• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
G. D. Kontes, C. Valmaseda, G. I. Giannakis, 2014, Intelligent BEMS design using detailed thermal simulation models and surrogate-based stochastic optimization, Journal of Process Control, Vol. 24, No. 6, pp. 846-855DOI
2 
J. Yuan, C. Farnham, K. Emura, 2015, Development and application of a simple BEMS to measure energy consumption of buildings, Energy and Buildings, Vol. 109, pp. 1-11DOI
3 
S. Wang, H. Hae, J. Kim, 2018, Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR, Energies, Vol. 11, No. 2, pp. 14-DOI
4 
K. Amasyali, N. M. El-Gohary, 2018, A review of data driven building energy consumption prediction studies, Renewable & Sustainable Energy Reviews, Vol. 81, pp. 1192-1205DOI
5 
J. J. Yang, C. Ning, C. Deb, 2017, k-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement, Energy and Buildings, Vol. 146, pp. 27-37DOI
6 
J. J. Yang, K. K. Tan, M. Santamouris, 2019, Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks, Buildings, Vol. 9, No. 9DOI
7 
Y.-F. Zhang, P. J. Thorburn, W. Xiang, 2019, SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data, IEEE Internet of Things Journal, Vol. 6, No. 4, pp. 6618-6628DOI
8 
N. Jaques, S. Taylor, A. Sano, R. Picard, Oct 2017, Multimodal autoencoder: A deep learning approach to filling in missing sensor data and enabling better mood prediction, in Proc. 7th Int. Conf. Affective Comput. Intell. Interact. (ACII) San Antonio, TX, USA, pp. 202-208DOI
9 
M. Das, S. K. Ghosh, 2017, A deep-learning-based forecasting ensemble to predict missing data for remote sensing analysis, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., Vol. 10, No. 12, pp. 5228-5236DOI
10 
Yeon-Ju An, Taeck-Kie Lee, Kyu-Ho Kim, 2021, Prediction of Photovoltaic Power Generation Based on LSTM Considering Daylight and Solar Radiation Data, The Transactions of the Korean Institute of Electrical Engineers, Vol. 70, No. 8, pp. 1096-1101Google Search
11 
H. Yuan, G. Xu, Z. Yao, J. Jia, Y. Zhang, 2018, Imputation of missing data in time series for air pollutants using long short-term memory recurrent neural networks, in Proc. ACM Int. Joint Conf. Int. Symp. Pervasive Ubiquitous Comput. Wearable Comput., Vol. 2018, pp. 1293-1300DOI
12 
H. Verma, S. Kumar, 2019, An accurate missing data prediction method using LSTM based deep learning for health care, in Proc. 20th Int. Conf. Distrib. Comput. Netw., Vol. 2019, pp. 371-376DOI
13 
L. Shen, Q. Ma, S. Li, 2018, End-to-end time series imputation via residual short paths, in Proc. Asian Conf. Mach. Learn., Vol. 2018, pp. 248-263Google Search
14 
L. Li, J. Zhang, Y. Wang, B. Ran, 2018, Missing value imputation for traffic-related time series data based on a multi-view learning method, IEEE Trans. Intell. Transp. Syst to be publishedDOI
15 
Z. C. Lipton, D. C. Kale, R. Wetzel, 2016, Modeling missing data in clinical time series with RNNS, in Proc. Mach. Learn. Healthcare, Vol. 2016, pp. 1-17Google Search
16 
J. Yoon, W. R. Zame, M. van der Schaar, 2017, Estimating missing data in temporal data streams using multi-directional recurrent neural networks, IEEE Trans. Biomed. Eng. to be publishedDOI
17 
C. E. Kontokosta, C. Tull, 2017, A data-driven predictive model of city-scale energy use in buildings, Applied Energy, Vol. 197, pp. 303-317DOI
18 
C. Robinson, B. Dilkina, J. Hubbs, 2017, Machine learning approaches for estimating commercial building energy consumption, Applied Energy, Vol. 208, pp. 889-904DOI
19 
H. X. Zhao, F. Magoules, Aug 2012, A review on the prediction of building energy consumption, Renewable & Sustainable Energy Reviews, Vol. 16, No. 6, pp. 3586-3592DOI
20 
J. Runge, R. Zmeureanu, 2019, Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review, Energies, Vol. 12, No. 17, pp. 27DOI
21 
Seon Hyeog Kim, Gyul Lee, Gu-Young Kwon, Do-In Kim, 2018, Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting, energies, Vol. 11, No. 12, pp. 1-17DOI
22 
Bedi Jatin, Toshniwal Durga, 2019, Deep learning framework to forecast electricity demand, APPLIED ENERGY, Vol. 238, pp. 1312-1326DOI
23 
Elsa, Yean-Der Kuan, 2021, Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms, SUSTAINABILITY, Vol. 13, No. 2, pp. 1-18DOI
24 
J. Zhou, Z. Huang, 2018, Recover missing sensor data with iterative imputing network, in Proc. Workshops 32nd Artif. Intell. (AAAI) Conf. New Orleans LA USA, pp. 209-215Google Search
25 
C. Leke, B. Twala, T. Marwala, 2014, Missing data prediction and classification: The use of auto-associative neural networks and optimization algorithms, arXiv preprint arXiv: 1403.5488Google Search
26 
ASHRAE, 2014, ASHRAE’s guideline 14-2014 Measurement of Energy, Demand , and Water SavingsGoogle Search