A smart home energy management system using IoT and big data analytics approach

Al-Ali, A. R.; Zualkernan, Imran A.; Rashid, Mohammed; Gupta, Ragini; Alikarar, Mazin Abdelbadea Nasralla

doi:10.1109/tce.2017.015014

Cited by 507 publications

(186 citation statements)

References 15 publications

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“…The model adopts a time interval optimization strategy to find an optimal scheme and uses radio frequency technology to achieve fully automatic control of appliances. Al‐Ali et al presented an IoT‐based energy management system for smart homes. This system uses off‐the‐self business intelligence and big data analytics to process and analyze data collected from home devices and manage energy consumption and meet consumer demand.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it is necessary to automatically extract useful knowledge from this information to cost‐effective use of energy at home. In this sense, big data analytics technologies can be used to collect and analyze large amounts of data . Specifically, such technologies allows large volumes of data generated by using electronic sensors and users’ demands to be coordinated, analyzed, and effectively used …”

Section: Introductionmentioning

confidence: 99%

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IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

Paredes-Valverde

Alor-Hernández

Alcaráz³

et al. 2019

Computational Intelligence

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Despite there has been an increasing energy price due to factors such as supply, demand, government regulation, among others, users do not like to spend their time to analyze their power consumption and establish actions to save money. Hence, there is a need for smart solutions that help users to save energy at home in an easy way. The smart home concept is attracting the attention of both academia and industry to address this need. Nowadays, high volumes of data are available in the smart home context, facilitated by the growth of internet of things (IoT)‐based devices and advanced sensing infrastructure. Therefore, it is necessary to automatically extract useful knowledge from this information to cost‐effective use of energy at home. In this sense, this work presents IntelliHome, a smart‐home system that aims to reduce electrical energy consumption at home. To this end, IntelliHome uses big data analytics technologies and Machine Learning and statistical techniques to provide users with a meaningful perspective of their electricity consumption habits aiming to actively involve them in the energy‐saving process through real‐time information and energy‐saving recommendations. This work also discusses a case study and an evaluation aligned with the objectives of this work. The obtained results verify the effectiveness of the proposed system regarding electrical energy saving.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

Paredes-Valverde

Alor-Hernández

Alcaráz³

et al. 2019

Computational Intelligence

View full text Add to dashboard Cite

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“…HEMS technologies to manage the energy flow into a house, e.g. demand response (DR), have been discussed extensively in the literature . Their control algorithms include linear programming‐related approach , heuristic searching techniques, and artificial intelligence, e.g.…”

Section: Introductionmentioning

confidence: 99%

Home Energy Management Systems under Effects of Solar‐Battery Smart Inverter Functions

Dao

Ishii

Takenobu

et al. 2020

IEEJ Transactions Elec Engng

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Rooftop solar panels paired with home‐scale batteries are increasingly popular in the residential area and they bring a fantastic opportunity to households, considered as prosumers, to make benefits from trading electricity. Unlike research in the literature, we consider the trading with technical influences of utility requirements: Volt‐Watt and Volt‐Var functions. As possible active power curtailments may occur and lead to economic losses to households, our work reveals how a home energy management system (HEMS) maximizes profit from trading energy with an aggregator and an electricity retailer. Appropriate optimization problems are formulated with technical constraints, these functions, and home appliances. HEMS responses are optimized in two phases. The first phase is for planning an optimal day‐ahead energy commitment to the aggregator with a consideration of the predicted power curtailments. On the day of operation, the second phase tries to follow the commitment by reoptimizing the responses in the real condition. Simulation of a practical power system shows that addressing these functions can mitigate power curtailment, reduce economic loss, and improve home profits. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…The term Smart Energy has evolved from the term Smart Grids [3,4]. While Smart Grids were focused solely on the area of electricity, the term Smart Energy also encompasses HVAC (heating, ventilation, and air-conditioning) systems [5], and the intelligent use of energy in Industry 4.0 [6], transportation [7], public buildings [8,9], and homes [10,11]. Smart Energy solutions involve the use of different disruptive technologies, including Artificial Intelligence (such as case-based reasoning systems [12], multi-agent systems) [13], Deep Learning [14]; Distributed • Layer 1 (IoT + Sensors): The first layer is formed by the IoT devices (sensors, smart-phones, relays, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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Context: Smart Energy is a disruptive concept that has led to the emergence of new energy policies, technology projects, and business models. The development of those models is driven by world capitals, companies, and universities. Their purpose is to make the electric power system more efficient through distributed energy generation/storage, smart meter installation, or reduction of consumption/implementation costs. This work approaches Smart Energy as a paradigm that is concerned with systemic strategies involving the implementation of innovative technological developments in energy systems. However, many of the challenges encountered under this paradigm are yet to be overcome, such as the effective integration of solutions within Smart Energy systems. Edge Computing is included in this new technology group. Objective: To investigate developments that involve the use of Edge Computing and that provide solutions to Smart Energy problems. The research work will be developed using the methodology of systematic mapping of literature, following the guidelines established by Kitchenham and Petersen that facilitate the identification of studies published on the subject. Results: Inclusion and exclusion criteria have been applied to identify the relevant articles. We selected 80 papers that were classified according to the type of publication (journal, conferences, or book chapter), type of research (conceptual, experience, or validation), type of activity (implement, validate, analyze) and asset (architecture, framework, method, or models). Conclusion: A complete review has been conducted of the 80 articles that were closely related to the questions posed in this research. To reach the goal of building Edge Computing architectures for Smart Energy environments, several lines of research have been defined. In the future, such architectures will overcome current problems, becoming highly energy-efficient, cost-effective, and capacitated to process and respond in real-time.Electronics 2020, 9, 48 2 of 33 Ledger Technologies (such as blockchain) [15]; or distributed sensing and actuation technologies such as the Internet of Things (IoT) [3,16].Regarding the Internet of Things (IoT), its recognition as a paradigm dates back to 2011-2012, when renowned companies and institutions such as Gartner, Forbes, or Wired started using this term to refer to the emerging technology [17]. The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provid...

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A smart home energy management system using IoT and big data analytics approach

Cited by 507 publications

References 15 publications

IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

Home Energy Management Systems under Effects of Solar‐Battery Smart Inverter Functions

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

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