A smart residential security assisted load management system using hybrid cryptography

Chiñas-Palacios, Cristian; Águila-León, Jesús; Vargas‐Salgado, Carlos; García, Edith; Sotelo-Castañón, J.; Hurtado-Pérez, Elías

doi:10.1016/j.suscom.2021.100611

Cited by 4 publications

(4 citation statements)

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“…Moreover, a load management system was designed to optimize energy consumption in smart homes where the load was classified into several significant classes. 17 The Hopfield neural network was used to identify the status of the load and classify it into active, affordable, and inactive loads. Moreover, an unsupervised machine learning tool was used to better realize the behavior of household electricity demand profiles in a city in China.…”

Section: State Of the Artmentioning

confidence: 99%

“…The study used the artificial neural network (ANN)‐based model to estimate the consumption of energy in residential buildings. Moreover, a load management system was designed to optimize energy consumption in smart homes where the load was classified into several significant classes 17 . The Hopfield neural network was used to identify the status of the load and classify it into active, affordable, and inactive loads.…”

Section: Introductionmentioning

confidence: 99%

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Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Ramadan

Huang

Bamisile

et al. 2023

Energy Science & Engineering

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Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

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Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Ramadan

Huang

Bamisile

et al. 2023

Energy Science & Engineering

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“…However, concerning the estimations of the total power of this vehicles fleet, which has been carried out in the most conservative current solution, cars would charge/discharge through slow recharging (3.7 kW), although the existing Spanish electricity tariff "Tarifa 2.0 TD" offers a 10 kW of charge/discharge power [57], which supposes an increase of the total power to approximately 2300 MW. According to [50], the current Electric Vehicle (EV) average capacity is close to 27 kWh (average of 57 current EV models) [42]. However, such capacity has significantly increased during the last years.…”

Section: Vehicle-to-grid (V2g) Strategiesmentioning

confidence: 99%

“…In addition, HOMER has the advantage of being able to integrate storage systems [35][36][37]. Moreover, much scientific work uses HOMER to simulate renewable energy systems [38][39][40][41][42]. It is used to solve rural electrification problems where grid power is expensive or insufficient, in scenarios where the software calculates the most cost-effective and techno-economically renewable energy alternative [43].…”

Section: Introductionmentioning

confidence: 99%

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

et al. 2022

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The decarbonization of the electric generation system is fundamental to reaching the desired scenario of zero greenhouse gas emissions. For this purpose, this study describes the combined utilization of renewable sources (PV and wind), which are mature and cost-effective renewable technologies. Storage technologies are also considered (pumping storage and mega-batteries) to manage the variability in the generation inherent to renewable sources. This work also analyzes the combined use of renewable energies with storage systems for a total electrification scenario of Grand Canary Island (Spain). After analyzing the natural site’s resource constraints and focusing on having a techno-economically feasible, zero-emission, and low-waste renewable generation mix, six scenarios for 2040 are considered combining demand response and business as usual. The most optimal solution is the scenario with the maximum demand response, consisting of 3700 MW of PV, around 700 MW of off-shore wind system, 607 MW of pump storage, and 2300 MW of EV batteries capacity. The initial investment would be EUR 8065 million, and the LCOE close to EUR 0.11/kWh, making the total NPC EUR 13,655 million. The payback is 12.4 years, and the internal rate of return is 6.39%.

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An Optimised QPSO-Based Algorithm for Power Management in Smart Grids

Nleya,

Bhekinkosi

2024

2024 International Conference on Electrical, Computer and Energy Technologies (ICECET

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A smart residential security assisted load management system using hybrid cryptography

Cited by 4 publications

References 30 publications

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

An Optimised QPSO-Based Algorithm for Power Management in Smart Grids

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