Long-term load forecasting for Smart Grid DOI
Vikash Kumar, Rajib Kumar Mandal

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(4), P. 045339 - 045339

Published: Nov. 6, 2024

Abstract The load forecasting problem is a complicated non-linear connected with the weather, economy, and other complex factors. For electrical power systems, long-term provides valuable information for scheduling maintenance, evaluating adequacy, managing limited energy supplies. A future generating, transmission, distribution facility’s development planning process begins demand forecasting. of advanced metering infrastructure (AMI) has greatly expanded amount real-time data collection on large-scale electricity consumption. techniques have changed significantly as result utilization this vast smart meter data. This study suggests numerous approaches using smart-metered from an actual system NIT Patna campus. Data pre-processing converting unprocessed into suitable format by eliminating possible errors caused lost or interrupted communications, presence noise outliers, duplicate incorrect data, etc. model trained historical significant climatic variables discovered through correlation analysis. With minimum MAPE RMSE every testing scenario, proposed artificial neural network yields greatest performance used efficacy technique been comparison acquired results various alternative methods.

Language: Английский

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics DOI
Junjie Wang,

Wenyu Huang,

Yuanping Ding

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134518 - 134518

Published: Jan. 1, 2025

Language: Английский

Citations

2
InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation DOI
Mingwei Zhong,

J.M. Fan,

Jianqiang Luo

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 371, P. 123745 - 123745

Published: June 20, 2024

Language: Английский

Citations

5
ShuffleTransformerMulti-headAttentionNet network for user load forecasting DOI
Linfei Yin,

Linyi Ju

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135537 - 135537

Published: March 1, 2025

Language: Английский

Citations

0
A probabilistic load forecasting method for multi-energy loads based on inflection point optimization and integrated feature screening DOI
Xiaoyu Zhao, Pengfei Duan, Xiaodong Cao

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136391 - 136391

Published: May 1, 2025

Language: Английский

Citations

0
Short-Term Load Forecasting Based on Similar Day Theory and BWO-VMD DOI Creative Commons
Qi Cheng, Jing Shi,

S.‐W. Grace Cheng

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(9), P. 2358 - 2358

Published: May 6, 2025

Short-term power load forecasting at the regional level is essential for maintaining grid stability and optimizing generation, consumption, maintenance scheduling. Considering temporal, periodic, nonlinear characteristics of load, a novel short-term method proposed in this paper. First, Random Forest importance ranking applied to select similar days weighted eigenspace coordinate system established measure similarity. The daily sequence then decomposed into high-, medium-, low-frequency components using Variational Mode Decomposition (VMD). high-frequency component predicted day averaging method, while neural networks are employed medium components, leveraging historical similar-day data, respectively. This multi-faceted approach enhances accuracy granularity pattern analysis. final forecast obtained by summing predictions these components. case study demonstrates that model outperforms LSTM, GRU, CNN, TCN Transformer, with an RMSE 660.54 MW MAPE 7.81%, also exhibiting fast computational speed low CPU usage.

Language: Английский

Citations

0
Long-term load forecasting for Smart Grid DOI
Vikash Kumar, Rajib Kumar Mandal

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(4), P. 045339 - 045339

Published: Nov. 6, 2024

Abstract The load forecasting problem is a complicated non-linear connected with the weather, economy, and other complex factors. For electrical power systems, long-term provides valuable information for scheduling maintenance, evaluating adequacy, managing limited energy supplies. A future generating, transmission, distribution facility’s development planning process begins demand forecasting. of advanced metering infrastructure (AMI) has greatly expanded amount real-time data collection on large-scale electricity consumption. techniques have changed significantly as result utilization this vast smart meter data. This study suggests numerous approaches using smart-metered from an actual system NIT Patna campus. Data pre-processing converting unprocessed into suitable format by eliminating possible errors caused lost or interrupted communications, presence noise outliers, duplicate incorrect data, etc. model trained historical significant climatic variables discovered through correlation analysis. With minimum MAPE RMSE every testing scenario, proposed artificial neural network yields greatest performance used efficacy technique been comparison acquired results various alternative methods.

Language: Английский

Citations

0