Capacitor Capacitance Prediction Method Based on Time Series Method DOI
Shenglei Wang, Ruishi Lin, Liang Bao

et al.

Published: Aug. 16, 2024

Language: Английский

A novel decision support system for enhancing long-term forecast accuracy in virtual power plants using bidirectional long short-term memory networks DOI Creative Commons
Reza Nadimi, Mika Goto

Applied Energy, Journal Year: 2025, Volume and Issue: 382, P. 125273 - 125273

Published: Jan. 13, 2025

Language: Английский

Citations

2
A coupled framework for power load forecasting with Gaussian implicit spatio temporal block and attention mechanisms network DOI
Dezhi Liu, Xuan Lin,

Hanyang Liu

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110263 - 110263

Published: March 20, 2025

Language: Английский

Citations

1
A hybrid ultra-short-term wind speed prediction model using adaptive VMD and Time-Series Mixer DOI
Xiaoxia Wang, Pu Liu, Qingyi Liu

et al.

International Journal of Green Energy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: March 3, 2025

Language: Английский

Citations

0
The Electricity Load Prediction Model for Residential Buildings: A Critical Review of Output Types, Prediction Methods and Driving Factors DOI Creative Commons

Zhenjing Wu,

Min Qi, Weiling Zhang

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(6), P. 925 - 925

Published: March 15, 2025

An electrification revolution in the Chinese building energy field has been promoted by China’s carbon peak and neutrality goals. accurate electricity load prediction was essential to resolve conflict between substations which caused current increase demand, on both generation consumption sides. This review provided an in-depth study of models for residential inspected various output types, methods driving factors. The types were divided into three categories: (i) time scale, (ii) geographical scale (iii) regional scale. Predictive model classified as classical, algorithms based Machine Learning (ML) or Deep (DL) hybrid methods. Driving factors included single multiple features. By summarizing factors, influence improving accuracy according characteristics selecting correctly discussed. a key perspective future studies analyzing variations characteristics. It suggested that buildings diverse each region established offer valuable solutions planning distribution.

Language: Английский

Citations

0
Capacitor capacitance prediction based on VMD and ARIMA DOI Open Access
Hao Li, Jingjing Lyu, Hao Li

et al.

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 2971(1), P. 012005 - 012005

Published: Feb. 1, 2025

Abstract Capacitor capacitance prediction is an important means of analysing the reliability electronic systems. Although method based on physical models can theoretically explain aging process capacitors, its implementation complicated. To this end, a data-driven used in combination with time series model to predict capacitance. First, capacitor test system constructed, and acquired data pre-processed; then Autoregressive Integrated Moving Average Model (ARIMA) In order further improve accuracy, VMD-ARIMA combined constructed variational mode decomposition (VMD) extract characteristic components sequence, ARIMA each component. The results component are reconstructed obtain results. experimental show that compared single model, reduces MAE, RMSE, MAPE by 32.44%, 30.95%, 32.42%, respectively, effect significantly improved.

Language: Английский

Citations

0
Advancing Short-Term Load Forecasting with decomposed Fourier ARIMA: A Case Study on the Greek Energy Market DOI Creative Commons

Spyridon Karamolegkos,

D.E. Koulouriotis

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135854 - 135854

Published: March 1, 2025

Language: Английский

Citations

0
Integrated multi-energy load prediction system with multi-scale temporal channel features fusion DOI
Dezhi Liu, Jiaming Zhu,

Mengyang Wen

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117559 - 117559

Published: April 1, 2025

Language: Английский

Citations

0
Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer DOI Open Access

Zhewei Huang,

Yawen Yi

Sustainability, Journal Year: 2024, Volume and Issue: 16(17), P. 7613 - 7613

Published: Sept. 2, 2024

Accurate short-term load forecasting is critical for enhancing the reliability and stability of regional smart energy systems. However, inherent challenges posed by substantial fluctuations volatility in electricity patterns necessitate development advanced techniques. In this study, a novel approach based on two-stage feature extraction process hybrid inverted Transformer model proposed. Initially, Prophet method employed to extract essential features such as trends, seasonality holiday from original dataset. Subsequently, variational mode decomposition (VMD) optimized IVY algorithm utilized significant periodic residual component obtained Prophet. The extracted both stages are then integrated construct comprehensive data matrix. This matrix inputted into deep learning that combines an (iTransformer), temporal convolutional networks (TCNs) multilayer perceptron (MLP) accurate forecasting. A thorough evaluation proposed conducted through four sets comparative experiments using collected Elia grid Belgium. Experimental results illustrate superior performance approach, demonstrating high accuracy robustness, highlighting its potential ensuring stable operation

Language: Английский

Citations

1
Capacitor Capacitance Prediction Method Based on Time Series Method DOI
Shenglei Wang, Ruishi Lin, Liang Bao

et al.

Published: Aug. 16, 2024

Language: Английский

Citations

0