Short-Term Load Forecasting for Jordan Power System Based on NARX-ELMAN Neural Network and ARMA Model

2021 ◽  
Vol 44 (3) ◽  
pp. 356-363
Author(s):  
Lina Alhmoud ◽  
Qusay Nawafleh
Keyword(s):  
Neural Network ◽  
Power System ◽  
Load Forecasting ◽  
Arma Model ◽  
Short Term ◽  
Elman Neural Network ◽  
Short Term Load Forecasting

A Method Based on Wavelet Neural Network for Power System Short Term Load Forecasting

2014 ◽  
Vol 521 ◽  
pp. 303-306 ◽  
Author(s):  
Hong Mei Zhong ◽  
Jie Liu ◽  
Qi Fang Chen ◽  
Nian Liu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Wavelet Neural Network ◽  
Signal Spectrum ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Sequence ◽  
Daily Load

The short-term load of Power System is uncertain and the daily-load signal spectrum is continuous. The approach of Wavelet Neural Network (WNN) is proposed by combing the wavelet transform (WT) and neural network. By the WT, the time-based short-term load sequence can be decomposed into different scales sequences, which is used to training the BP neural network. The short-term load is forecasted by the trained BP neural network. Select the load of a random day in Lianyungang to study, according to the numerical simulation results, the method proves to achieve good performances.

scholarly journals A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Lizhen Wu ◽  
Chun Kong ◽  
Xiaohong Hao ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Power System ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Vector ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Hybrid Neural Network ◽  
Short Term Load Forecasting

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

Comparative Study Of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning And Radial Basis Functional Neural Network

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Amit Tiwari ◽  
Adarsh Dhar Dubey ◽  
And Devesh Patel
Keyword(s):  
Neural Network ◽  
Power System ◽  
Structural Changes ◽  
Unit Commitment ◽  
Network Reliability ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Short Term Load Forecasting

The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling & for system security such as peak load shaving by power interchange with interconnected utilities. With structural changes to electricity in recent years, there is an emphasis on Short Term Load Forecasting (STLF).STLF is the essential part of power system planning & operation. Basic operating functions such as unit commitment, economic dispatch, and fuel scheduling & unit maintenance can be performed efficiently with an accurate forecast. Short term load forecasting can help to estimate load flows & to make decisions that can prevent overloading. Timely implementations of such decisions lead to improvement of network reliability & to the reduced occurrences of equipment failures & blackouts. The aim of short term load forecasting is to predict future electricity demands based, traditionally on historical data and predicted weather conditions. Short term load forecasting in its basic form is a statistical problem, where in the previous load values (time series variables) and influencing factors (casual variables) are used to determine the future loads.

Short-Term Load Forecasting Based on GA-Elman Model

2014 ◽  
Vol 986-987 ◽  
pp. 520-523
Author(s):  
Wen Xia You ◽  
Jun Xiao Chang ◽  
Zi Heng Zhou ◽  
Ji Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Relative Error ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Elman Neural Network ◽  
Learning Efficiency ◽  
Multiple Layer ◽  
Short Term Load Forecasting

Elman Neural Network is a typical neural-network which shares the characteristics of multiple-layer and dynamic recurrent, and it’s more suitable than BP Neural Network when it’s applied to forecast the short-term load with periodicity and similarity. To solve the problem that Elman Neural Network lacks learning efficiency, GA-Elman model is established by optimizing the weights and thresholds using Genetic Algorithm. An example is then given to prove the effectiveness of GA-Elman model, using the load data of a certain region. Relative error and MSE have been considered as criterions to analyze the results of load forecasting. By comparing the results calculated by BP, Elman and GA-Elman model, the effectiveness of GA-Elman model is verified, which will improve the accuracy of short-term load forecasting.

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