scholarly journals Solar Irradiance Short-Term Prediction Model Based on BP Neural Network

2011 ◽  
Vol 12 ◽  
pp. 488-494 ◽  
Author(s):  
Zhe Wang ◽  
Fei Wang ◽  
Shi Su
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Solar Irradiance ◽  
Short Term ◽  
Model Based ◽  
Term Prediction ◽  
Short Term Prediction

Short-Term Prediction Model for a Grid-Connected Photovoltaic System Using EMD and GABPNN

2013 ◽  
Vol 291-294 ◽  
pp. 74-82
Author(s):  
Zeng Wei Zheng ◽  
Yuan Yi Chen ◽  
Xiao Wei Zhou ◽  
Mei Mei Huo ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Prediction Method ◽  
Photovoltaic System ◽  
Photovoltaic Systems ◽  
Short Term ◽  
Model Based ◽  
Term Prediction ◽  
Short Term Prediction

The integration between photovoltaic systems and tradition grid have a lot of challenges. To accurately predict is a key to solve these challenges. Due to complex, non-linear and non-stationary characteristics, it is difficult to accurately predict the power of photovoltaic systems. In this paper, a short-term prediction model based on empirical mode decomposition (EMD)and back propagation neural network(BPNN) was constructed, and use genetic algorithm as the learn algorithm of BPNN. The power data after pre-processing is decomposed into several components, then using prediction model based on BPNN and genetic algorithm to predict each component, and all the component prediction values were aggregated to obtain the ultimate predicted result. The simulation shows the purposed prediction model has higher prediction precision compare with traditional neural network prediction method and it is an effective prediction method of photovoltaic systems.

Short-Term Prediction on the Time Series of PCP Speed Based on Elman Neural Network

2012 ◽  
Vol 569 ◽  
pp. 749-753
Author(s):  
Xiao Ren Lv ◽  
Xuan Luo ◽  
Shi Jie Wang ◽  
Rui Nie
Keyword(s):  
Neural Network ◽  
Nonlinear Dynamics ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Varying ◽  
Rapid Convergence ◽  
Short Term ◽  
Elman Neural Network ◽  
Term Prediction ◽  
Short Term Prediction

Elman neural network is a classical kind of recurrent neural network. It is well suitable to predict complicated nonlinear dynamics system like progressing cavity pump (PCP) speed due to its greater properties of calculation and adaptation to time-varying with the comparison of BP neural network. This paper provides one method to create, predict, and decide the model of PCP speed based on Elman neural network. At the same time, short-term prediction is made on time series of PCP speed using this model. The results of the experiment show that the model owns higher precision, steadier forecasting effect and more rapid convergence velocity, displaying that this kind of model based on Elman neural network is feasible and efficient to predict short-term PCP speed.

scholarly journals Bayesian regularized NAR neural network based short-term prediction method of water consumption

2019 ◽  
Vol 118 ◽  
pp. 03024
Author(s):  
Jianyu Liu ◽  
Linxue Zhao ◽  
Yanlong Mao
Keyword(s):  
Neural Network ◽  
Water Supply ◽  
Prediction Model ◽  
Water Consumption ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Short Term ◽  
Neural Network Prediction ◽  
Short Term Prediction ◽  
Consumption Prediction

With the continuous construction of urban water supply infrastructure, it is extremely urgent to change the management mode of water supply from traditional manual experience to modern and efficient means. The water consumption forecast is the premise of water supply scheduling, and its accuracy also directly affects the effectiveness of water supply scheduling. This paper analyzes the regularity of water consumption time series, establishes a short-term water consumption prediction model based on Bayesian regularized NAR neural network, and compares and evaluates the prediction effect of the model. The verification results show that the Bayesian based NAR neural network prediction model has higher adaptability to the water consumption prediction than the standard BP neural network and the Bayesian regularized BP neural network. The prediction accuracy can more accurately reflect the short-term variation of water consumption.

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