Research on Intelligent Control Technology with Building Energy Control Model Based on Intelligent Control Algorithm

Wei Ping Wang; Li Zhou

doi:10.4028/www.scientific.net/AMR.1014.329

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Research on Intelligent Control Technology with Building Energy Control Model Based on Intelligent Control Algorithm
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1014Research on Intelligent Control Technology with...

Research on Intelligent Control Technology with Building Energy Control Model Based on Intelligent Control Algorithm

Abstract:

For the current smart building energy control algorithms are still large energy loss, poor energy-saving effect and other issues, this paper presents a fuzzy neural network algorithm based on improved BP algorithm, the improved algorithm of BP neural network algorithm first reverse dissemination and weighting coefficients are adjusted to accelerate the convergence rate of the original algorithm, and then build the improved BP neural network algorithm for fuzzy neural network, and then to improve it fuzzy membership function parameters to improve the efficiency of fuzzy neural network learning. Simulation results show that the proposed fuzzy neural network algorithm based on improved BP algorithm in the intelligent building energy control, with the algorithm is better than traditional BP neural network energy savings, reducing the energy loss rate.

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Periodical:

Advanced Materials Research (Volume 1014)

Pages:

329-332

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1014.329

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Online since:

July 2014

Authors:

Wei Ping Wang*, Li Zhou

Keywords:

BP Neural Network Algorithm, Building Energy Control, Fuzzy Membership Function, Fuzzy Neural Network

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* - Corresponding Author

References

[1] Zhou Peng, Chen Yang, Guan Wenyue. Construction equipment on energy-saving control strategy[J]. Journal of Nanjing University of Science and Technology. 2014, (1): 54-58.

Google Scholar

[2] Xu Feng, Tang Zhou. Passive energy-saving design in the renovation of Changsha south railway station[J]. Industrial Construction. 2014, 44(3): 177-179.

Google Scholar

[3] LI Nun. Distributed relay assignment method in green energy efficiency[J]. Journal of Xidian University. 2014, 41(2): 159-164.

Google Scholar

[4] Wei Liming, Chen Jianchun. Analysis and applied research on problem of building electrical energy saving[J]. Electrotechnical Application. 2014, (2): 44-47.

Google Scholar

[5] Zou Cui, Liu Xiuli. Urban building energy consumption calcaulation based on the IPAT theory[J]. China Population, Resources and Environment. 2012, 22(11): 82-85.

Google Scholar