论文信息 - Power Consumption Modeling for Indoor Environment Using Artificial Neural Network

Power Consumption Modeling for Indoor Environment Using Artificial Neural Network

In this paper, a new Artificial Neural Network (ANN) model that relates human comfort and electrical power consumption of a building with temperature, illumination and carbon dioxide (CO2) level inside the building is developed. The model has been developed using samples of simulated data representing the indoor environment variables. Results have shown that neural network with 14 hidden layer neurons produces outputs that is closest to the actual system outputs.

Hazlina Selamat | Ahmad Faiz Ab Rahman | Fatimah Sham Ismail | F. S. Ismail | H. Selamat

[1] P. O. Fanger,et al. Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[2] D. Kolokotsaa,et al. Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks , 2003 .

[3] A.J. Calleja,et al. Intelligent control system for fluorescent lighting based on LonWorks technology , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[4] S. Ari,et al. Fuzzy Logic and Neural Network Approximation to Indoor Comfort and Energy Optimization , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[5] Benjamin H. Evans. Daylight in architecture , 1981 .

[6] Jiong Shu,et al. Experimental design and the GA-BP prediction of human thermal comfort index , 2011, ICNC.

[7] Hui Xie,et al. Prediction of Indoor Air Quality Using Artificial Neural Networks , 2009, 2009 Fifth International Conference on Natural Computation.

[8] Lingfeng Wang,et al. Multi-objective optimization for decision-making of energy and comfort management in building automation and control , 2012 .

[9] Ma Bingxin,et al. Experimental design and the GA-BP prediction of human thermal comfort index , 2011, 2011 Seventh International Conference on Natural Computation.