Temporal association rules for electrical activity detection in residential homes

Cao, Heng; Wijaya, Tri Kurniawan; Aberer, Karl; Nunes, Nuno

doi:10.1109/bigdata.2016.7840964

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…Similarly, sequential association rule mining was used by Cao et al [59] to discover frequent patterns of appliance-use in terms of order, duration and time-windows. A number of Gaussian Mixture Models (GMM) were used for discovering these appliance use patterns, based on circuit and appliance-level electricity consumption in one-minute intervals from 800 households in the United States.…”

Section: Improvement Based On Predicting Appliance-use Patternsmentioning

confidence: 99%

“…Other works that considered OBs to improve load forecasting and other related solutions are summarized in Table 2 as an additional sample to the 18 works described in Section 4.1. These research works [19,43,53,54,56,57,59] are mainly described by their used household and building characteristics, occupant behavioural characteristics, and machine learning techniques.…”

Section: Summary Of Load Forecasting Work Considering Obsmentioning

confidence: 99%

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Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

2022

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There are two primary ways to save energy within a building: (1) through improving building engineering structures and adopting efficient appliance ownership, and (2) through changing occupants’ energy-consuming behaviors. Unfortunately the second way suffers from many challenges and limitations. Occupant behavior is, indeed, a complex and multi-disciplinary concept depending on several human factors. Although its importance is recognized by the energy management community, it is often oversimplified and naively defined when used to study, analyze or model energy load. This paper aims at promoting the definition of occupant behavior as well as exploring the extent to which the latter is involved in research works, targeting directly or indirectly energy savings. Hence, in this work, we propose an overview of interdisciplinary research approaches that consider occupants’ energy-saving behaviors, while we present the big picture and evaluate how occupant behavior is defined, we also propose a categorization of the major works that consider energy-consuming occupant behavior. Our findings via a literature review methodology, based on a bibliometric study, reveal a growth of the number of research works involving occupant behavior to model load forecasting and household segmentation. We have equally identified a research trend showing an increasing interest in studying how to successfully change occupant behaviors towards energy saving.

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Section: Improvement Based On Predicting Appliance-use Patternsmentioning

confidence: 99%

Section: Summary Of Load Forecasting Work Considering Obsmentioning

confidence: 99%

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

2022

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“…To model occupants' indoor behavior and activities in interaction with appliances, diverse algorithms were employed in the studies, such as pedestrian dead reckoning [253], Bayesian network mode and linear regression [254], k-means and Gaussian mixture [69], random forest [255], and SVMs [256]. According to power usage of appliances, Gaussian mixture [257], k-means [258], optimization based on defined objective function [243] were used to infer load distribution and scheduling for systems. Similarly, power data showed potentials to extract building occupancy using data-driven approaches, such as decision trees [259] and NNs [260].…”

Section: Appliance Usementioning

confidence: 99%

Modeling occupant behavior in buildings

Carlucci

Simone

Firth

et al. 2020

Building and Environment

151

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In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants' actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the Revised Manuscript with No Changes MarkedClick here to view linked References data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.

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