Smart Home Energy Management

Pau, Giovanni; Collotta, Mario; Ruano, A. E.; Qin, Jiahu

doi:10.3390/en10030382

Cited by 46 publications

(23 citation statements)

References 18 publications

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“…For this reason, Home Energy Management Systems (HEMS) are becoming increasingly important to invert the continuously increasing trend in (electrical) energy consumption. Reviews on HEMS can be found in [109][110][111][112][113][114], as well as the works included in [115].…”

Section: General Overview Of Hemsmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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Section: General Overview Of Hemsmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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“…Automatic home systems (AHSs) equipped with sensors and actuators are utilized to monitor and control the household energy consumption and generation [73]. In general, home energy management systems (HEMSs) are introduced to manage household appliances as well as generate resources in an optimal manner.…”

Section: Flexibility At Customer Levelmentioning

confidence: 99%

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.

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“…Most of the researches are based on algorithms, strategies, techniques and hybrid algorithms for the DSM in smart grid [8,9]. The behaviour of pricing signals such as RTP, TOU, and CPP are analyzed for optimized consumption scheduling of HEMS [10,11], and [12]. Fuzzy based peak load reduction a smart grid environment is proposed in [13,14].…”

Section: Related Workmentioning

confidence: 99%

Energy Consumption Scheduling Using Adaptive Differential Evolution Algorithm in Demand Response Programs

Muthuselvi¹,

Balasubramanian²

2019

IJIES

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Demand Side Management (DSM) provides a better solution in order to manage increased electricity demand in the power system network. The DSM program relieves the stress on the electrical network for maintaining power system reliability during peak hours. This work proposes a new scheduling approach based on an Adaptive Differential Evolution algorithm (ADEA) by considering a new recombination probability factor (CP) and real mutation factor (F) for analysis. This proposed method is analyzed for industrial, commercial and residential network loads. The main aim of this demand control technique is to reduce the difference between the target curve and total load consumption curve. This paper provides a better solution compared to other algorithms like Evolutionary Approach (EA) and Symbiotic Organism Search (SOS) to reduce the peak load and electricity cost in industrial, commercial and residential distribution networks. The proposed method gives 8.2 % peak load reduction compared to EA for residential area and 2.28% reduction in peak load when compared to SOS for commercial sector. Also it reduces the electricity cost at 17.25%, 20.76% and 21.15 % for residential, commercial and industrial sectors when compared to without DSM. The participation factor of a consumer may be increased by price factor and the less violation in their scheduled demand. The described concept provides a relatively accurate fit to the target curve after load shifting. This concept will increase consumer participation in the DR Program.

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Smart Home Energy Management

Abstract: The new challenges on Information and Communication Technologies (ICT) in Automatic Home Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management flow and the use of energy.[...]

Cited by 46 publications

References 18 publications

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

Energy Consumption Scheduling Using Adaptive Differential Evolution Algorithm in Demand Response Programs

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