KJACR
Korean Journal of
Air-Conditioning and Refrigeration Engineering
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ISSN : 1229-6422 (Print)
ISSN : 2465-7611 (Online)
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Korean Journal of Air-Conditioning and Refrigeration Engineering
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Korean J. Air-Cond. Refrig. Eng.
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ISSN : 1229-6422 (Print)
ISSN : 2465-7611 (Online)
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2019-01
(Vol.31 No.01)
10.6110/KJACR.2019.31.1.022
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References
1
He W., 2017, Load forecasting via deep neural networks, Procedia Computer Science, Vol. 122, pp. 308-314
2
Weron R., 2014, Electricity price forecasting : A review of the state-of-the-art with a look into the future, International Journal of Forecasting, Vol. 30, No. 4, pp. 1030-1081
3
Martinez-Anido C. B., Brinkman G., Hodge B. M., 2016, The impact of wind power on electricity prices, Renewable Energy, Vol. 94, pp. 474-487
4
Baldick R., 2012, Wind and energy markets : A case study of Texas, IEEE Systems Journal, Vol. 6, No. 1, pp. 27-34
5
Pipattanasomporn M., Kuzlu M., Rahman S., 2012, An algorithm for intelligent home energy management and demand response analysis, IEEE Transactions on Smart Grid, Vol. 3, No. 4, pp. 2166-2173
6
Hyun S. H., Park C. S., Augenbroe G. L. M., 2008, Analysis of uncertainty in natural ventilation predictions of high-rise apartment buildings, Building Services Engineering Research and Technology, Vol. 29, No. 4, pp. 311-326
7
Wouters P., Heijmans N., Loncour X., 2004, Outline for a general framework for the assessment of innovative ventilation systems, RESHYVENT working report nWP4 D, Vol. 4
8
Lago J., De Ridder F., De Schutter B., 2018, Forecasting spot electricity prices : Deep learning approaches and empirical comparison of traditional algorithms, Applied Energy, Vol. 221, pp. 386-405
9
Roldn-Blay C., Escriv-Escriv G., lvarez-Bel C., Roldn-Porta C., Rodrguez-Garca J., 2013, Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model, Energy and Buildings, Vol. 60, pp. 38-46
10
Jetcheva J. G., Majidpour M., Chen W. P., 2014, Neural network model ensembles for building-level electricity load forecasts, Energy and Buildings, Vol. 84, pp. 214-223
11
Hippert H. S., Pedreira C. E., Souza R. C., 2001, Neural networks for short-term load forecasting : A review and evaluation, IEEE Transactions on power systems, Vol. 16, No. 1, pp. 44-55
12
Kim M., Hong C., 2016, The Artificial Neural Network based Electric Power Demand Forecast using a Season and Weather Informations, Journal of the Institute of Electronics and Information Engineers, Vol. 53, No. 1, pp. 71-78
13
Din G. M. U., Marnerides A. K., 2017, Short term power load forecasting using deep neural networks, In Computing, Networking and Communications (ICNC), 2017 International Conference on IEEE, pp. 594-598
14
Marino D. L., Amarasinghe K., Manic M., 2016, Building energy load forecasting using deep neural networks, In Industrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE, pp. 7046-7051
15
Hosein S., Hosein P., 2017, Load forecasting using deep neural networks, In Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2017 IEEE, pp. 1-5
16
Asare-Bediako B., 2014, SMART Energy Homes and the Smart Grid, University of Technology, Eindhoven
17
Kim J. H., Park S. L., Kim D. W., 2011, Agent-based Building Energy Simulation, Architectural Institute of Korea, Vol. 27, No. 12, pp. 315-324
18
Fujii H., Tanimoto J., 2004, Integration of building simulation and agent simulation for exploration to environmentally symbiotic architecture, Building and Environment, Vol. 39, No. 8, pp. 885-893
19
Kashif A., Le X. H. B., Dugdale J., Ploix S., 2011, Agent based Framework to Simulate Inhabitants’ Behaviour in Domestic Settings for Energy Management, In ICAART, Vol. 2, pp. 190-199
20
Bonabeau E., 2002, Agent-based modeling: Methods and techniques for simulating human systems, Proceedings of the National Academy of Sciences, Vol. 99, pp. 7280-7287
21
Lee L. J., 2017, A study on fundamental and application of CNN and RNN, Broadcasting and Media Magazine, Vol. 22, No. 1, pp. 87-95
22
Saito G., 년도, Deep learning from scratch, Hanbit Media Inc.
23
Baccouche M., Mamalet F., Wolf C., Garcia C., Baskurt A., 2011, Sequential deep learning for human action recognition, In International Workshop on Human Behavior Understanding, Springer, Berlin, Heidelberg, pp. 29-39
24
Kinga D., Adam J. B., 2015, A method for stochastic optimization, In International Conference on Learning Representations (ICLR), Vol. 5
25
Kong D. S., Kwak Y. H., Huh J. H., 2010, Artificial Neural Network Based Energy Demand Prediction for the Urban District Energy Planning, Journal of the Architectural Institute of Korea, Vol. 26, No. 2, pp. 221-230
26
Documentation M., 2005, The MathWorks Inc.
27
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