2022
DOI: 10.1049/rpg2.12572
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Smart coordination of predictive load balancing for residential electric vehicles based on EMD‐Bayesian optimised LSTM

Abstract: The charging load forecasting of residential Electric Vehicles help grid operators make informed decisions in terms of scheduling and managing demand response. The residence can include integrated residential appliances with multi-state and high-frequency features. For this reason, it is difficult to estimate the total load of residence accurately. To overcome this problem, this paper proposes a hybrid forecasting model using the empirical mode decomposition and Bayesian optimised Long Short-Term Memory for lo… Show more

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Cited by 9 publications
(4 citation statements)
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References 61 publications
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“…Akil et. al., [86] proposed a hybrid approach using empirical mode decomposition, Bayesian optimization, and neural networks for residential EV forecasting. Empirical mode decomposition is used to extract the features from the data.…”
Section: Load Forecastingmentioning
confidence: 99%
“…Akil et. al., [86] proposed a hybrid approach using empirical mode decomposition, Bayesian optimization, and neural networks for residential EV forecasting. Empirical mode decomposition is used to extract the features from the data.…”
Section: Load Forecastingmentioning
confidence: 99%
“…Thus, new electric vehicles on the grid are also allowed to be charged and existing EV users will be relieved of their charging concerns and needs. In way, a PV system with a peak power generation capacity of 50 kW [8] is connected to the busbar to which the EVs are connected in the IEEE 33 busbar test system. The power produced by the PV system in the real world and the total EV demand in the grid are given together in Fig.…”
Section: Load Balancing With Pv Systemmentioning
confidence: 99%
“…The overall charging power profile of EVs has two peaks per day, based on session times at work and at home. The large energy demand from many EVs, with its addition to the baseload profile, can overload the power grid or affect the daily load profiles at a common node in the distribution line [8].…”
Section: Introductionmentioning
confidence: 99%
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