Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system

2021 ◽  
Vol 33 ◽  
pp. 101854 ◽  
Author(s):  
Xiuming Li ◽  
Zongwei Han ◽  
Tianyi Zhao ◽  
Jili Zhang ◽  
Da Xue
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Air Conditioning ◽  
Temperature Prediction ◽  
Indoor Temperature ◽  
Elman Neural Network ◽  
Air Conditioning System

A Novel Forecast Method for Air-Conditioning Load of Public Building Considering Accumulated Temperature Effect

2015 ◽  
Vol 9 (1) ◽  
pp. 363-367
Author(s):  
Qingshan Xu ◽  
Xufang Wang ◽  
Chenxing Yang ◽  
Hong Zhu ◽  
Qingguo Yan
Keyword(s):  
Neural Network ◽  
Temperature Effect ◽  
Air Conditioning ◽  
Forecast Accuracy ◽  
Power Network ◽  
Forecast Method ◽  
Elman Neural Network ◽  
Public Building ◽  
Effective Accumulated Temperature ◽  
Accumulated Temperature

It has great significance to estimate the schedulable capacity of air-conditioning load of public building for participating the power network regulation by forecasting the air-conditioning load accurately. A novel forecast method considering the accumulated temperature effect is proposed in this paper based on Elman neural network. Firstly, the starting and ending date for forecast considering the accumulated temperature effect are determined by providing the five day sliding average thermometer algorithm which is usually adopted in aerology research. Then, the effective accumulated temperature of each day is calculated. Finally, take the effective accumulated temperature, temperature and humidity into consideration, the air-conditioning load of public building in the forecast day is acquired by Elman neural network. Simulated results show that the higher forecast accuracy can be achieved by considering the accumulated temperature effect.

Time-delay characteristics of air-conditioning system for subway trains

2021 ◽  
pp. 102731
Author(s):  
Yuanlong Zhou ◽  
Haiquan Bi ◽  
Honglin Wang ◽  
Yichen Zhao ◽  
Bo Lei
Keyword(s):  
Time Delay ◽  
Air Conditioning ◽  
Air Conditioning System

scholarly journals Thermal comfort control via air conditioning system using fuzzy neural network feedback controller

2020 ◽  
Vol 19 (2) ◽  
pp. 586
Author(s):  
Somaye A. Mohamadi ◽  
Abdulraheem J. Ahmed
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Environmental Conditions ◽  
Air Conditioning ◽  
Fuzzy Controller ◽  
Environmental Parameters ◽  
Thermal Conditions ◽  
Real Time Control ◽  
Air Conditioning System ◽  
Air Conditioning Systems

<span>Despite their complexity and uncertainty, air conditioning systems should provide the optimal thermal conditions in a building. These controller systems should be adaptable to changes in environmental parameters. In most air conditioning systems, today, there are On/Off controllers or PID in more advanced types, which, due to different environmental conditions, are not optimal and cannot provide the optimal environmental conditions. Controlling thermal comfort of an air conditioning system requires estimation of thermal comfort index. In this study, fuzzy controller was used to provide thermal comfort in an air conditioning system, and neural network was used to estimate thermal comfort in the feedback path of the controller. Fuzzy controller has a good response given the non-linear features of air conditioning systems. In addition, the neural network makes it possible to use thermal comfort feedback in a real-time control.</span>

Neural network air-conditioning system for individual comfort

1991 ◽  
Author(s):  
Toshikazu Takemori ◽  
Nobuji Miyasaka ◽  
Shozo Hirose
Keyword(s):  
Neural Network ◽  
Air Conditioning ◽  
Air Conditioning System

scholarly journals A hybrid elman neural network predictor for time series prediction

2018 ◽  
Vol 7 (2.20) ◽  
pp. 159 ◽  
Author(s):  
N Mohana Sundaram ◽  
S N. Sivanandam
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Delay ◽  
Adaptive Learning ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Series Data ◽  
Elman Neural Network ◽  
Dynamic Processing ◽  
Elman Recurrent Neural Network

Artificial Neural Networks have become popular in the world of prediction and forecasting due to their nonlinear nonparametric adaptive-learning property. They become an important tool in data analysis and data mining applications. Elman neural network due to its recurrent nature and dynamic processing capabilities can perform the prediction process with a good range of accuracy. In this paper an Elman recurrent Neural Network is hybridised with a time delay called a tap delay line for time series prediction process to improve its performance. The Elman neural network with the time delay inputs is trained tested and validated using the solar sun spot time series data that contains the monthly mean sunspot numbers for a 240 year period having 2899 data values. The results confirm that the proposed Elman network hybridised with time delay inputs can predict the time series with more accurately and effectively than the existing methods.  

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