Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network

Main Article Content

Francis Boafo Effah
Daniel Kwegyir
Daniel Opoku
Peter Asigri
Emmanuel Asuming Frimpong


electric vehicle, forecasting, artificial neural network, spider monkey optimization


The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.


D. Yu, M. P. Adhikari, A. Guiral, A. S. Fung, F. Mohammadi, and K. Raahemifar, “The Impact of Charging Battery Electric Vehicles on the Load Profile in the Presence of Renewable Energy,” 2019 IEEE Can. Conf. Electr. Comput. Eng. CCECE 2019, pp. 3–6, 2019, doi: 10.1109/CCECE.2019.8861730.

“Carbon Pollution from Transportation | Transportation, Air Pollution, and Climate Change | US EPA.” (accessed Jun. 29, 2021).

USAID, “Greenhouse gas emissions in Guatemala,” USAID Factsheet, no. December 2009, pp. 1–2, 2017, doi: 13 / 006.

A. Gautam, A. K. Verma, and M. Srivastava, “A Novel Algorithm for Scheduling of Electric Vehicle Using Adaptive Load Forecasting with Vehicle-to-Grid Integration,” 2019 8th Int. Conf. Power Syst. Transit. Towar. Sustain. Smart Flex. Grids, ICPS 2019, pp. 6–11, 2019, doi: 10.1109/ICPS48983.2019.9067702.

A. F. Botero and M. A. Rios, “Demand Forecasting Associated with Electric Vehicle Penetration on Distribution Systems,” 2012.

R. M. G. D. Ranathunga and L. A. Samaliarachchi, “Impact of Electric Vehicle Loads on the System Load Profile of Sri Lanka Impact of Electric Vehicle Loads on the System Load Profile of Sri Lanka,” no. December, 2017, doi: 10.4038/engineer.v50i4.7270.

M. M. Sangdehi, K. Lakshmi Varaha Iyer, K. Mukherjee, and N. C. Kar, “Short term power demand forecasting in light- and heavy-duty electric vehicles through linear prediction method,” 2012 IEEE Transp. Electrif. Conf. Expo, ITEC 2012, no. 2, pp. 4–9, 2012, doi: 10.1109/ITEC.2012.6243480.

Q. Huang et al., “Forecasting of the electric vehicles’ charging amount of electricity based on curves clustering,” ICNC-FSKD 2017 - 13th Int. Conf. Nat. Comput. Fuzzy Syst. Knowl. Discov., pp. 2424–2428, 2018, doi: 10.1109/FSKD.2017.8393153.

S. Ai, A. Chakravorty, and C. Rong, “Household EV charging demand prediction using machine and ensemble learning,” Proc. - 2nd IEEE Int. Conf. Energy Internet, ICEI 2018, pp. 163–168, 2018, doi: 10.1109/ICEI.2018.00037.

M. Pertl, F. Carducci, M. Tabone, M. Marinelli, S. Kiliccote, and E. C. Kara, “An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation,” IEEE Trans. Ind. Informatics, vol. 15, no. 4, pp. 1899–1910, 2019, doi: 10.1109/TII.2018.2865433.

E. S. Xydas, C. E. Marmaras, L. M. Cipcigan, A. S. Hassan, and N. Jenkins, “Electric Vehicle Load Forecasting using Data Mining Methods,” pp. 1–6.

D. Panahi, S. Deilami, M. A. S. Masoum, and S. M. Islam, “Forecasting plug-in electric vehicles load profile using artificial neural networks,” 2015 Australas. Univ. Power Eng. Conf. Challenges Futur. Grids, AUPEC 2015, pp. 1–6, 2015, doi: 10.1109/AUPEC.2015.7324879.

D. Kwegyir, E. A. Frimpong, and D. Opoku, “Optimization of Feedforward Neural Network Training using Modified Local Leader Phase Spider Monkey Optimization,” no. July, pp. 2157–2167, 2021.

D. Kwegyir, E. A. Frimpong, and D. Opoku, “Modified Local Leader Phase Spider Monkey Optimization Algorithm,” vol. 5, no. 2, pp. 1–18, 2021.

D. Devikanniga, K. Vetrivel, and N. Badrinath, “Review of meta-heuristic optimization based artificial neural networks and its applications,” J. Phys. Conf. Ser., vol. 1362, no. 1, 2019, doi: 10.1088/1742-6596/1362/1/012074.

E. S. Xydas, C. E. Marmaras, L. M. Cipcigan, A. S. Hassan, and N. Jenkins, “Forecasting Electric Vehicle charging demand using Support Vector Machines,” Proc. Univ. Power Eng. Conf., 2013, doi: 10.1109/UPEC.2013.6714942.

“Publisher: UK Power Networks - London Datastore.” (accessed Jul. 01, 2021).

E. Frank, M. A. Hall, and I. H. Witten, “The WEKA workbench,” Data Min., pp. 553–571, 2017, doi: 10.1016/b978-0-12-804291-5.00024-6.

Most read articles by the same author(s)