Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies

• URL: http://arxiv.org/abs/2010.13288v2
• Date: Wed, 10 Mar 2021 12:53:33 GMT
• Title: Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies
• Authors: Hao Wang, Gonzague Henri, Chin-Woo Tan, Ram Rajagopal
• Abstract summary: This paper proposes a new and more effective approach, i.e., activity disaggregation. We develop a framework by leverage machine learning for activity detection based on residential load data and features. We show through numerical case studies to demonstrate the effectiveness of the activity detection method.
• Score: 6.7336801526732755
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure. There has been a growing research interest toward appliance load disaggregation via nonintrusive load monitoring. As the electricity consumption of appliances is directly associated with the activities of consumers, this paper proposes a new and more effective approach, i.e., activity disaggregation. We present the concept of activity disaggregation and discuss its advantage over traditional appliance load disaggregation. We develop a framework by leverage machine learning for activity detection based on residential load data and features. We show through numerical case studies to demonstrate the effectiveness of the activity detection method and analyze consumer behaviors by time-dependent activity modeling. Last but not least, we discuss some potential use cases that can benefit from activity disaggregation and some future research directions.

Related papers

• Human Action Anticipation: A Survey [86.415721659234]
  The literature on behavior prediction spans various tasks, including action anticipation, activity forecasting, intent prediction, goal prediction, and so on. Our survey aims to tie together this fragmented literature, covering recent technical innovations as well as the development of new large-scale datasets for model training and evaluation.
  arXiv  Detail & Related papers  (2024-10-17T21:37:40Z)
• Estimating the Unobservable Components of Electricity Demand Response with Inverse Optimization [0.0]
  Understanding and predicting electricity demand responses to prices are critical activities for system operators, retailers, and regulators. The emergence of active consumers with flexible assets such as solar-plus-storage systems, and electric vehicles, introduces new challenges. We define the main components of net demand, as baseload, flexible, and self-generation, each having nonlinear responses to market price signals.
  arXiv  Detail & Related papers  (2024-09-17T14:17:31Z)
• A Scoping Review of Energy Load Disaggregation [1.6783315930924723]
  Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management. The field currently lacks a comprehensive overview. Domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed.
  arXiv  Detail & Related papers  (2024-01-10T09:59:12Z)
• Occupancy Detection Based on Electricity Consumption [0.0]
  This article presents a new methodology for extracting intervals when a home is vacant from low-frequency electricity consumption data. It shows encouraging results on both simulated and real consumption curves.
  arXiv  Detail & Related papers  (2023-12-13T21:49:09Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors [56.554277096170246]
  We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-the-wild data collection. For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels.
  arXiv  Detail & Related papers  (2023-05-15T16:02:56Z)
• A Neural Active Inference Model of Perceptual-Motor Learning [62.39667564455059]
  The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans. We present a novel formulation of the prior function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy.
  arXiv  Detail & Related papers  (2022-11-16T20:00:38Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)
• 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)
• Reshaping consumption habits by exploiting energy-related micro-moment recommendations: A case study [2.741120981602367]
  This work builds on the detection of repeated usage patterns from consumption logs. It presents the structure and operation of an energy consumption reduction system, which employs a set of sensors, smart-meters and actuators. The system recommends to the user the proper energy saving action at the right moment and gradually shapes user's habits.
  arXiv  Detail & Related papers  (2020-10-09T17:29:56Z)
• Energy Disaggregation with Semi-supervised Sparse Coding [0.0]
  Energy disaggregation research aims to decompose the aggregated energy consumption data into its component appliances. In this paper, a discriminative disaggregation model based on sparse coding has been evaluated on large-scale household power usage dataset for energy conservation.
  arXiv  Detail & Related papers  (2020-04-20T21:05:25Z)

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.