Domestic Hot Water Load Prediction in Residential Communities using the Structured Probabilistic Statistical Models |
Kim, Chulho
(Department of Civil, Environmental and Architectural Engineering, Korea University)
Byun, Jiwook (Department of Civil, Environmental and Architectural Engineering, Korea University) Go, Jaehyun (Department of Civil, Environmental and Architectural Engineering, Korea University) Heo, Yeonsook (Department of Civil, Environmental and Architectural Engineering, Korea University) |
1 | Lee, J. Y. & Yim, T. S. (2021). A Study on the Domestic Hot Water demand in Apartment Households. 2021 SAREK summer conference, 329. |
2 | Korea Energy Agency (KEA), 2018 data, Public Data Portal, Retrieved August 11, 2021 from https://www.data.go.kr/tcs/dss/selectDataSetList.do |
3 | Ahmad, T., Zhang, H., & Yan, B. (2020). A review on renewable energy and electricity requirement forecasting models for smart grid and buildings. Sustainable Cities and Society, 55, 102052. DOI |
4 | Building Research Institute(BRE), UK's National Calculation Method for Non-Domestic Building, The National Calculation Methodology (NCM), Retrieved December 7, 2021 from https://www.uk-ncm.org.uk/ |
5 | Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017). A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74, 902-924. DOI |
6 | De Santiago, J., Rodriguez-Villalon, O., & Sicre, B. (2017). The generation of domestic hot water load profiles in Swiss residential buildings through statistical predictions. Energy and Buildings, 141, 341-348. DOI |
7 | De Wilde, P. (2014). The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in construction, 41, 40-49. DOI |
8 | Ferrantelli, A., Ahmed, K., Pylsy, P., & Kurnitski, J. (2017). Analytical modelling and prediction formulas for domestic hot water consumption in residential Finnish apartments. Energy and Buildings, 143, 53-60. DOI |
9 | Lee, S. J., Jin, H. S., Kim, S. I., Lim, S. H., Lim, J. H., & Song, S. Y. (2018). A measurement and an analysis of heating and DHW energy consumption in apartment buildings with individual heating systems. Journal of the Architectural Institute of Korea, Planning and Design Section, 34(6), 15-22. |
10 | Menezes, A. C., Cripps, A., Bouchlaghem, D., & Buswell, R. (2012). Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap. Applied energy, 97, 355-364. DOI |
11 | Ministry of Land, Infrastructure and Transport(MOLIT) & Korea Institute of Civil Engineering and Building Technology(KICT), (2014), A Study for Standardization of Quantitative Evaluation Methods for Building Energy Performance. |
12 | Korea Energy Economics Institute(KEEI) & Korea Energy Agency(KEA), (2015) 2014 Energy Consumption Survey, Ministry of Trade, Industry and Energy(MOTIE). |
13 | Neu, O., Oxizidis, S., Flynn, D., & Finn, D. (2016). Utilising time of use surveys to predict domestic hot water consumption and heat demand profiles of residential building stocks. British Journal of Environment and Climate Change, 6(2), 77-89. DOI |
14 | Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting-A review. Renewable and sustainable energy reviews, 16(2), 1223-1240. DOI |
15 | Sun, Y. (2014). Closing the Building Energy Performance Gap by Improving Our Predictions, Ph. D. Dissertation, Georgia Institute of Technology. |
16 | Kim, C. H, Byun, J. W., Go, J. H., & Heo, Y. S. (2021). Development of the series of probabilistic statistical models for electricity demand prediction in residential communities. Journal of the Architectural Institute of Korea, 37(7), 157-165. DOI |
17 | Yuan, X., Chen, C., Jiang, M., & Yuan, Y. (2019). Prediction interval of wind power using parameter optimized Beta distribution based LSTM model. Applied Soft Computing, 82, 105550. DOI |
18 | Fischer, D., Wolf, T., Scherer, J., & Wille-Haussmann, B. (2016). A stochastic bottom-up model for space heating and domestic hot water load profiles for German households. Energy and Buildings, 124, 120-128. DOI |
19 | IEA-EBC Annex 66, Final Report, Definition and Simulation of Occupant Behavior in Buildings. |
20 | Korea Policy Briefing. (2020), Korean-version New Deal, Green New Deal, Retrieved March 21, 2022 from https://www.knewdeal.go.kr/front/view/newDeal02.do. |
21 | Kim, S. M., Chung, K. S., & Kim, Y. I. (2012). An empirical study of hot water supply patterns and peak time in apartment housing with district heating system. Journal of Energy Engineering, 21(4), 435-443. DOI |
22 | Liu T., Wang B., & Yang, C. (2018). Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning. Energy, 160, 544-555. DOI |
23 | Oh, B. K. (2019). A study on the usage and pattern of water supply and hot water in apartment houses. 2019 SAREK summer conference, 535-538. |
24 | U.S Department of Energy(DOE), ANSI/ASHRAE/IES Standard 90.1 Prototype Building Model Package, Retrieved December 7, 2021 from https://www.energycodes.gov/development/commercial/prototype_models |
25 | Korea Energy Economics Institute(KEEI) & Korea Energy Agency(KEA), (2018) 2017 Energy Consumption Survey, Ministry of Trade, Industry and Energy(MOTIE). |
26 | Kim, J. H., Kim, I. K., Lee, W. J., & Shin, U. C. (2021). Analysis of annual domestic hot water energy usage on apartment for district heating apartment in Seoul. 2021 The Korean Society for New and Renewable Energy summer conference, 147. |
27 | Yoo, S. Y., Kim, T. H., Han, K. H., Yoon, H. I., Kang, H. C., & Kim K. H. (2012). Prediction of heating load for optimum heat supply in apartment building. Transactions of the Korean Society of Mechanical Engineers-B, 36(8), 803-809. DOI |
28 | Chen, J., Gao, X., Hu, Y., Zeng, Z., & Liu, Y. (2019). A meta-model-based optimization approach for fast and reliable calibration of building energy models. Energy, 188, 116046. DOI |