Smart home energy management systems survey

Amer, Motaz; Naaman, A.; M'Sirdi, Kouider; El-Zonkoly, Amany

doi:10.1109/redec.2014.7038551

Cited by 45 publications

(29 citation statements)

References 59 publications

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“…Power curtailment is the basic approach for home appliance management, and the main idea of this type of algorithm is that appliances are normally turned off in the event of a DR event according to the demand limit signal from the utility company and depending on predefined customer preferences (load priority and comfort level). Based on this concept, Amer et al [18] proposed an algorithm that is capable of reducing the overall consumed power of a household by managing its appliances based on a predefined appliance priority. Pipattanasomporn et al [19] presented a HEMS algorithm that manages and controls the consumption of certain household appliances to guarantee the total energy consumption below a certain level during a DR event and according to predefined customer preferences.…”

Section: Hems Algorithms Based On Load Power Curtailmentmentioning

confidence: 99%

“…This model assumes that all the appliances are operating in specific modes and that the appliances can switch between various modes depending on the energy consumption. Amer and El-zonkoly implemented another HEMS algorithm [18] using an Arduino microcontroller. Besides managing the end-use appliances as per their preset priority to reduce the total consumption under a certain limit, the algorithm also achieves the purpose of clipping the peak-to-average ratio (PAR).…”

Section: Hems Algorithms Based On Load Power Shiftingmentioning

confidence: 99%

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Unified Algorithm for Demand-Side Appliance Commitment

et al. 2018

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Recent energy efficiency and conservation programs have created an unprecedented demand for home energy management systems (HEMS) in the residential sector aimed at reducing electricity consumption and saving on electricity bills. This paper gives a brief review of the basic algorithms found in the literature for HEMS that target optimum scheduling for home appliances participating in demand response (DR) programs. The working principles, as well as the pros and the cons, of these algorithms are explained and analyzed. Then, a unified algorithm to manage the hourly power consumption of home appliances on a daily basis is suggested using two scenarios. The first scenario aims to simultaneously achieve dual utility/customer benefits while avoiding the individual drawbacks of each presented algorithm. The second scenario aims to actively involve DR customers in making the optimum decision regarding their appliances in the face of their dynamic desires. The proposed algorithm is generic in the sense that it has the ability to achieve three different objectives for dual utility/customer benefits. Moreover, the paper takes into consideration a range of constraints, such as load priority, customer preferences, utility request, and electricity dynamic pricing scheme. The essential goal of this algorithm is not only to curtail or control the power consumption of appliances but to also shift it to a better price period based on different tariff rates. The results reflect the effectiveness of the proposed algorithm, which extends the previous findings in the literature by considering a wider range of limitations applied on HEMS.

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Section: Hems Algorithms Based On Load Power Curtailmentmentioning

confidence: 99%

Section: Hems Algorithms Based On Load Power Shiftingmentioning

confidence: 99%

Unified Algorithm for Demand-Side Appliance Commitment

et al. 2018

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“…As shown in Figure 1, the Energy System Interface (ESI) and Energy Management System (EMS) represent the inside environment of the smart home [21,22]. ESI is the interface between the smart home and smart grid.…”

Section: Attack Against Home Monitoring and Controlmentioning

confidence: 99%

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

2017

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Abstract:The growing demand for an independent and comfortable lifestyle has motivated the development of the smart home, and providing security is a major challenge for developers and security analysts. Enhancing security in the home environment has been recognized as one of the main obstacles to realizing the vision of creating energy-efficient smart homes and buildings. Understanding the risks associated with the use and potential exploitation of information about homes, end-users, and partners, as well as forming techniques for integrating security assessments into the design, is not straightforward. To address this challenge, we propose enhanced secure network architecture (SH-SecNet) for the diagnosis of security threats in the smart home. In our architecture, we use the Multivariate Correlation Analysis (MCA) technique to analyze the network flow packet in the network layer, as this classifies the network traffic by extracting the correlation between network traffic features. We evaluated the performance of our architecture with respect to various parameters, such as CPU utilization, throughput, round trip time, and accuracy. The result of the evaluation shows that our architecture is efficient and accurate in detecting and mitigating attacks in the smart home network with a low performance overhead.

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“…Along with the industrial and commercial sectors, EMSs are also advantageous in the context of smart homes, where different kinds of system components (i.e., hardware elements, software algorithms, network connections, and sensors) cooperate with each other to provide various services supporting human lifestyle [11]. An EMS should exhibit distinct features to be engaging for end users.…”

Section: Introductionmentioning

confidence: 99%

Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi

et al. 2019

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As the adoption of distributed generation and energy storage grows and the attention to energy efficiency rises, Energy Management is assuming a growing importance in smart homes. Energy Management Systems (EMSs) should be easily deployable on smart homes and seamlessly integrate with the Internet of Things (IoT) ecosystem, including generators and storage devices. This paper redesigns a previously presented EMS to reduce its computational complexity, implement it on a Raspberry Pi, and make it compatible with the IoT paradigm. The EMS manages the power flows between smart home loads, renewable generators, electrical storage, and power grid. It communicates with a network of wireless sensors for electrical appliances and with a cloud-based utility data aggregator. The EMS uses Artificial Intelligence and a Dynamic Programming algorithm to fulfill two objectives at the same time: lowering the end user’s electricity bill and reducing the uncertainty on the power exchanged between the end user and the grid manager. The latter goal is obtained by an effective compensation of forecasting errors. A test bench emulating four smart homes was used to measure the effectiveness of the EMS and the efficiency of the proposed implementation. The results show an uncertainty of the aggregated exchanged power of only 2.88% and a reduction of the electrical bill for end-users of up to 3.23%. Furthermore, the EMS can complete its most onerous task in less than 9 min. The good performance of the proposed EMS makes it a candidate for fast adoption by the market.

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Smart home energy management systems survey

Cited by 45 publications

References 59 publications

Unified Algorithm for Demand-Side Appliance Commitment

Unified Algorithm for Demand-Side Appliance Commitment

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi

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