Load Forecasting of Electric Vehicle Charging Station Based on Edge Computing

Luo, Anqin; Yuan, Jianan; Liang, Fei; Yang, Qi; Mu, Di

doi:10.1109/ccet50901.2020.9213117

Cited by 15 publications

(1 citation statement)

References 4 publications

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“…A thermal model of a transformer is developed in [13], and it uses different models to predict the load data for the next 24 h. The authors of [14] use time-series data spanning over 2 million minutes in 1 min intervals from a single household in France to predict the load for that house five minutes into the future and achieve an R 2 of 83%. A stacked autoencoder neural network (SAE) is used in [15] to create a 50 h load forecast. The load of an area in China served by a 2500 kVA transformer is forecast in [16].…”

Section: Short-term Forecastsmentioning

confidence: 99%

Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System

O’Donnell

2023

Energies

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Greater variation in electrical load should be expected in the future due to the increasing penetration of electric vehicles, photovoltaics, storage, and other technologies. The adoption of these technologies will vary by area and time, and if not identified early and managed by electric utilities, these new customer needs could result in power quality, reliability, and protection issues. Furthermore, comprehensively studying the uncertainty and variation in the load on circuit elements over periods of several months has the potential to increase the efficient use of traditional resources, non-wires alternatives, and microgrids to better serve customers. To increase the understanding of electrical load, the authors propose a multistep, attention-focused, and efficient machine learning process to provide probabilistic forecasts of distribution transformer load for several months into the future. The method uses the solar irradiance, temperature, dew point, time of day, and other features to achieve up to an 86% coefficient of determination (R2).

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