A Novel Ensemble Method for Electric Vehicle Power Consumption Forecasting: Application to the Spanish System

Gómez-Quiles, Catalina; Asencio-Cortés, Gualberto; Gastalver-Rubio, Adolfo; Martínez‐Álvarez, Francisco; Troncoso, Alicia; Manresa, Joan; Riquelme, José C.; Santos, José Cristóbal Riquelme

doi:10.1109/access.2019.2936478

Cited by 25 publications

(11 citation statements)

References 35 publications

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“…Given their limited autonomy, the prediction of EV consumption seems crucial to efficiently manage the power supply. In this article, we followed the same steps to generate the EV demand time series as in [31]. The data was collected hourly and ranges from 2015-03-02T00:00 to 2016-05-31T23:00.…”

Section: Electric Vehicles Power Consumptionmentioning

confidence: 99%

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Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

et al. 2020

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Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

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Section: Electric Vehicles Power Consumptionmentioning

confidence: 99%

“…For evaluating the predictive performance of all models we use the weighted absolute percentage error (WAPE). This metric has been suggested by recent studies dealing with energy demand data [31]. Electric industries are interested in knowing the deviation in watts for better load generation planning.…”

Section: Modelsmentioning

confidence: 99%

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

et al. 2020

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“…Gomez-Quiles et al [24] used connection attempt real data-data of an EV connected to a charging station regardless of its state of charge (SoC)-to measure EVs' power consumption based on geographical zones each hour. This information was then used to perform hourly EV power consumption predictaion via ensemble learning with ARIMA, GARCH, and PSF algorithms for different zones.…”

Section: State Of the Artmentioning

confidence: 99%

Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions

2021

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The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range.

show abstract

“…Given their limited autonomy, the prediction of EV consumption seems crucial to efficiently manage the power supply. In this article, we have followed the same steps to generate the EV demand time series as in [31]. The data is collected hourly and ranges from 2015-03-02T00:00 to 2016-05-31T23:00.…”

Section: Electric Vehicles Power Consumptionmentioning

confidence: 99%

Section: Evaluation Metricmentioning

confidence: 99%

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

Lara-Benítez¹,

Carranza-García²,

Luna-Romera³

et al. 2020

Preprint

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Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

show abstract

A Novel Ensemble Method for Electric Vehicle Power Consumption Forecasting: Application to the Spanish System

Cited by 25 publications

References 35 publications

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

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