Bayesian - BP Neural Network based Short-term Load Forecasting for power system

2010 ◽  
Author(s):  
Yuan Ning ◽  
Yufeng Liu ◽  
Qiang Ji
Keyword(s):  
Neural Network ◽  
Power System ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
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 Short-Term Load Forecasting of Power System Based on Improved BP Neural Network

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power System ◽  
Bp Neural Network ◽  
Normal Load ◽  
Load Forecasting ◽  
System Optimization ◽  
Forecasting Model ◽  
Short Term ◽  
Short Term Load Forecasting

Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

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.

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