Prediction Research on the Energy Consumption of Public Building Based on MLR-BP Neural Network

2019 ◽  
Author(s):  
Jingjian Yang ◽  
Yuning Amy Xie ◽  
Hongyan Ma
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Public Building

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.

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 Model of the Sinter Comprehensive Performance Based on Neural Network

2013 ◽  
Vol 753-755 ◽  
pp. 62-65 ◽  
Author(s):  
Wei Chen ◽  
Hui Juan Zhang ◽  
Bao Xiang Wang ◽  
Ying Chen ◽  
Xing Li
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Chemical Composition ◽  
Bp Neural Network ◽  
Steel Industry ◽  
Iron And Steel Industry ◽  
Accuracy Rate ◽  
Iron And Steel ◽  
Yield Quality ◽  
The Stability

The sinter quality and the stability of composition could directly affect the yield, quality and energy consumption of ironmaking production. It is important for iron and steel industry to steadily control sinter chemical composition and analyze sintering energy consumption. The MATLAB m file editor was used to write code directly in this paper. A predictive system for two important sinter chemical composition (TFe and FeO), sinter output and sintering solid fuel consumption of was established based on BP neural network, which was trained by actual production data.) The application results show that the prediction system has high accuracy rate, stability and reliability, the sintering productivity was improved effectively.

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.

Analysis of LED Light Source Energy Consumption for Lighting System Optimized by BP Neural Network Based on Genetic Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 2509-2514
Author(s):  
De Qiang Wei ◽  
Hu Cheng Chen ◽  
Jun Wei Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Energy Consumption ◽  
Light Source ◽  
Bp Neural Network ◽  
Electricity Consumption ◽  
Analysis Model ◽  
Led Light ◽  
Working Face ◽  
Accuracy Of Prediction

To study influence of LED light source lifetime on electricity consumption, optimization of BP neural network is adopted to establish analysis model of energy consumption for neural network, regarding environmental illumination, LED working face illumination, attenuation rates of LED lifetime as input parameters and PWM as output parameters. Under future lifetime of LED, energy consumption is predicted through the model. Results show BP neural network based on genetic algorithm can calculate energy consumption of LED light source quickly and accuracy of prediction is high. The method can be well used to predict energy consumption of short-time LED.

scholarly journals A Method for Short-Term Prediction of the Metro Station’s Individual Energy Consumption Item Based on G-ACO-BP Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guorong Sha ◽  
Qing Qian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Air Conditioning ◽  
Principal Component ◽  
Neural Model ◽  
Primary Energy ◽  
Kernel Principal Component Analysis ◽  
Short Term ◽  
Metro Station

This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing metro station energy consumption through the kernel principal component analysis (KPCA). Second, improved genetic-ant colony optimization (G-ACO) was fused into the BP neural network to train and optimize the connection weights and thresholds between each BP neural network layer. The paper then builds a G-ACO-BP neural model to make short-term predictions about different energy consumption in the metro station to predict the energy consumed by power, lighting, and ventilated air conditioning. The experimental results showed that the G-ACO-BP neural model could give a more accurate and effective prediction for the main energy consumption in a metro station.

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