A Power Load Forecasting Method Based on Intelligent Data Analysis

A Power Load Forecasting Method Based on Intelligent Data Analysis

Abnormal electricity consumption behavior not only affects the safety of power supply but also damages the infrastructure of the power system, posing a threat to the secure and stable operation of the grid. Predicting future electricity consumption plays a crucial role in resource management in the...

Full description

Bibliographic Details
Main Authors:	He Liu, Xuanrui Xiong, Biao Yang, Zhanwei Cheng, Kai Shao, Amr Tolba
Format:	Article
Language:	English
Published:	MDPI AG 2023-08-01
Series:	Electronics
Subjects:	power forecasting data analysis CEEMDAN decomposition LSTM forecasting
Online Access:	https://www.mdpi.com/2079-9292/12/16/3441

Similar Items

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)

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

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

An Integrated Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Optimize LSTM for Significant Wave Height Forecasting
by: Lingxiao Zhao, et al.
Published: (2023-02-01)

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach
by: Tingting Zhang, et al.
Published: (2023-01-01)

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

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model
by: Pin Lv, et al.
Published: (2022-01-01)

Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting
by: Zhao Zixu
Published: (2023-01-01)

Evaluating the EEMD-LSTM model for short-term forecasting of industrial power load: A case study in Vietnam
by: Nam Nguyen Vuu Nhat, et al.
Published: (2023-09-01)

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR
by: Yonghui Duan, et al.
Published: (2023-12-01)

AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition
by: Mughair Aslam Bhatti, et al.
Published: (2024-03-01)

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

Short-term load forecasting method based on secondary decomposition and improved hierarchical clustering
by: Wenting Zha, et al.
Published: (2024-06-01)

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm
by: Yonghui Duan, et al.
Published: (2024-03-01)

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns
by: Huimin Han, et al.
Published: (2024-01-01)

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 review of ultra-short-term forecasting of wind power based on data decomposition-forecasting technology combination model
by: Yulong Chen, et al.
Published: (2022-11-01)

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

Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC
by: Satyabrata Sahoo, et al.
Published: (2024-02-01)

A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion
by: Qiuhong Huang, et al.
Published: (2022-07-01)

A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits
by: Fang Wang, et al.
Published: (2023-07-01)

Precipitation prediction based on CEEMDAN–VMD–BILSTM combined quadratic decomposition model
by: Xianqi Zhang, et al.
Published: (2023-09-01)

Multi-scale regional photovoltaic power generation forecasting method based on sequence coding reconstruction
by: Diyang Gong, et al.
Published: (2023-10-01)

A Study on China coal Price forecasting based on CEEMDAN-GWO-CatBoost hybrid forecasting model under Carbon Neutral Target
by: Xiang Wang, et al.
Published: (2022-09-01)

Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks
by: Akylas Stratigakos, et al.
Published: (2021-07-01)

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet
by: Ahmad Mohsenimanesh, et al.
Published: (2022-09-01)

Developing Personalised Learning Support for the Business Forecasting Curriculum: The Forecasting Intelligent Tutoring System
by: Devon Barrow, et al.
Published: (2024-03-01)

Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting
by: Weishan Zhang, et al.
Published: (2023-10-01)

Short term power load forecasting based on BES-VMD and CNN-Bi-LSTM method with error correction
by: Nier Wang, et al.
Published: (2023-01-01)

Modelling of Deep Learning-Based Downscaling for Wave Forecasting in Coastal Area
by: Didit Adytia, et al.
Published: (2023-01-01)

Two-Stage Short-Term Power Load Forecasting Based on SSA–VMD and Feature Selection
by: Weijian Huang, et al.
Published: (2023-06-01)

Daily Weather Forecasting Based on Deep Learning Model: A Case Study of Shenzhen City, China
by: Guici Chen, et al.
Published: (2022-08-01)

Flood Forecasting Method and Application Based on Informer Model
by: Yiyuan Xu, et al.
Published: (2024-03-01)

Deep convolutional long short-term memory for forecasting wind speed and direction
by: Anggraini Puspita Sari, et al.
Published: (2021-06-01)

Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System
by: Van Bui, et al.
Published: (2021-04-01)

Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems
by: Daniel Dylewsky, et al.
Published: (2022-01-01)

High Precision LSTM Model for Short-Time Load Forecasting in Power Systems
by: Tomasz Ciechulski, et al.
Published: (2021-05-01)

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

Investigation of computational intelligence methods in forecasting problems at stock exchanges
by: Yuriy Zaychenko, et al.
Published: (2021-09-01)

A Combined Model Incorporating Improved SSA and LSTM Algorithms for Short-Term Load Forecasting
by: Mingchong Han, et al.
Published: (2022-06-01)