Implementation of a Hybrid Technique for the Predictive Control of the Residential Heating Ventilation and Air Conditioning Systems

Authors

  • M. Ray School of Electrical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, India
  • P. Samal School of Electrical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, India
  • C. K. Panigrahi School of Electrical Engineering, Kalinga Institute of Industrial Technology Deemed to be University, India

Abstract

Since daily energy needs are increasing, it is imperative to find ways to save energy, such as improving the energy consumption of buildings. Heating Ventilating and Air-Conditioning (HVAC) loads account for the majority of a building's energy use. The accurate estimation of energy consumption and the examination of various ways to improve the energy efficiency of buildings are very important. This paper presents an analysis of HVAC loads in a residential building by examining three Neural Networks (NNs): Feed-Forward (FF), Cascaded Forward Backpropagation (CFBP), and Elman Backpropagation (EBP) networks, based on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Relative Error (MRE). Furthermore, these networks were combined in hybrid NNs to obtain more optimized results. These results were also compared with other approaches and showed better prediction performance.

Keywords:

HVAC loads, neural networks, energy management, hybrid networks

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References

"IEA – International Energy Agency," IEA. https://www.iea.org.

A. Yezioro, B. Dong, and F. Leite, "An applied artificial intelligence approach towards assessing building performance simulation tools," Energy and Buildings, vol. 40, no. 4, pp. 612–620, Jan. 2008. DOI: https://doi.org/10.1016/j.enbuild.2007.04.014

T. Catalina, J. Virgone, and E. Blanco, "Development and validation of regression models to predict monthly heating demand for residential buildings," Energy and Buildings, vol. 40, no. 10, pp. 1825–1832, Jan. 2008. DOI: https://doi.org/10.1016/j.enbuild.2008.04.001

B. Dong, C. Cao, and S. E. Lee, "Applying support vector machines to predict building energy consumption in tropical region," Energy and Buildings, vol. 37, no. 5, pp. 545–553, May 2005. DOI: https://doi.org/10.1016/j.enbuild.2004.09.009

Q. Li, Q. Meng, J. Cai, H. Yoshino, and A. Mochida, "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, vol. 86, no. 10, pp. 2249–2256, Oct. 2009. DOI: https://doi.org/10.1016/j.apenergy.2008.11.035

A. Tsanas and A. Xifara, "Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools," Energy and Buildings, vol. 49, pp. 560–567, Jun. 2012. DOI: https://doi.org/10.1016/j.enbuild.2012.03.003

E. A. Al-Ammar, N. H. Malik, and M. Usman, "Application of using Hybrid Renewable Energy in Saudi Arabia," Engineering, Technology & Applied Science Research, vol. 1, no. 4, pp. 84–89, Aug. 2011. DOI: https://doi.org/10.48084/etasr.33

B. Zahran, "Using Neural Networks to Predict the Hardness of Aluminum Alloys," Engineering, Technology & Applied Science Research, vol. 5, no. 1, pp. 757–759, Feb. 2015. DOI: https://doi.org/10.48084/etasr.529

S. N. Truong, "A Low-cost Artificial Neural Network Model for Raspberry Pi," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5466–5469, Apr. 2020. DOI: https://doi.org/10.48084/etasr.3357

R. Yao, B. Li, and K. Steemers, "Energy policy and standard for built environment in China," Renewable Energy, vol. 30, no. 13, pp. 1973–1988, Oct. 2005. DOI: https://doi.org/10.1016/j.renene.2005.01.013

J.-S. Chou and D.-K. Bui, "Modeling heating and cooling loads by artificial intelligence for energy-efficient building design," Energy and Buildings, vol. 82, pp. 437–446, Oct. 2014. DOI: https://doi.org/10.1016/j.enbuild.2014.07.036

Y. Sonmez, U. Guvenc, H. T. Kahraman, and C. Yilmaz, "A comperative study on novel machine learning algorithms for estimation of energy performance of residential buildings," in 2015 3rd International Istanbul Smart Grid Congress and Fair (ICSG), Istanbul, Turkey, Apr. 2015, pp. 1–7. DOI: https://doi.org/10.1109/SGCF.2015.7354915

W. Qiao and Z. Yang, "Modified Dolphin Swarm Algorithm Based on Chaotic Maps for Solving High-Dimensional Function Optimization Problems," IEEE Access, vol. 7, pp. 110472–110486, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2931910

A. Moradzadeh, A. Mansour-Saatloo, B. Mohammadi-Ivatloo, and A. Anvari-Moghaddam, "Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings," Applied Sciences, vol. 10, no. 11, Jan. 2020, Art. no. 3829. DOI: https://doi.org/10.3390/app10113829

S. Das, A. Swetapadma, C. Panigrahi, and A. Y. Abdelaziz, "Improved Method for Approximation of Heating and Cooling Load in Urban Buildings for Energy Performance Enhancement," Electric Power Components and Systems, vol. 48, no. 4–5, pp. 436–446, Mar. 2020. DOI: https://doi.org/10.1080/15325008.2020.1793838

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How to Cite

[1]
Ray, M., Samal, P. and Panigrahi, C.K. 2022. Implementation of a Hybrid Technique for the Predictive Control of the Residential Heating Ventilation and Air Conditioning Systems. Engineering, Technology & Applied Science Research. 12, 3 (Jun. 2022), 8772–8776. DOI:https://doi.org/10.48084/etasr.5027.

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