Matlab for Forecasting of Energy Consumption Based on BP Neural Network

2011 ◽  
Vol 281 ◽  
pp. 54-58
Author(s):  
Chang Huan Tu ◽  
Guo You Li ◽  
Liang Zhao
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Single Layer ◽  
Training Sample ◽  
Consumption Data ◽  
The Future

The paper used Matlab to program for single-layer BP neural network, selected the annual energy consumption data as the training sample and inspection sample to train BP neural network, then, predict the future China's energy consumption quantity.

Prediction of Airport Energy Consumption Using a Hybrid Grey Neural Network Model

2012 ◽  
Vol 608-609 ◽  
pp. 1252-1256 ◽  
Author(s):  
Jing Jie Chen ◽  
Chen Xiao ◽  
Wen Gao Qian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Consumption Data ◽  
Prediction And Control ◽  
And Control

Prediction and control of airport energy consumption plays an important role in promoting energy saving and emission reduction in the civil aviation industry. In view of the complexity and nonlinearity of energy consumption system, as well as a small number of airport energy consumption data, this study develops a hybrid grey neural network model, which organically combines GM (1, 1) model and BP neural network in parallel and series connections, on the basis of analysis of main prediction methods. With energy consumption data from one Chinese airport for the whole year 2010, this study analyzes and compares different prediction results using different models through matlab. It shows that the hybrid model has a better accurate prediction, and its prediction accuracy can be controlled within 7%.

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinsong Tu ◽  
Yuanzhen Liu ◽  
Ming Zhou ◽  
Ruixia Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Compressive Strength ◽  
Thermal Insulation ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Training Sample ◽  
Recycled Aggregate ◽  
Generalization Performance ◽  
Content Type

Purpose This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately. Design/methodology/approach The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform. Findings Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better. Originality/value The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

Energy Consumption Prediction Model of SiCp/Al Composite in Grinding Based on PSO-BP Neural Network

2020 ◽  
Vol 305 ◽  
pp. 163-168
Author(s):  
Peng Gu ◽  
Chuan Min Zhu ◽  
Yin Yue Wu ◽  
Andrea Mura
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Swarm Optimization ◽  
Al Composite ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

As the typical particle-reinforced aluminum matrix composite, SiCp/Al composite has low density, high elastic modulus and high thermal conductivity, and is one of the most competitive metal matrix composites. Grinding is the main processing technique of SiCp/Al composite, energy consumption of the grinding process provides guidance for the energy saving, which is the aim of green manufacturing. In this paper, grinding experiments were designed and conducted to obtain the energy consumption of the grinding machine tool. The Particle Swarm Optimization (PSO) BP neural network prediction model was applied in the energy consumption prediction model of SiCp/Al composite in grinding. It showed that the Particle Swarm Optimization (PSO) BP neural network prediction model has high prediction accuracy. The prediction model of energy consumption based on PSO-BP neural network is helpful in energy saving, which contributes to greening manufacturing.

Research on Wireless Sensor Networks Data Fusion Algorithm Based on COA-BP

2014 ◽  
Vol 539 ◽  
pp. 247-250
Author(s):  
Xiao Xiao Liang ◽  
Li Cao ◽  
Chong Gang Wei ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Network Lifetime ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Bp Algorithm ◽  
Wireless Sensor

To improve the wireless sensor networks data fusion efficiency and reduce network traffic and the energy consumption of sensor networks, combined with chaos optimization algorithm and BP algorithm designed a chaotic BP hybrid algorithm (COA-BP), and establish a WSNs data fusion model. This model overcomes shortcomings of the traditional BP neural network model. Using the optimized BP neural network to efficiently extract WSN data and fusion the features among a small number of original date, then sends the extracted features date to aggregation nodes, thus enhance the efficiency of data fusion and prolong the network lifetime. Simulation results show that, compared with LEACH algorithm, BP neural network and PSO-BP algorithm, this algorithm can effectively reduce network traffic, reducing 19% of the total energy consumption of nodes and prolong the network lifetime.

scholarly journals Modeling the energy consumption of trains by applying neural networks

2017 ◽  
Vol 232 (3) ◽  
pp. 816-823 ◽  
Author(s):  
Juan D Pineda-Jaramillo ◽  
Ricardo Insa ◽  
Pablo Martínez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Power Consumption ◽  
High Accuracy ◽  
Calibration And Validation ◽  
The Neural Network ◽  
Consumption Data ◽  
Metro System

This paper presents the training of a neural network using consumption data measured in the underground network of Valencia (Spain), with the objective of estimating the energy consumption of the systems. After the calibration and validation of the neural network using part of the gathered consumption data, the results obtained show that the neural network is capable of predicting power consumption with high accuracy. Once fully trained, the network can be used to study the energy consumption of a metro system and for testing the hypothetical operation scenarios.

scholarly journals Prediction of Future State Based on Up-To-Date Information of Green Development Using Algorithm of Deep Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liyan Sun ◽  
Li Yang ◽  
Junqi Zhu
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Deep Neural Network ◽  
Environmentally Friendly ◽  
Green Energy ◽  
Wind Generators ◽  
The Future ◽  
Future Prediction ◽  
Neural Network Algorithm ◽  
Current Energy

In this study, the focus was on the development of green energy and future prediction for the consumption of current energy sources and green energy development using an improved deep learning (DL) algorithm. In addition to the analysis of the current energy consumption used for the natural gas and oil as fuel, deep neural network algorithm is used to train the system as well as to process the data obtained previously, ranging from literature from the year 2003 until the year 2019, for consumption of fuel. Also, using the proposed algorithm to predict the development of green energy consumption till 2030 is presented in terms of solar and wind generators. The resulting study also focuses on depletion of energy currently used or pollution caused because of it. The green energy controlling issue can take effect by using multiple layers of handling different features extracted from different sources and then learning the system to control it.This study aims to take advantage of carbon emissions to reduce their impact and dependence in the future on environmentally friendly renewable energies. Predicting the correct and precise amount of energy consumption and increasing the amount of environmentally friendly energy lead to a healthy ecosystem. The expected green energy consumption in the future is almost 78.25 EJ in 2030 and will be, in total energy average, 56% in 2045. The aim is to reduce dependency on costly and environmentally harmful fuels.

Study of Digital Character Recognition Based on BP Neural Networks

2013 ◽  
Vol 333-335 ◽  
pp. 856-859 ◽  
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Solution Space ◽  
Training Sample ◽  
Back Propagation Algorithm ◽  
Sample Object ◽  
Training Error ◽  
Hidden Layer

A digital character recognition method is presented based on BP Neural Network. This paper preprocesses the digital character image and extracts character feature, then uses BP Neural Network to recognize digital character. Back Propagation algorithm seeks network weights to minimize training error in the solution space. A network with hidden layer is created at first, then an input sample vector is sent to network input terminal and the square error E between output values and training sample object output values is calculated. Above process is repeated for input samples of training sets until the error is reduced within the limits of the threshold. The results show that the method presented has good accuracy, quick speed and strong robustness for realtime application.

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