참고문헌 (25)

1. Ahn, K. U., Kim, D. W., Kim, Y. J., & Park, C. S. (2016). Development of an Gaussian Process Model using a Data Filtering Method. Journal of the architectural institute of Korea planning & design, 32(4), 97-105. 

2. Afram, A., & Janabi-Sharifi, F. (2014). Review of modeling methods for HVAC systems. Applied Thermal Engineering, 67(1-2), 507-519. 

   상세보기

3. Ansett, M. (1993). Application of neural networking models to predict energy use. ASHRAE transactions, 99(1), 505-517. 

   상세보기

4. Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008). Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing. International Journal of Thermal Sciences, 47(4), 431-441. 

   상세보기

5. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. 

6. Javed, A., Larijani, H., Ahmadinia, A., & Emmanuel, R. (2014). Comparison of the robustness of RNN, MPC and ANN controller for residential heating system. Big Data and Cloud Computing (BdCloud), IEEE Fourth International Conference on 2014, 604-611. IEEE. 

7. Jovanovic, R. Z., Sretenovic, A. A., & Zivkovic, B. D. (2015). Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings, 94, 189-199. 

   상세보기

8. Kamar, H. M., Ahmad, R., Kamsah, N. B., & Mustafa, A. F. M. (2013). Artificial neural networks for automotive air-conditioning systems performance prediction. Applied Thermal Engineering, 50(1), 63-70. 

   상세보기

9. Kim, Y. M., Ahn, K. U., & Park, C. S. (2016). Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings. Sustainability, 8(6), 543. 

   상세보기

10. Lu, Y., Wang, S., Sun, Y., & Yan, C. (2015). Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming. Applied Energy, 147, 49-58. 

    상세보기

11. Nassif, N. (2014, June). Modeling and optimization of HVAC systems using artificial neural network and genetic algorithm. Building Simulation, 7(3), 237-245. Springer Berlin Heidelberg. 

    상세보기

12. Nishiguchi, J., Konda, T., & Dazai, R. (2010). Data-driven optimal control for building energy conservation. Proceedings of SICE Annual Conference on 2010, 116-120. 

13. Park, S., Ahn, K. U., Hwang, S., Choi, S., & Park, C. S. (2017). Machine Learning Model for a Turbo Chiller and Validity Check of the Model by Gaussian Mixture Model. 2017 Autumn Annual Conference of Architectural Institute of Korea, 37(2), 550-551 

14. Park, S., Ahn, K. U., Hwang, S., Choi, S., & Park, C. S. (2018). Determination of optimal start/stop time of a chiller using machine learning. 2018 Autumn Annual Conference of Architectural Institute of Korea, 38(2), 297-298. 

15. Park, S., Ahn, K. U., Hwang, S., Choi, S., & Park, C. S. (2019). Machine learning vs. hybrid machine learning model for optimal operation of a chiller. Science and Technology for the Built Environment, 1-12. 

16. Ra, S. J., Shin, H. S., Suh, W. J., Chu, H. G., Park, C. S. (2017). Five Machine Learning Models for HVAC Systems in an Existing Office Building. Journal of the architectural institute of Korea planning & design, 33(10), 69-78. 

17. Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352-1372. 

    상세보기

18. Solomatine, D. P., & Ostfeld, A. (2008). Data-driven modelling: some past experiences and new approaches. Journal of hydroinformatics, 10(1), 3-22. 

    상세보기

19. Sun, Y., Wang, S., & Huang, G. (2010). Model-based optimal start control strategy for multi-chiller plants in commercial buildings. Building Services Engineering Research and Technology, 31(2), 113-129. 

    상세보기

20. Sahin, A. S. (2011). Performance analysis of single-stage refrigeration system with internal heat exchanger using neural network and neuro-fuzzy. Renewable energy, 36(10), 2747-2752. 

    상세보기

21. Shin, H. S., & Park, C. S. (2017). Development of a Machine Learning Model for a Chiller using Random Forest Algorithm and Data Pre-processing. Journal of the architectural institute of Korea planning & design, 33(9), 67-74. 

22. Suh, W. J., & Park, C. S. (2016). Room air Temperature Prediction Model using Genetic Programming and BEMS Data. Journal of the architectural institute of Korea planning & design, 32(6), 105-112. 

23. Sun, W., Hu, P., Lei, F., Zhu, N., & Jiang, Z. (2015). Case study of performance evaluation of ground source heat pump system based on ANN and ANFIS models. Applied Thermal Engineering, 87, 586-594. 

    상세보기

24. Vaghefi, S. A., Jafari, M. A., Zhu, J., Brouwer, J., & Lu, Y. (2016). A hybrid physics-based and data driven approach to optimal control of building cooling/heating systems. IEEE Transactions on Automation Science and Engineering, 13(2), 600-610. 

    상세보기

25. Yang, I. H., Yeo, M. S., & Kim, K. W. (2003). Application of artificial neural network to predict the optimal start time for heating system in building. Energy Conversion and Management, 44(17), 2791-2809. 

    상세보기