Acknowledgement
Supported by : 국토교통부
References
- Jang, J. H., & Leigh, S. B. (2017). A Prediction of Optimal Heating Timing based on Artificial Neural Network by utilizing BEMS data, Spring Conference of AIK, Vol. 37, No. 2, pp. 563-564
- Jeong, J. H., & Chae, Y. T. (2017). A Study on selection of Machine Learning types for Building Energy Consumption Forecasting, 2017 Summer Annual Conference, pp. 93-94
- Jeong, J. H,, & Chae, Y. T. (2017). Assessment of Input Variable Importance and Machine Learning Model Selection for Improving Short Term Load Forecasting on Different Building Types, Journal of KIAEBS, Vol. 11, No. 6, pp. 586-598
- Seong, N. C., Kim. J. H., Choi, W. C., Yoon, S. C., & Nabil, N. (2017). Development of Optimization algorithms for Building Energy model Using Artificial Neural Networks, KSLES, Vol. 24, No. 1, pp. 29-36 https://doi.org/10.21086/ksles.2017.02.24.1.29
- Seong, N. C., & Choi, W. C. (2017). Development of Predictive Fan Model using the Artificial Neural Network, Autumn Annual Conference of AIK, pp. 604-607
- ASHRAE. (2002). Measurement of Energy and Demand Savings, ASHRAE Guideline 14, pp. 10-21
- ASHRAE. (1993). Algorithms and Subroutines for Secondary HVAC System Energy Calculations, HVAC 2 Toolkit, pp. 1-34
- Nassif, N. (2014). Modeling and Optimization of HVAC systems using Artificial Neural Network and Genetic Algorithm, International Journal of Building Simulation, Vol. 7, No. 3, pp. 237-245 https://doi.org/10.1007/s12273-013-0138-3
- Park, B. R., Choi, E. J., & Moon, J. W. (2017). Performance tests on the ANN model prediction accuracy for cooling load of buildings during the setback period, KIEAE Journal, Vol. 17, No. 4, pp. 83-88 https://doi.org/10.12813/kieae.2017.17.4.083
- Jeon, B. K., & Kim, E. J. (2017). Short-Term Load Prediction Using Artificial Neural Network Models, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 29, No. 10, pp. 97-503
- Ahmed, M. W., Mourshed, M., & Rezgui, Y. (2017). Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption, Energy and Buildings, Vol. 147, No. 15, pp. 77-89 https://doi.org/10.1016/j.enbuild.2017.04.038