Version 1
: Received: 28 August 2024 / Approved: 29 August 2024 / Online: 30 August 2024 (10:51:58 CEST)
How to cite:
Formaggio, G.; Tonelli-Neto, M. S.; Vilela, D. B.; Plasencia Lotufo, A. D. Short-Term Forecasting of Total Aggregate Demand in Uncontrolled Residential Charging with Electric Vehicles Using Artificial Neural Networks. Preprints2024, 2024082158. https://doi.org/10.20944/preprints202408.2158.v1
Formaggio, G.; Tonelli-Neto, M. S.; Vilela, D. B.; Plasencia Lotufo, A. D. Short-Term Forecasting of Total Aggregate Demand in Uncontrolled Residential Charging with Electric Vehicles Using Artificial Neural Networks. Preprints 2024, 2024082158. https://doi.org/10.20944/preprints202408.2158.v1
Formaggio, G.; Tonelli-Neto, M. S.; Vilela, D. B.; Plasencia Lotufo, A. D. Short-Term Forecasting of Total Aggregate Demand in Uncontrolled Residential Charging with Electric Vehicles Using Artificial Neural Networks. Preprints2024, 2024082158. https://doi.org/10.20944/preprints202408.2158.v1
APA Style
Formaggio, G., Tonelli-Neto, M. S., Vilela, D. B., & Plasencia Lotufo, A. D. (2024). Short-Term Forecasting of Total Aggregate Demand in Uncontrolled Residential Charging with Electric Vehicles Using Artificial Neural Networks. Preprints. https://doi.org/10.20944/preprints202408.2158.v1
Chicago/Turabian Style
Formaggio, G., Danieli Biagi Vilela and Anna Diva Plasencia Lotufo. 2024 "Short-Term Forecasting of Total Aggregate Demand in Uncontrolled Residential Charging with Electric Vehicles Using Artificial Neural Networks" Preprints. https://doi.org/10.20944/preprints202408.2158.v1
Abstract
Electric vehicles are in focus and growing, with new adoptions every day. Its wide use represents a new scenario and challenge for the electrical power system due to the high charge storage requirements. Predicting these loads using artificial neural networks proves to be a very efficient tool for solving time series. This work uses a multilayer perceptron network through supervised backpropagation training with Bayesian regularization to improve generalization, reducing overfit- ting errors. In this research, the aggregation of the actual consumption of 200 homes and 348 plug-in electric vehicles is used in the training and forecasting process, and to validate the method developed, MAPE was used. Short-term forecasts were made considering the year’s four seasons, predicting the next 24 hours of total aggregate demand from homes and vehicles. The methodology presented significant and relevant results using hybrid training for the problem, with the potential to be applied in the real world.
Keywords
Electric vehicles; Artificial neural networks; Load forecasting; multilayer perceptron 12 network; Residential charging; Backpropagation Bayesian regularization training
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
