A Comparison of NARX and BP Neural Network in Short-Term Building Cooling Load Prediction

2014 ◽  
Vol 513-517 ◽  
pp. 1545-1548 ◽  
Author(s):  
Yan Li Xu ◽  
Hong Xun Chen ◽  
Wang Guo ◽  
Qiu Yu Zhu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Prediction Errors ◽  
Cooling Load ◽  
Climate Data ◽  
Load Prediction ◽  
Short Term ◽  
Building Cooling

A comparison of nonlinear autoregression with exogenous inputs (NARX) neural network and back-propagation (BP) neural network in short-term prediction of building cooling load is presented in this dissertation. Both predictive models have been applied in a group of commercial buildings and analysis of prediction errors has been highlighted. Training and testing data for both prediction models have been generated from DeST (Designers Simulation Toolkits) with climate data of Shanghai. The simulation results indicate that NARX method can achieve better accuracy and generalization ability than traditional method of BP neural network. This work provides a key support in smooth and optimizing control in air-conditioning system.

Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

2014 ◽  
Vol 986-987 ◽  
pp. 524-528 ◽  
Author(s):  
Ting Jing Ke ◽  
Min You Chen ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Model Parameters ◽  
Short Term ◽  
Dynamic Prediction ◽  
Power Dynamic ◽  
Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power 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.

scholarly journals Application of Improved PSO-BP Neural Network in Cold Load Forecasting of Mall Air-Conditioning

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
JunQi Yu ◽  
WenQiang Jing ◽  
AnJun Zhao ◽  
YanHuan Ren ◽  
Meng Zhou
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Cooling Load ◽  
Random Errors ◽  
Load Prediction ◽  
Actual Application ◽  
Neural Network Prediction ◽  
Application Requirements

A combination of JMP, PSO-BP neural network, and Markov chain which aims at the low correlation between input and output data and the error of prediction model in the PSO-BP neural network prediction model is proposed. First, the JMP data processing software is used to process the input data and eliminate the samples with low coupling degree. Then, obtaining the cooling load prediction results relies on the training from the PSO-BP neural network. Finally, the final prediction results will be generated by eliminating the random errors using the Markov chain. The results show that the combination of the prediction methods has higher prediction accuracy and conforms to the change rule of the cooling load in shopping malls. Besides, the combination fits the actual application requirements as well.

Applied-Information Technology in Short-Term Wind Speed Forecast Model for Wind Farms Based on Ant Colony Optimization and BP Neural Network

2014 ◽  
Vol 662 ◽  
pp. 259-262 ◽  
Author(s):  
Qi Di Zhao ◽  
Yang Yu ◽  
Meng Meng Jia
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Ant Colony Optimization ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Wind Farms ◽  
Forecast Model ◽  
Ant Colony ◽  
Short Term

To improve the short-term wind speed forecasting accuracy of wind farms, a prediction model based on back propagation (BP) neural network combining ant colony algorithm is built to predict short-term wind speed. The input variables of BP neural network predictive model are historical wind speeds, temperature, and air pressure. Ant colony algorithm is used to optimize the weights and bias of BP neural networks. Using the ant colony optimization BP neural network model to predict the future 1h wind speed, the simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.

scholarly journals Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Pengpeng Cheng ◽  
Daoling Chen ◽  
Jianping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network

AbstractIn order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

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