Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient power system operation. A hybrid method that is a combinati...

Full description

Bibliographic Details
Main Authors:	Ahmad Mohsenimanesh, Evgueniy Entchev, Filip Bosnjak
Format:	Article
Language:	English
Published:	MDPI AG 2022-09-01
Series:	Applied Sciences
Subjects:	EV charging load SD selection CEEMDAN GRU LSTM
Online Access:	https://www.mdpi.com/2076-3417/12/18/9288

Similar Items

EV Fleet Charging Load Forecasting Based on Multiple Decomposition With CEEMDAN and Swarm Decomposition
by: Emrah Dokur, et al.
Published: (2022-01-01)

Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer
by: Shichao Huang, et al.
Published: (2022-05-01)

HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model
by: Zhicheng Xiao, et al.
Published: (2023-11-01)

Prediction of CORS Water Vapor Values Based on the CEEMDAN and ARIMA-LSTM Combination Model
by: Xingxing Xiao, et al.
Published: (2022-09-01)

Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR
by: Xiaoyu Gao, et al.
Published: (2020-11-01)

Study on Forecasting Break-Up Date of River Ice in Heilongjiang Province Based on LSTM and CEEMDAN
by: Mingyang Liu, et al.
Published: (2023-01-01)

Green Bond Index Prediction Based on CEEMDAN-LSTM
by: Jiaqi Wang, et al.
Published: (2022-02-01)

Prediction of EV Charging Load Using Two-Stage Time Series Decomposition and DeepBiLSTM Model
by: Chenghan Li, et al.
Published: (2023-01-01)

Prediction of sea ice area based on the CEEMDAN-SO-BiLSTM model
by: Qiao Guo, et al.
Published: (2023-07-01)

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM
by: Po Yun, et al.
Published: (2023-01-01)

Dissolved Oxygen Forecasting for Lake Erie’s Central Basin Using Hybrid Long Short-Term Memory and Gated Recurrent Unit Networks
by: Daiwei Pan, et al.
Published: (2024-02-01)

Forecasting of runoff in the lower Yellow River based on the CEEMDAN–ARIMA model
by: Minghui Zhang, et al.
Published: (2023-03-01)

Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks
by: Hualing Lin, et al.
Published: (2020-03-01)

RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model
by: Xifeng Guo, et al.
Published: (2023-10-01)

Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model
by: Ningke Xu, et al.
Published: (2022-06-01)

Short Term Solar Irradiation Forecasting using CEEMDAN Decomposition Based BiLSTM Model Optimized by Genetic Algorithm Approach
by: Anuj Gupta, et al.
Published: (2022-08-01)

Condition Identification of Gears based on CEEMDAN Energy Entropy
by: Dou Chunhong, et al.
Published: (2018-01-01)

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization
by: Xiaoyi Wang, et al.
Published: (2023-11-01)

A Power Load Forecasting Method Based on Intelligent Data Analysis
by: He Liu, et al.
Published: (2023-08-01)

Research on precipitation prediction based on a complete ensemble empirical mode decomposition with adaptive noise–long short-term memory coupled model
by: Shaolei Guo, et al.
Published: (2022-12-01)

A hybrid CEEMDAN-VMD-TimesNet model for significant wave height prediction in the South Sea of China
by: Tong Ding, et al.
Published: (2024-03-01)

A Novel Hybrid Model of CEEMDAN-CNN-SAGU for Shanghai Copper Price Prediction
by: Jingyang Wang, et al.
Published: (2024-01-01)

Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models
by: Andrea Maria N. C. Ribeiro, et al.
Published: (2020-10-01)

A Novel Remaining Useful Life Prediction Method Based on CEEMDAN-IFTC-PSR and Ensemble CNN/BiLSTM Model for Cutting Tool
by: Lanjun Wan, et al.
Published: (2022-01-01)

Universities power energy management: A novel hybrid model based on iCEEMDAN and Bayesian optimized LSTM
by: Yaqing He, et al.
Published: (2021-11-01)

Research of Fault Diagnosis Method of Rolling Bearing based on Improved CEEMDAN-TKEO
by: Xia Changfeng
Published: (2017-01-01)

Combined Prediction of Short-Term Travel Time of Expressway Based on CEEMDAN Decomposition
by: Xingli Jia, et al.
Published: (2022-01-01)

A novel rainfall prediction model based on CEEMDAN-PSO-ELM coupled model
by: Xianqi Zhang, et al.
Published: (2022-04-01)

A Novel Non-Ferrous Metals Price Forecast Model Based on LSTM and Multivariate Mode Decomposition
by: Zhanglong Li, et al.
Published: (2023-07-01)

Rolling Bearings Fault Diagnosis Method Integrating CEEMDAN-SVD and Cepstrum
by: Jinni ZHENG, et al.
Published: (2021-05-01)

Fault Prediction for Capacitor of Power Converters Based on CEEMDAN and GWO-RELM
by: Linghui Meng, et al.
Published: (2022-01-01)

Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
by: Xianqi Zhang, et al.
Published: (2021-11-01)

Wireless Traffic Usage Forecasting Using Real Enterprise Network Data: Analysis and Methods
by: Su P. Sone, et al.
Published: (2020-01-01)

Current Trends in Electric Vehicle Charging Infrastructure; Opportunities and Challenges in Wireless Charging Integration
by: Konstantina Dimitriadou, et al.
Published: (2023-02-01)

A Hybrid De-Noising Algorithm for the Gear Transmission System Based on CEEMDAN-PE-TFPF
by: Lili Bai, et al.
Published: (2018-05-01)

LARGE-SCALE POWER TRANSMISSION SYSTEMS' INTEGRATED ELECTRIC VEHICLE LOAD MODELLING
by: Harsh Shrivastava, et al.
Published: (2023-08-01)

Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches
by: Agbessi Akuété Pierre, et al.
Published: (2023-06-01)

Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model
by: Sonali Swagatika, et al.
Published: (2024-01-01)

Air Pollutant Concentration Prediction Based on a CEEMDAN-FE-BiLSTM Model
by: Xuchu Jiang, et al.
Published: (2021-11-01)

Enhancing rock and soil hazard monitoring in open-pit mining operations through ultra-short-term wind speed prediction
by: Pengxiang Sun, et al.
Published: (2024-01-01)