Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland

Tim Unterluggauer,Pertti Järventausta,Christian Rehtanz,Kalle Rauma

doi:10.1049/els2.12028

Abstract

This study assesses the performance of a multivariate multi-step charging load prediction approach based on the long short-term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging sites. Real charging data from shopping centres, residential, public, and workplace charging sites are gathered. Altogether, the data consists of 50,504 charging events measured at 37 different charging sites in Finland between January 2019 and January 2020. A forecast of the aggregated charging load is performed in 15-min resolution for each type of charging site. The second contribution of the work is the extended short-term forecast horizon. A multi-step prediction of either four (i.e., one hour) or 96 (i.e., 24 h) time steps is carried out, enabling a comparison of both horizons. The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories. Furthermore, the results indicate that the forecasting accuracy strongly correlates with the forecast horizon. The 4-time step prediction yields considerably superior results compared with the 96-time step forecast.

Highlights

In 2017, the transport sector was responsible for 27% of all greenhouse gas emissions in the European Union, with passenger cars accounting for 44% of the transport emissions [1]
The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories
The analysis demonstrates that the long short‐term memory (LSTM) outperforms a simple Artificial neural network (ANN), both for a forecast horizon of 15 and 30 min and that the prediction error diminishes with decreasing forecast horizon

Summary

Introduction

In 2017, the transport sector was responsible for 27% of all greenhouse gas emissions in the European Union, with passenger cars accounting for 44% of the transport emissions [1]. Electric vehicles (EVs) are one of the solutions to cut carbon emissions in the transport sector and achieve the climate protection goals. Other related concerns, such as urban air pollution and its impact on health, have encouraged politicians to promote the adoption of EVs [2]. EVs could benefit the power system by providing ancillary services. The charging events are clustered according to the four categories of charging sites selected for this work. The charging events of 21 sites are assigned to the second category named residential charging (REc), representing the charging at home. Eight and seven sites are assigned to PUc and WOc, respectively

Methods

Discussion

Conclusion