Dimensionality Reduction on IoT Monitoring Data of Smart Building for Energy Consumption Forecasting

• URL: http://arxiv.org/abs/2506.22468v1
• Date: Thu, 19 Jun 2025 08:29:35 GMT
• Title: Dimensionality Reduction on IoT Monitoring Data of Smart Building for Energy Consumption Forecasting
• Authors: Konstantinos Koutras, Agorakis Bompotas, Constantinos Halkiopoulos, Athanasios Kalogeras, Christos Alexakos,
• Abstract summary: The present work focuses on correlation analysis of data retrieved from a pilot IoT network installation monitoring a small smart office by means of environmental and energy consumption sensors.<n>The research motivation was to find statistical correlation between the monitoring variables that will allow the use of machine learning (ML) prediction algorithms for energy consumption reducing input parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Internet of Things (IoT) plays a major role today in smart building infrastructures, from simple smart-home applications, to more sophisticated industrial type installations. The vast amounts of data generated from relevant systems can be processed in different ways revealing important information. This is especially true in the era of edge computing, when advanced data analysis and decision-making is gradually moving to the edge of the network where devices are generally characterised by low computing resources. In this context, one of the emerging main challenges is related to maintaining data analysis accuracy even with less data that can be efficiently handled by low resource devices. The present work focuses on correlation analysis of data retrieved from a pilot IoT network installation monitoring a small smart office by means of environmental and energy consumption sensors. The research motivation was to find statistical correlation between the monitoring variables that will allow the use of machine learning (ML) prediction algorithms for energy consumption reducing input parameters. For this to happen, a series of hypothesis tests for the correlation of three different environmental variables with the energy consumption were carried out. A total of ninety tests were performed, thirty for each pair of variables. In these tests, p-values showed the existence of strong or semi-strong correlation with two environmental variables, and of a weak correlation with a third one. Using the proposed methodology, we manage without examining the entire data set to exclude weak correlated variables while keeping the same score of accuracy.

Related papers

• Meta-Statistical Learning: Supervised Learning of Statistical Inference [59.463430294611626]
  This work demonstrates that the tools and principles driving the success of large language models (LLMs) can be repurposed to tackle distribution-level tasks.<n>We propose meta-statistical learning, a framework inspired by multi-instance learning that reformulates statistical inference tasks as supervised learning problems.
  arXiv  Detail & Related papers  (2025-02-17T18:04:39Z)
• A task of anomaly detection for a smart satellite Internet of things system [0.9427635404752934]
  This paper proposes an unsupervised deep learning anomaly detection system. Based on the generative adversarial network and self-attention mechanism, it automatically learns the complex linear and nonlinear dependencies between environmental sensor variables. It can monitor the abnormal points of real sensor data with high real-time performance and can run on the intelligent satellite Internet of things system.
  arXiv  Detail & Related papers  (2024-03-21T14:26:29Z)
• An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness in IoT Applications [0.0]
  It is important to build IoT-based Machine Learning systems that are robust against data incompleteness while simultaneously being energy efficient. ENAMLE is a proactive, energy-aware technique for mitigating the impact of concurrent missing data. We present extensive experimental studies on two distinct datasets that demonstrate the energy efficiency of ENAMLE.
  arXiv  Detail & Related papers  (2024-03-15T15:01:48Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis [2.3226893628361682]
  Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices. Data collected by IoT contain a tremendous amount of information for anomaly detection. In this paper, we present a study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection.
  arXiv  Detail & Related papers  (2023-07-20T05:23:49Z)
• 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)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments [67.24598072875744]
  Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices. To alleviate this issue, sensors are often configured to operate at relatively low sampling frequencies. This can hamper dramatically subsequent decision-making, such as forecasting.
  arXiv  Detail & Related papers  (2022-06-15T19:46:59Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Occupancy Detection in Room Using Sensor Data [0.0]
  This paper provides a solution to detect occupancy using sensor data by using and testing several variables. Seven famous algorithms in Machine Learning, namely as Decision Tree, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, Kernelized SVM and K-Nearest Neighbors are tested and compared.
  arXiv  Detail & Related papers  (2021-01-10T19:53:57Z)
• 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)

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.