Results 51 to 60 of about 3,806,141 (312)

Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

Forecasting, 2022
Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning.
V. Veeramsetty, D. R. Chandra, F. Grimaccia, Marco Mussetta   +3 more
semanticscholar   +1 more source

Short-Term Load-Forecasting Method Based on Wavelet Decomposition With Second-Order Gray Neural Network Model Combined With ADF Test

IEEE Access, 2017
Improving the accuracy of power system load forecasting is important for economic dispatch. However, a load sequence is highly nonstationary and hence makes accurate forecasting difficult. In this paper, a method based on wavelet decomposition (WD) and a
Bowen Li, Jing Zhang, Yu He, Yang Wang
doaj   +1 more source

Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model

Energies, 2022
Load forecasting (LF) is an essential factor in power system management. LF helps the utility maximize the utilization of power-generating plants and schedule them both reliably and economically.
Md Jamal Ahmed Shohan, Md Omar Faruque, Simon Y. Foo   +2 more
doaj   +1 more source

A review of research on spatial-temporal forecasting of power load based on spatial-temporal data mining

International Journal of Electrical Power & Energy Systems
Lately, in the context of global energy crisis and economic recovery after the COVID-19 epidemic, the contradiction between power supply and demand has become more prominent, showing a tight balance.
Zhanshuo Hu, Pengfei Zhang, Jianning Cai, Hengyu Liu, Gaohan Wang, Zhe Chen   +5 more
doaj   +1 more source

Short-Term Load Forecasting: A Comprehensive Review and Simulation Study With CNN-LSTM Hybrids Approach

IEEE Access
Short-term load forecasting (STLF) is vital in effectively managing the reserve requirement in modern power grids. Subsequently, it supports the grid operator in making effective and economical decisions during the power balancing operation.
Kaleem Ullah, Muhammad Ahsan, Syed Muhammad Hasanat, Muhammad Haris, Hamza Yousaf, Syed Muhammad Faraz Raza, Ritesh Tandon, Samain Abid, Zahid Ullah   +8 more
semanticscholar   +1 more source

Short-Term Load Forecasting Method Based on Feature Preference Strategy and LightGBM-XGboost

IEEE Access, 2022
Short term load forecasting is one of the important problems in power system. Accurate forecasting results can improve the flexibility of power market and resource utilization efficiency, which is of great significance to the efficient operation of power
Xiaotong Yao, Xiaoli Fu, Chaofei Zong
semanticscholar   +1 more source

Load Forecasting Model Using LSTM for Indian State Load Dispatch Centre

Electrica
This paper presents an approach to address the critical challenge of load forecasting in the Indian state of Odisha. Motivated by the necessity for accurate predictions to support efficient planning and operation of the power system network, the work ...
Rashmi Bareth, Matushree Kochar, Anamika Yadav, Mohammad Pazoki   +3 more
doaj   +1 more source

Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms

IEEE Access, 2019
Load forecasting is useful for various applications, including maintenance planning. The study of load forecasting using recent state-of-the-art hybrid artificial intelligence (AI) and deep learning (DL) techniques is limited in South Africa (SA) and ...
Sibonelo Motepe, Ali N. Hasan, Riaan Stopforth   +2 more
doaj   +1 more source

Load pocket forecasting software [PDF]

IEEE PES Power Systems Conference and Exposition, 2004., 2005
In this paper we describe the load pocket forecasting software that can be used by electric utilities to estimate and forecast the load growth in different service areas. The software builds statistical load models for various service areas (load pockets), estimates weather-normalized loads, estimates the ratios between the actual peak loads and the ...
E.A. Feinberg, D. Genethliou, J.T. Hajagos, B.G. Irrgang, R.J. Rossin   +4 more
openaire   +1 more source

A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method

Energies, 2022
In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load.
Sanlei Dang, Long Peng, Jingming Zhao, Jiajie Li, Zhengmin Kong   +4 more
doaj   +1 more source