Short-Term Power Load Forecasting Based on Self-Adapting PSO-BP Neural Network Model

2012 ◽  
Author(s):  
Yaoyao He ◽  
Qifa Xu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Bp Neural Network Model

A Short-Term Power Load Forecasting Method Based on BP Neural Network

2014 ◽  
Vol 494-495 ◽  
pp. 1647-1650 ◽  
Author(s):  
Ling Juan Li ◽  
Wen Huang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Load Forecasting ◽  
High Accuracy ◽  
Back Propagation Algorithm ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

Prediction Models of Short-Term Traffic Flow Based on Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2908-2911 ◽  
Author(s):  
Chao Jun Dong ◽  
Ang Cui
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Topology ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Measured Data ◽  
Short Term ◽  
Hidden Layer ◽  
Bp Neural Network Model

For the city’s road conditions, a nonlinear regression prediction model based on BP Neural Network was built. The simulation shows it has good adaptability and strong nonlinear mapping ability. Using the wavelet basis function as hidden layer nodes transfer function, a BP-Neural- Network-topology-based Wavelet Neural Network model was proposed. The model can overcome the defects of the BP Neural Network model that easy to fall into local minimum and cannot perform global search. The feasibility of the model was proved using measured data from yingbin avenue in jiangmen city.

Implementation of Power Load Forecasting Based on the Improved Neural Network Model

2015 ◽  
Vol 734 ◽  
pp. 468-471 ◽  
Author(s):  
Yang Xu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Elman Network ◽  
Bp Network ◽  
Power Load ◽  
Power Load Forecasting

This paper introduces the importance of power load forecasting, and makes forecasting based on the power load values collected in Botou City within a week. The conclusion shows that the accuracy of PSO - ELMAN network forecasting results is much higher than that of PSO - BP network forecasting results.

Load Forecasting of Coal-Fired Unit Based on SVM Model

2012 ◽  
Vol 466-467 ◽  
pp. 1015-1019 ◽  
Author(s):  
Wei Liang Liu ◽  
Yong Guang Ma ◽  
Liang Yu Ma ◽  
Yong Jun Lin ◽  
Shuang Sai Liu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Svm Model ◽  
Bp Neural Network Model

In order to obtain accurate load forecasting of coal-fired unit, a new algorithm based on Support Vector Machine (SVM) method is presented. This algorithm establishes a model to reflect the complicated relation between the load of coal-fired unit and the furnace flame Images. The trained SVM model is applied to a 660MW coal-fired unit to forecast the load with two groups of test samples. The results are compared with that of BP neural network model. It is shown the SVM model is more accurate than the BP NN model. The SVM method can satisfy the demand of engineering applications with the advantages of high forecasting accuracy and more generalized performance.

Integrated Optimum Gray Neural Network Model of Monthly Power Load Forecasting Based on Optimum Credibility

2005 ◽  
Author(s):  
Dong-Xiao Niu ◽  
Jian-Chang Lu ◽  
Yuan-Yuan Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Historical Data ◽  
Load Forecasting ◽  
Electric Capacity ◽  
Power Load ◽  
Power Load Forecasting ◽  
Calculation Results ◽  
Trend Forecasting

For the monthly load with double trends of increasing and fluctuating, the integrated optimum gray neural network model of monthly load forecasting is proposed in the paper for the first time. In the model, we regard vertical historical data as the primitive array to forecast increasing trend by the gray model, and regard horizontal historical data as the primitive array to forecast fluctuating trend by the . Based on that, the concept of the optimum credibility is introduced, and the integrated optimum model is built in the paper. In the model, the double trends of monthly load are considered at the same time and the two models’ modeling characters are given attention. So the integrated model is superior to the model of single trend forecasting. An application case of the power load forecasting is given. Through the analysis to the monthly supplying electric capacity in LiaoNing power system, the corresponding integrated optimum gray neural network model is built. It is compared with other algorithms. The calculation results prove that this method raises accuracy of the monthly load forecasting greatly. For the weekly and seasonal load with the same double trends, the method has same suitability to them.

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