A Survey on Information Communication Technologies in Modern Demand-Side Management for Smart Grids: Challenges, Solutions, and Opportunities

Said, Dhaou

doi:10.1109/emr.2022.3186154

Cited by 20 publications

(2 citation statements)

References 165 publications

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“…The benchmark used to evaluate the models is the simplest forecast baseline model, Naive Persistence (Persistence), it is based on the most trivial assumption that can be made when forecasting, it takes the value at the current time-step (t) and assigns it to the predicted value for the next step (t +1), in our case the value of the time-step (t) is assigned to the time-step that has to be predicted 24 hours ahead (t + 96) as described in (6), where y p is the predicted value and y the actual one.…”

Section: Model Tuning and Evaluation On Jpl Datasetmentioning

confidence: 99%

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Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Matrone,

Ogliari,

Nespoli

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Transports is one of the sectors that produce the highest emissions of CO 2 ; in the last ten years, there has been a process of decarbonization which has led to a considerable increase in Electric Vehicles (EVs). However, the sudden introduction of a large number of Electric vehicle supply equipment (EVSE) supplying electrical energy to EVs could cause problems in the management of the electric grid which must cope with the consequent increase in the electrical load demand. In this context, the 24 hour ahead forecast of the power curve associated with the recharge of EVs becomes of vital importance to ensure the reliability of the electric grid. In this paper, different Machine Learning models based on Recurrent Neural Networks (LSTM, GRU) and with different architectures, are compared based on their capability to accurately predict the power curve of an EV charging station one day in advance. A Sequence to Sequence model has been implemented and a thorough analysis of an Attention layer has been detailed. The models are tested on a real world open dataset.

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Section: Model Tuning and Evaluation On Jpl Datasetmentioning

confidence: 99%

“…EV load forecast is a form of Demand Side Management (DSM) [6], [7], [8] that can be crucial to balance load demand and power production. It is particularly relevant when the vehicles are integrated into Distributed Generation (DG) such as a micro-grid [9].…”

Section: Introductionmentioning

confidence: 99%