Detail & Related papers  (2024-03-27T10:47:06Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning [0.0]
  Incomplete knowledge of the environment leads an agent to make decisions under uncertainty. One of the major dilemmas in Reinforcement Learning (RL) where an autonomous agent has to balance two contrasting needs in making its decisions. We show that adaptive methods better approximate the trade-off between exploration and exploitation.
  arXiv  Detail & Related papers  (2023-10-12T13:45:33Z)
• Energy-frugal and Interpretable AI Hardware Design using Learning Automata [5.514795777097036]
  A new machine learning algorithm, called the Tsetlin machine, has been proposed. In this paper, we investigate methods of energy-frugal artificial intelligence hardware design. We show that frugal resource allocation can provide decisive energy reduction while also achieving robust and interpretable learning.
  arXiv  Detail & Related papers  (2023-05-19T15:11:18Z)
• Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion [17.079430640475962]
  The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization. Among all Internet of Things (IoT)-based communication technologies, Bluetooth Low Energy (BLE) plays a vital role in city-wide decision making and services.
  arXiv  Detail & Related papers  (2022-10-27T02:38:25Z)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization [57.88118988775461]
  We propose PROjected Feasibility (PROF) to enforce convex operational constraints within neural policies. We demonstrate PROF on two applications: energy-efficient building operation and inverter control.
  arXiv  Detail & Related papers  (2021-05-19T01:58:10Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)
• Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework [30.58186749790728]
  We propose a theoretically-justified approach to maintaining safety in the presence of dynamics uncertainty. Our framework guarantees the high-probability satisfaction of all constraints at all times jointly. This theoretical analysis also motivates two regularizers of last-layer meta-learning models that improve online adaptation capabilities.
  arXiv  Detail & Related papers  (2020-08-26T17:39:58Z)

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.