Research on Energy-saving Regulation Model of Climate Compensation for Central Heating Station Based on Artificial Neural Network

Author(s):  
Zhang Jun ◽  
Zhang Qiang ◽  
Chai Yan-hong
Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2445 ◽  
Author(s):  
Jin-Hyun Lee ◽  
Yong-Shik Kim ◽  
Jae-Hun Jo ◽  
Hyun Cho ◽  
Young-Hum Cho

Achieving energy efficiency by improving the operating method of the system used in existing buildings is attracting considerable attention. The Building Design Criteria for Energy Saving was established to induce energy saving design in the domestic construction field, and the introduction of a free-cooling system, such as an economizer system, as an item of the mechanical sector, was evaluated. The economizer is an energy saving method that reduces the building load by introducing outdoor air through damper control according to the indoor and outdoor conditions. The system consists of dry-bulb temperature control and enthalpy control and the mixed air temperature is kept constant in the conventional economizer controls. On the other hand, in dry-bulb temperature control, when the set value of the mixed air temperature is changed according to the load, additional energy savings are expected compared to the conventional control method. Therefore, this paper proposes an economizer control that makes the mixed air temperature variable according to the load in a Constant Air Volume single duct system. For this, a load prediction is needed and an Artificial Neural Network is used for the load prediction. In addition, the relationship between the mixed air temperature and energy were analyzed using the BIN method and TRNSYS 17. Based on the results of previous analysis, a control method which predicting the load using Artificial Neural Network and controlling the mixed air temperature according to the predicted load in the dry-bulb temperature control of a Constant Air Volume single duct system is proposed and the proposed control was applied to the dynamic simulation program and compared with the conventional control. The results show that the temperature of each room was 21–23 °C in summer and 22.5–26 °C in winter when the economizer was controlled using the proposed control method and the energy consumption analysis showed that 19% of the energy was reduced compared to the conventional method when the proposed method was controlled.


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.


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