參考文獻 |
[1] 行政院環境保護署,“109年度的二氧化碳排放統計.” https://www.epa.gov.tw/Page/81825C40725F211C/6a1ad12a-4903-4b78-b246-8709e7f00c2b, 2021.
[2] 經濟部能源局, “氣溫對我國電力消費的影響,” 經濟部能源局, 2017.
[3] 陳易, “基於機器學習之冰機設備運轉參數優化系統,” Master’s thesis, 國立中央大學, 桃園市, 2021. (王文俊指導)
[4] B. Sun, P. B. Luh, Q.-S. Jia, Z. O’Neill, and F. Song, “Building energy doctors: An SPC and kalman filter-based method for system-level fault detection in HVAC systems,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 1, pp. 215–229, 2013.
[5] M. Bourdeau, X. qiang Zhai, E. Nefzaoui, X. Guo, and P. Chatellier, “Modeling and forecasting building energy consumption: A review of data-driven techniques,” Sustainable Cities and Society, vol. 48, p. 101533, 2019.
[6] A. Ghanbari, S. Abbasian-Naghneh, and E. Hadavandi, “An intelligent load forecasting expert system by integration of ant colony optimization, genetic algorithms and fuzzy logic,” in 2011 IEEE symposium on computational intelligence and data mining (CIDM), 2011, pp. 246–251.
[7] M. Bonvini, M. D. Sohn, J. Granderson, M. Wetter, and M. A. Piette, “Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques,” Applied Energy, vol. 124, pp. 156–166, 2014.
[8] M. Elnour and N. Meskin, “Novel actuator fault diagnosis framework for multizone HVAC systems using 2-d convolutional neural networks,” IEEE Transactions on Automation Science and Engineering, 2021.
[9] F. Sidi, P. H. S. Panahy, L. S. Affendey, M. A. Jabar, H. Ibrahim, and A. Mustapha, “Data quality: A survey of data quality dimensions,” in 2012 international conference on information retrieval & knowledge management, 2012, pp. 300–304.
[10] V. Tra, M. Amayri, and N. Bouguila, “Outlier detection via multiclass deep autoencoding gaussian mixture model for building chiller diagnosis,” Energy and Buildings, p. 111893, 2022.
[11] Z. Wang, L. Wang, Y. Tan, and J. Yuan, “Fault detection based on bayesian network and missing data imputation for building energy systems,” Applied Thermal Engineering, vol. 182, p. 116051, 2021.
[12] B. Li, F. Cheng, X. Zhang, C. Cui, and W. Cai, “A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data,” Applied Energy, vol. 285, p. 116459, 2021.
[13] Z. Ferdousi and A. Maeda, “Unsupervised outlier detection in time series data,” in 22nd international conference on data engineering workshops (ICDEW’06), 2006, pp. x121–x121.
[14] J. Jabez and B. Muthukumar, “Intrusion detection system (IDS): Anomaly detection using outlier detection approach,” Procedia Computer Science, vol. 48, pp. 338–346, 2015.
[15] M. Goldstein and A. Dengel, “Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm,” KI-2012: poster and demo track, vol. 9, 2012.
[16] T. Pevnỳ, “Loda: Lightweight on-line detector of anomalies,” Machine Learning, vol. 102, no. 2, pp. 275–304, 2016.
[17] Z. Li, Y. Zhao, N. Botta, C. Ionescu, and X. Hu, “COPOD: Copula-based outlier detection,” in 2020 IEEE international conference on data mining (ICDM), 2020, pp. 1118–1123.
[18] F. T. Liu, K. M. Ting, and Z.-H. Zhou, “Isolation-based anomaly detection,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 6, no. 1, pp. 1–39, 2012.
[19] H.-P. Kriegel, M. Schubert, and A. Zimek, “Angle-based outlier detection in high-dimensional data,” in Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, 2008, pp. 444–452.
[20] K. Krishna and M. N. Murty, “Genetic k-means algorithm,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 29, no. 3, pp. 433–439, 1999.
[21] D. A. Reynolds, “Gaussian mixture models.” Encyclopedia of biometrics, vol. 741, no. 659–663, 2009.
[22] U. Von Luxburg, “A tutorial on spectral clustering,” Statistics and computing, vol. 17, no. 4, pp. 395–416, 2007.
[23] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “Density-based spatial clustering of applications with noise,” in Int. Conf. Knowledge discovery and data mining, 1996, vol. 240, p. 6.
[24] M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander, “OPTICS: Ordering points to identify the clustering structure,” ACM Sigmod record, vol. 28, no. 2, pp. 49–60, 1999.
[25] L. McInnes, J. Healy, and S. Astels, “Hdbscan: Hierarchical density based clustering.” J. Open Source Softw., vol. 2, no. 11, p. 205, 2017.
[26] D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 5, pp. 603–619, 2002.
[27] K. P. Murphy, Machine learning: A probabilistic perspective. MIT press, 2012.
[28] R. W. Schafer, “What is a savitzky-golay filter?[lecture notes],” IEEE Signal processing magazine, vol. 28, no. 4, pp. 111–117, 2011.
[29] Z. He, X. Xu, and S. Deng, “Discovering cluster-based local outliers,” Pattern recognition letters, vol. 24, no. 9–10, pp. 1641–1650, 2003.
[30] D. Dua and C. Graff, “UCI machine learning repository.” University of California, Irvine, School of Information; Computer Sciences, 2017.
[31] G. H. Dunteman, Principal components analysis. Sage, 1989.
[32] Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.
[33] F. Angiulli and C. Pizzuti, “Fast outlier detection in high dimensional spaces,” in European conference on principles of data mining and knowledge discovery, 2002, pp. 15–27.
[34] M.-L. Shyu, S.-C. Chen, K. Sarinnapakorn, and L. Chang, “A novel anomaly detection scheme based on principal component classifier,” Miami Univ Coral Gables Fl Dept of Electrical; Computer Engineering, 2003. |