Exploring Capabilities of Time Series Foundation Models in Building Analytics

• URL: http://arxiv.org/abs/2411.08888v1
• Date: Mon, 28 Oct 2024 02:49:22 GMT
• Title: Exploring Capabilities of Time Series Foundation Models in Building Analytics
• Authors: Xiachong Lin, Arian Prabowo, Imran Razzak, Hao Xue, Matthew Amos, Sam Behrens, Flora D. Salim,
• Abstract summary: 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.
• Score: 15.525789412274587
• License:
• Abstract: The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By leveraging IoT-based monitoring systems, stakeholders such as building managers, energy suppliers, and policymakers can make data-driven decisions to improve energy efficiency. However, accurate energy forecasting and analytics face persistent challenges, primarily due to the inherent physical constraints of buildings and the diverse, heterogeneous nature of IoT-generated data. In this study, we conduct a comprehensive benchmarking of two publicly available IoT datasets, evaluating the performance of time series foundation models in the context of building energy analytics. Our analysis shows that single-modal models demonstrate significant promise in overcoming the complexities of data variability and physical limitations in buildings, with future work focusing on optimizing multi-modal models for sustainable energy management.

Related papers

• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• 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)
• A Survey on Semantic Modeling for Building Energy Management [0.2301816954855697]
  This survey explores the leading semantic modeling techniques deployed for energy management in buildings. It aims to offer tangible use cases for applying semantic models, shedding light on the pivotal concepts and limitations intrinsic to each model.
  arXiv  Detail & Related papers  (2024-04-17T20:10:43Z)
• The Forecastability of Underlying Building Electricity Demand from Time Series Data [1.3757257689932039]
  Forecasting building energy consumption has become a promising solution in Building Energy Management Systems. Different data-driven approaches to forecast the future energy demand of buildings can be found in the scientific literature. The identification of the most accurate forecaster model which can be utilized to predict the energy demand of such a building is still challenging.
  arXiv  Detail & Related papers  (2023-11-29T20:47:47Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning [15.995891934245334]
  We propose a novel variational auto-encoder-generative adversarial network (VAE-GAN) technique for generating time-series data on energy consumption in smart homes. We tested the online performance of Q-learning-based HEMS with real-world smart home data.
  arXiv  Detail & Related papers  (2023-05-14T22:22:16Z)
• In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance [0.0]
  Short-term load forecasting (STLF) is crucial to the optimization of their reliability, emissions, and costs. This work conducts a comparative study of Deep Learning (DL) architectures, with respect to forecasting accuracy and training sustainability. The case study focuses on day-ahead forecasts for the Portuguese national 15-minute resolution net load time series.
  arXiv  Detail & Related papers  (2023-02-25T10:08:23Z)
• Latent Diffusion Energy-Based Model for Interpretable Text Modeling [104.85356157724372]
  We introduce a novel symbiosis between the diffusion models and latent space EBMs in a variational learning framework. We develop a geometric clustering-based regularization jointly with the information bottleneck to further improve the quality of the learned latent space.
  arXiv  Detail & Related papers  (2022-06-13T03:41:31Z)
• 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)
• 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.