An IoT-Based Prediction Technique for Efficient Energy Consumption in Buildings

Goudarzi, Shidrokh; Anisi, Mohammad Hossein; Soleymani, Seyed Ahmad; Ayob, Masri; Zeadally, Sherali

doi:10.1109/tgcn.2021.3091388

Cited by 17 publications

(3 citation statements)

References 40 publications

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“…Increasing power consumption: Attackers might manipulate IoT edge devices by introducing false code or running infinite loops, causing a surge in power consumption. This can lead to the rapid depletion of batteries, resulting in a service denial response because of dead batteries ( Goudarzi et al, 2021 ).…”

Section: Issues Of Iot Securitymentioning

confidence: 99%

Exploring security threats and solutions Techniques for Internet of Things (IoT): from vulnerabilities to vigilance

Sahu,

Mazumdar

2024

Front. Artif. Intell.

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The rapid proliferation of Internet of Things (IoT) devices across various industries has revolutionized the way we interact with technology. However, this widespread adoption has also brought about significant security challenges that must be addressed to ensure the integrity and confidentiality of data transmitted and processed by IoT systems. This survey paper delves into the diverse array of security threats faced by IoT devices and networks, ranging from data breaches and unauthorized access to physical tampering and denial-of-service attacks. By examining the vulnerabilities inherent in IoT ecosystems, we highlight the importance of implementing robust security measures to safeguard sensitive information and ensure the reliable operation of connected devices. Furthermore, we explore cutting-edge technologies such as blockchain, edge computing, and machine learning as potential solutions to enhance the security posture of IoT deployments. Through a comprehensive analysis of existing security frameworks and best practices, this paper aims to provide valuable insights for researchers, practitioners, and policymakers seeking to fortify the resilience of IoT systems in an increasingly interconnected world.

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Section: Issues Of Iot Securitymentioning

confidence: 99%

Exploring security threats and solutions Techniques for Internet of Things (IoT): from vulnerabilities to vigilance

Sahu,

Mazumdar

2024

Front. Artif. Intell.

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“…Accurate forecasts enable stakeholders, including building owners, facility managers, and policymakers, to make informed decisions, optimize resource allocation, and mitigate the environmental impacts of energy use [17,18]. The development and implementation of accurate algorithms for the prediction of building energy consumption emerge as indispensable tools in this endeavor, which is vital for the realization of sustainable, energy-efficient smart cities [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory

Yang,

Mao

2023

Sustainability

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As the world grapples with the challenges posed by climate change and depleting energy resources, achieving sustainability in the construction and operation of buildings has become a paramount concern. The construction and operation of buildings account for a substantial portion of global energy consumption and carbon emissions. Hence, the accurate prediction of building energy consumption is indispensable for reducing energy waste, minimizing greenhouse gas emissions, and fostering sustainable urban development. The aspiration to achieve predicted outcomes with remarkable accuracy has emerged as a pivotal objective, coinciding with the burgeoning popularity of deep learning techniques. This paper presents an auto-evaluation model for building energy consumption prediction via Long Short-Term Memory with modified Kalman filtering (LSTM-MKF). Results gleaned from data validation activities evince a notable transformation—a reduction of the maximal prediction error from an initial 83% to a markedly ameliorated 24% through the intervention of the proposed model. The LSTM-MKF model, a pioneering contribution within this paper, clearly exhibits a distinct advantage over the other models in terms of predictive accuracy, as underscored by its superior performance in all three key metrics, including mean absolute error, root mean square error, and mean square error. The model presents excellent potential as a valuable tool for enhancing the precision of predictions of building energy consumption, a pivotal aspect in energy efficiency, smart city development, and the formulation of informed energy policy.

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“…In this case, there is a risk of inducing psychological reactance in the user. While research has focused on optimizing energy management systems in terms of improving Internet of Thing (IoT) features [22,23], computational techniques [24], security [25], artificial intelligence [26][27][28], and other technical aspects [29], less investigation has gone into the underlying psychological mechanisms involved with appropriately using and communicating with said systems. Due to the complex nature of implementing sustainability measures, it is becoming increasingly important to explore multidisciplinary solutions to bridge the gap between green technology and consumer behavior [30].…”

Section: Introductionmentioning

confidence: 99%

Don’t touch that dial: Psychological reactance, transparency, and user acceptance of smart thermostat setting changes

Heatherly,

Baker,

Canfield

2023

PLoS ONE

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Automation inherently removes a certain amount of user control. If perceived as a loss of freedom, users may experience psychological reactance, which is a motivational state that can lead a person to engage in behaviors to reassert their freedom. In an online experiment, participants set up and communicated with a hypothetical smart thermostat. Participants read notifications about a change in the thermostat’s setting. Phrasing of notifications was altered across three dimensions: strength of authoritative language, deviation of temperature change from preferences, and whether or not the reason for the change was transparent. Authoritative language, temperatures outside the user’s preferences, and lack of transparency induced significantly higher levels of reactance. However, when the system presented a temperature change outside of the user’s preferences, reactance was mitigated and user acceptance was higher if the thermostat’s operations were transparent. Providing justification may be less likely to induce psychological reactance and increase user acceptance. This supports efforts to use behavioral approaches, such as demand response, to increase sustainability and limit the impacts of climate change.

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An IoT-Based Prediction Technique for Efficient Energy Consumption in Buildings

Cited by 17 publications

References 40 publications

Exploring security threats and solutions Techniques for Internet of Things (IoT): from vulnerabilities to vigilance

Exploring security threats and solutions Techniques for Internet of Things (IoT): from vulnerabilities to vigilance

Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory

Don’t touch that dial: Psychological reactance, transparency, and user acceptance of smart thermostat setting changes

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