Clustering based on dynamic time warping to extract typical daily patterns from long-term operation data of a ground source heat pump system
Tài liệu tham khảo
2019
Programme, 2020
Harish, 2016, A review on modeling and simulation of building energy systems, Renew Sustain Energy Rev, 56, 1272, 10.1016/j.rser.2015.12.040
Omer, 2008, Ground-source heat pumps systems and applications, Renew Sustain Energy Rev, 12, 344, 10.1016/j.rser.2006.10.003
Wei, 2018, A review of data-driven approaches for prediction and classification of building energy consumption, Renew Sustain Energy Rev, 82, 1027, 10.1016/j.rser.2017.09.108
Liu, 2017, Feasibility and performance study of the hybrid ground-source heat pump system for one office building in Chinese heating dominated areas, Renew Energy, 101, 1131, 10.1016/j.renene.2016.10.006
Park, 2018, Application of a multiple linear regression and an artificial neural network model for the heating performance analysis and hourly prediction of a large-scale ground source heat pump system, Energy Build, 165, 206, 10.1016/j.enbuild.2018.01.029
Wang, 2014, Energy modeling of ground source heat pump vs. variable refrigerant flow systems in representative US climate zones, Energy Build, 72, 222, 10.1016/j.enbuild.2013.12.017
Yan, 2016, The performance prediction of ground source heat pump system based on monitoring data and data mining technology, Energy Build, 127, 1085, 10.1016/j.enbuild.2016.06.055
Ruiz-Calvo, 2014, Reference data sets for validating GSHP system models and analyzing performance parameters based on a five-year operation period, Geothermics, 51, 417, 10.1016/j.geothermics.2014.03.010
Ruiz-Calvo, 2016, Reference data sets for validating and analyzing GSHP systems based on an eleven-year operation period, Geothermics, 64, 538, 10.1016/j.geothermics.2016.08.004
Handbook, 2009
Fumo, 2014, A review on the basics of building energy estimation, Renew Sustain Energy Rev, 31, 53, 10.1016/j.rser.2013.11.040
Khalilnejad, 2020, Data-driven evaluation of HVAC operation and savings in commercial buildings, Appl Energy, 278, 115505, 10.1016/j.apenergy.2020.115505
Gao, 2014, A new methodology for building energy performance benchmarking: an approach based on intelligent clustering algorithm, Energy Build, 84, 607, 10.1016/j.enbuild.2014.08.030
Hepburn, 2016, Field-scale monitoring of a horizontal ground source heat system, Geothermics, 61, 86, 10.1016/j.geothermics.2016.01.012
Michopoulos, 2013, Operation characteristics and experience of a ground source heat pump system with a vertical ground heat exchanger, Energy, 51, 349, 10.1016/j.energy.2012.11.042
Chicco, 2012, Overview and performance assessment of the clustering methods for electrical load pattern grouping, Energy, 42, 68, 10.1016/j.energy.2011.12.031
Tran, 2021, Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis, Measurement, 184, 109962, 10.1016/j.measurement.2021.109962
Aggarwal, 2015, Outlier analysis, 237
Kotsiantis, 2006, Data preprocessing for supervised leaning, Proc Wrld Acad Sci E, 12, 278
Hodge, 2004, A survey of outlier detection methodologies, Artif Intell Rev, 22, 85, 10.1023/B:AIRE.0000045502.10941.a9
Li, 2020, Handling incomplete sensor measurements in fault detection and diagnosis for building HVAC systems, IEEE Trans Autom Sci Eng, 17, 833, 10.1109/TASE.2019.2948101
Pazhoohesh M, Pourmirza Z, Walker S. A comparison of methods for missing data treatment in building sensor data. Conference A Comparison of Methods for Missing Data Treatment in Building Sensor Data. p. 255-259.
Ratanamahatana, 2005, 506
Miller, 2015, Automated daily pattern filtering of measured building performance data, Autom ConStruct, 49, 1, 10.1016/j.autcon.2014.09.004
Aghabozorgi, 2015, Time-series clustering – a decade review, Inf Syst, 53, 16, 10.1016/j.is.2015.04.007
Senin, 2008, Dynamic time warping algorithm review, Information and Computer Science Department University of Hawaii at Manoa Honolulu, 855, 40
Ding, 2020, A novel similarity measurement and clustering framework for time series based on convolution neural networks, IEEE Access, 8, 173158, 10.1109/ACCESS.2020.3025048
Miller, 2018, A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings, Renew Sustain Energy Rev, 81, 1365, 10.1016/j.rser.2017.05.124
Yu, 2019, A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption, Appl Energy, 253, 113497, 10.1016/j.apenergy.2019.113497
Ruiz, 2020, A time-series clustering methodology for knowledge extraction in energy consumption data, Expert Syst Appl, 160, 113731, 10.1016/j.eswa.2020.113731
Liu, 2020, A moving shape-based robust fuzzy K-modes clustering algorithm for electricity profiles, Elec Power Syst Res, 187, 106425, 10.1016/j.epsr.2020.106425
Zhao, 2020, A review of data mining technologies in building energy systems: load prediction, pattern identification, fault detection and diagnosis, Energy and Built Environment, 1, 149, 10.1016/j.enbenv.2019.11.003
Hwang, 2009, Cooling performance of a vertical ground-coupled heat pump system installed in a school building, Renew Energy, 34, 578, 10.1016/j.renene.2008.05.042
Safa, 2015, Heating and cooling performance characterisation of ground source heat pump system by testing and TRNSYS simulation, Renew Energy, 83, 565, 10.1016/j.renene.2015.05.008
Williams, 2014, Survey sampling and weighting, 371
Zhou, 2016, Operation analysis and performance prediction for a GSHP system compounded with domestic hot water (DHW) system, Energy Build, 119, 153, 10.1016/j.enbuild.2016.03.024
Do, 2016, Development and validation of a custom-built ground heat exchanger model for a case study building, Energy Build, 119, 242, 10.1016/j.enbuild.2016.03.049
Zhu, 2015, Performance analysis of ground water-source heat pump system with improved control strategies for building retrofit, Renew Energy, 80, 324, 10.1016/j.renene.2015.02.021
Yu, 2016, Advances and challenges in building engineering and data mining applications for energy-efficient communities, Sustain Cities Soc, 25, 33, 10.1016/j.scs.2015.12.001
Chen, 2010, Automated load curve data cleansing in power systems, IEEE Trans Smart Grid, 1, 213, 10.1109/TSG.2010.2053052
Berndt DJ, Clifford J. Using dynamic time warping to find patterns in time series. Conference Using dynamic time warping to find patterns in time series, vol. vol. 10. Seattle, WA, p. 359-370.
Kate, 2015, Using dynamic time warping distances as features for improved time series classification, Data Min Knowl Discov, 30, 283, 10.1007/s10618-015-0418-x
Wang, 2019, Clustering of interval-valued time series of unequal length based on improved dynamic time warping, Expert Syst Appl, 125, 293, 10.1016/j.eswa.2019.01.005
Yang, 2020, Research on clustering method based on weighted distance density and K-means, Procedia Comput Sci, 166, 507, 10.1016/j.procs.2020.02.056
Rousseeuw, 1987, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J Comput Appl Math, 20, 53, 10.1016/0377-0427(87)90125-7
Hong, 2020, SSDTW: shape segment dynamic time warping, Expert Syst Appl, 150, 113291, 10.1016/j.eswa.2020.113291
Li, 2020, Adaptively constrained dynamic time warping for time series classification and clustering, Inf Sci, 534, 97, 10.1016/j.ins.2020.04.009
Łuczak, 2016, Hierarchical clustering of time series data with parametric derivative dynamic time warping, Expert Syst Appl, 62, 116, 10.1016/j.eswa.2016.06.012
Zhao, 2018, Shape dynamic time warping, Pattern Recogn, 74, 171, 10.1016/j.patcog.2017.09.020
