BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics

• URL: http://arxiv.org/abs/2412.14175v1
• Date: Wed, 20 Nov 2024 23:49:06 GMT
• Title: BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics
• Authors: Xiachong Lin, Arian Prabowo, Imran Razzak, Hao Xue, Matthew Amos, Sam Behrens, Flora D. Salim,
• Abstract summary: This paper presents the design of an interactive visualization tool, BiTSA. The tool enables building managers to interpret complex energy data quickly and take immediate, data-driven actions based on real-time insights.
• Score: 15.525789412274587
• License:
• Abstract: Incorporating AI technologies into digital infrastructure offers transformative potential for energy management, particularly in enhancing energy efficiency and supporting net-zero objectives. However, the complexity of IoT-generated datasets often poses a significant challenge, hindering the translation of research insights into practical, real-world applications. This paper presents the design of an interactive visualization tool, BiTSA. The tool enables building managers to interpret complex energy data quickly and take immediate, data-driven actions based on real-time insights. By integrating advanced forecasting models with an intuitive visual interface, our solution facilitates proactive decision-making, optimizes energy consumption, and promotes sustainable building management practices. BiTSA will empower building managers to optimize energy consumption, control demand-side energy usage, and achieve sustainability goals.

Related papers

• AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector [38.48549968280562]
  AI4EF is an advanced user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF enables stakeholders to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts.
  arXiv  Detail & Related papers  (2024-12-05T10:36:39Z)
• Exploring Capabilities of Time Series Foundation Models in Building Analytics [15.525789412274587]
  Internet of Things (IoT) networks have transformed the management and optimization of building energy consumption. We conduct a comprehensive benchmarking of two publicly available IoT datasets. Our analysis shows that single-modal models demonstrate significant promise in overcoming the complexities of data variability and physical limitations in buildings.
  arXiv  Detail & Related papers  (2024-10-28T02:49:22Z)
• Just In Time Transformers [2.7350304370706797]
  JITtrans is a novel transformer deep learning model that significantly improves energy consumption forecasting accuracy. Our findings highlight the potential of advanced predictive technologies to revolutionize energy management and advance sustainable power systems.
  arXiv  Detail & Related papers  (2024-10-22T10:33:00Z)
• A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings [15.525789412274587]
  This study investigates the diverse dimensions of IoT data heterogeneity in both intra-building and inter-building contexts. The results emphasize the critical need for multi-modal data integration, domain-informed modeling, and automated data engineering pipelines.
  arXiv  Detail & Related papers  (2024-05-23T07:45:48Z)
• Non-Intrusive Electric Load Monitoring Approach Based on Current Feature Visualization for Smart Energy Management [51.89904044860731]
  We employ computer vision techniques of AI to design a non-invasive load monitoring method for smart electric energy management. We propose to recognize all electric loads from color feature images using a U-shape deep neural network with multi-scale feature extraction and attention mechanism.
  arXiv  Detail & Related papers  (2023-08-08T04:52:19Z)
• Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems [0.1393076890612966]
  We present a comprehensive review of Internet of Things-based frameworks aimed at smart city energy management. We focus on systems that not only collect and store data but also support intelligent analysis for monitoring, controlling, and enhancing system efficiency. The findings indicate that IoT-based frameworks offer significant potential to reduce energy consumption and environmental impact in smart buildings.
  arXiv  Detail & Related papers  (2023-06-08T21:19:42Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs [62.91362897985057]
  An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
  arXiv  Detail & Related papers  (2021-10-04T19:41:42Z)
• Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications [77.00418462388525]
  This paper presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection.
  arXiv  Detail & Related papers  (2020-11-26T00:32:07Z)
• 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.