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Prediction of Building Energy Consumption Based on IPSO-CLSTM Neural Network

Qingwu Fan, Li Shuo, Xudong Liu

Abstract


Accurate prediction of building load is essential for energy saving and environmental protection. Exploring the impact of building characteristics on heating and cooling load can improve energy efficiency from the design stage of the building. In this paper, a prediction model of building heating and cooling loads is proposed, which based on Improved Particle Swarm Optimization (IPSO) algorithm and Convolution Long Short-Term Memory (CLSTM) neural network model. Firstly, the characteristic variables are extracted and evaluated by Spearmans correlation coefficient method; Then the prediction model based on the CLSTM neural network is constructed to predict building heating and cooling load. The IPSO algorithm is adopted to solve the problem that manual work cannot precisely adjust parameters. In this method, the optimization ability of the PSO algorithm is improved by changing the updating rule of inertia weight and learning factors.Finally, the parameters of the neural network are taken as IPSO optimization object to improve the prediction accuracy. In the experimental stage of this paper, a variety of algorithm models are compared, and the results show that IPSO-CLSTM can get the best results in the prediction of heating and cooling load.


Keywords


Heating Load; Cooling Load; Protection; Particle Swarm Optimization; Long-Short Term Memory

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References


1. D. Rim, S. Schiavon, and W. W. Nazaroff, Energy and Cost Associated with Ventilating Office Buildings in a Tropical Climate, Plos One, vol. 10, no. 3, 2015.

2. R. Yao, B. Li, and K. Steemers, Energy policy and standard for built environment in China, Renewable Energy, vol. 30, no. 13, pp. 19731988, 2005.

3. Q. Li, Q. Meng, J. Cai, H. Yoshino, and A. Mochida, Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks, Energy Conversion and Management, vol. 50, no. 1, pp. 9096, 2009.

4. T. Catalina, V. Iordache, and B. Caracaleanu, Multiple regression model for fast prediction of the heating energy demand, Energy and Buildings, vol. 57, pp. 302312, 2013.

5. Al-Shammari, et al. "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm." Energy, vol. 95, pp. 266-273,2013.

6. 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. 437446, 2014.

7. 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. 560567, 2012.

8. Z. Wang, Y. Wang, R. Zeng, R. S. Srinivasan, and S. Ahrentzen, Random Forest based hourly building energy prediction, Energy and Buildings, vol. 171, pp. 1125, 2018.

9. Z. Wang, T. Hong, and M. A. Piette, Building thermal load prediction through shallow machine learning and deep learning, Applied Energy, vol. 263, p. 114683, 2020.

10. J. Zhao and X. Liu, A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis, Energy and Buildings, vol. 174, pp. 293308, 2018.

11. Y. Guo, J. Wang, H. Chen, G. Li, J. Liu, C. Xu, R. Huang, and Y. Huang, Machine learning-based thermal response time ahead energy demand prediction for building heating systems, Applied Energy, vol. 221, pp. 1627, 2018.

12. K. Yun, R. Luck, P. J. Mago, and H. Cho, Building hourly thermal load prediction using an indexed ARX model, Energy and Buildings, vol. 54, pp. 225233, 2012.

13. K. K. Wan, D. H. Li, D. Liu, and J. C. Lam, Future trends of building heating and cooling loads and energy consumption in different climates, Building and Environment, vol. 46, no. 1, pp. 223234, 2011.

14. L. T. Le, H. Nguyen, J. Dou, and J. Zhou, A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings Energy Efficiency for Smart City Planning, Applied Sciences, vol. 9, no. 13, pp. 2630, 2019.

15. D. T. Bui, H. Moayedi, D. Anastasios, and L. K. Foong, Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models, Applied Sciences, vol. 9, no. 17, pp. 3543, 2019.

16. G. Zhou, H. Moayedi, M. Bahiraei, and Z. Lyu, Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings, Journal of Cleaner Production, vol. 254, p. 120082, 2020.

17. Huang Yuting, and Li Chao. Accurate heating, ventilation and air conditioning system load prediction for residential buildings using improved ant colony optimization and wavelet neural network. Journal of Building Engineering, vol. 35, 2019.

18. Sanjiban Sekhar Roy, R. Roy , and V. E. Balas . Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM. Renewable and Sustainable Energy Reviews, vol. 82, pp. 4256-4268, 2018.




DOI: https://doi.org/10.32629/jai.v3i2.285

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Copyright (c) 2021 Qingwu Fan, Li Shuo, Xudong Liu

License URL: https://creativecommons.org/licenses/by-nc/4.0