xiufengliu.github.io

Dr. Xiufeng Liu

Senior Researcher

Department of Technology, Management and Economics

Technical University of Denmark

Address: DTU, Produktionstorvet, Building 424, room 006, 2800 Kgs. Lyngby, Denmark

Email: xiuli@dtu.dk

Google Scholar / ResearchGate / ORCID /DTU Orbit / GitHub / LinkedIn

Short Bio

I am a Senior researcher (Associate Professor level) at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where I am a member of the research group of Energy Economics and Modelling. I completed my PhD at the Department of Computer Science (Daisy research group) at Aalborg University (AAU) in Denmark from 2008 to 2012. Before joining AAU, I worked as an Scientific Assistant at Åbo Akademi University in Finland from 2006 to 2008. From 2013 to 2014, I was a Postdoctoral Researcher in the Data System Group at the University of Waterloo in Canada, and was also cross-appointed as a Research Scientist by IBM Toronto Research Center, where I worked on the soscip project.

Research Interests

Publications

Peer-reviewed papers (*: Corresponding author; [c]: Conference; [j]: Journal; [b]: Book chapter):

2023:

  1. [j] Z. Chen, J. Li, L. Cheng, X. Liu. Federated-WDCGAN: A Federated Smart Meter Data Sharing Framework for Privacy Preservation, Applied Energy, Volume 334, 120711, 2023. [PDF]
  2. [c] X. Liu, X. Cheng, Y. Yang, H. Huo, Y. Liu, P.S. Nielsen. Understanding crowd energy consumption behaviors (demo), Accepted by EDBT, 2023 [PDF]
  3. [j] S. Wang, J. Gao, F. Lu, F. Wang, Z. You, M. Huang, X. Liu*, Y. Li, Y. Liu. Human motion recognition by a shoes-floor triboelectric nanogenerator and its application in fall detection. Nano Energy, 108230, 2023. [PDF]
  4. [j] P. Ezhilarasi, L. Ramesh, X. Liu, J.B. Holm-Nielsen. Smart Meter Synthetic Data Generator development in python using FBProphet. Software Impacts, 100468, 2023. [PDF]

    2022:

  5. [j] J. Wu, Z. Niu, P.S. Nielsen, L. Huang, X. Liu*. Understanding Multi-scale Spatiotemporal Energy Consumption Data: A Visual Analysis Approach, Energy, Vol. 263, 125939, 2022. [PDF]
  6. [c] Z. Zhang, Z. Niu, P.S. Nielsen, X. Liu*. A deep learning approach for spatiotemporal energy demand prediction, Proc. of the 17th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), 2022. [PDF]
  7. [c] X. Cheng, M. Zhao, J. Zhang, J. Wang, X. Pan, X. Liu*. TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring. The 4th International Conference on High Performance Big Data and Intelligent Systems, Best paper nomination, pp. 13-20, 2022. [PDF]
  8. [c] D. Zhang, W. Tian, Y. Yin, X. Liu, F. Shi. Human Knowledge-based Compressed Federated Learning Model for Wind Turbine Blade Icing Detection. The 4th International Conference on High Performance Big Data and Intelligent Systems, pp. 277-281, 2022. [PDF]
  9. [j]. K. Wan, X. Liu, Y. Liu, M. Nowostawski, H. Holtskog, L. Huang, Automated infection risks assessments (AIRa) for decision-making using a blockchain-based alert system : a case study in a representative building. Environmental Research, vol. 216, 114663, 2023. [PDF]
  10. [j] J. Li, Z. Chen, L. Cheng, X. Liu. Energy Data Generation with Wasserstein Deep Convolutional Generative Adversarial Networks. Energy, Vol. 257, 124694, 2022. [PDF]
  11. [j] J. Peng, A. Kimmig, D. Wang, Z. Fan, J. Wang, X. Liu, J. Ovtcharova. A systematic review of data-driven approaches to fault diagnosis and early warning. Journal of Intelligent Manufacturing, 2022, doi: 10.1007/s10845-022-02020-0. [PDF]
  12. [c] X. Cheng, D. Li, X. Liu. A review of federated learning in Energy Systems. IEEE IAS Industrial and Commercial Power System Asia(I&CPS Asia), pp. 2089-2095, 2022.
  13. [j] X. Cheng, F. Shi, Y. Liu, X. Liu* and L. Huang*. Wind Turbine Icing Detection: A Federated Learning Approach. Energy, vol. 254, Part C, 2022. [PDF]
  14. [j] X. Cheng, F. Shi, Y. Liu, J. Zhou, X. Liu* and L. Huang*. A Class-Imbalanced Heterogeneous Federated Learning Model for Detecting Icing on Wind Turbine Blades. IEEE Transactions on Industrial Informatics, 18(12):8487-8497, 2022. [PDF]
  15. [j] X. Cheng, F. Shi, X. Liu, M. Zhao, S. Chen. A Novel Deep Class-imbalanced Semi-supervised Model for Wind Turbine Blade Icing Detection. IEEE Transactions on Neural Networks and Learning Systems. 33(6), 2022. [PDF]
  16. [j] Z. Lai, X. Cheng, X. Liu, P. Liu, L. Huang. A Novel Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection of Wind Turbine, IEEE Sensors Journal, 2022, doi: 10.1109/JSEN.2022.3211079. [PDF]

    2021:

  17. [c] M. Biemann, Y. Zeng, X. Liu*, and L. Huang. Addressing partial observability in reinforcement learning for energy management. In Proc. of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 324-328, 2021 [PDF].
  18. [c] W. Dai, X. Liu*, A. Heller, and P.S. Sieverts. Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention. Proc. of the 4th International Conference on Intelligent Technologies and Applications, 2021. [PDF]
  19. [c] X. Cheng, X. Liu, Y. Liu, E. Onstein, L. Huang. A Novel Deep Neural Network for Non-intrusive Load Monitoring. The 16th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), 2021.
  20. [c] X. Cheng, X. Liu*, Z. Niu, L. Huang, P. S. Nielsen. Imbalanced Federated Learning Model for Blade Icing Detection of Wind Turbines. The 16th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), 2021.
  21. [j] B. Khan, X. Liu, SA. Ali, and M. Alam. Bivariate, Cluster and Suitability Analysis of NoSQL Solutions for Different Application Areas. Advances in Computers, Elsevier, 2021. [PDF]
  22. [j] J. Peng, A. Kimmig, Z. Niu, J. Wang, X. Liu, D. Wanga, J. Ovtcharova. Wind turbine failure prediction and health assessment based on adaptive maximum mean discrepancy. International Journal of Electrical Power and Energy Systems. Vol. 134, 107391, 2022. [PDF]
  23. [j] J. Peng, A. Kimmig, Z. Niu, J. Wang, X. Liu, J. Ovtcharova. A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework. Applied Energy, vol. 299: 117321, 2021. [PDF]
  24. [j] J. Peng, A. Kimmig, J. Wang, X. Liu*, Z. Niu, J. Ovtcharova. Dual-stage Attention-based Long Short-Term Memory Neural Network for Energy Demand Prediction. Energy & Building, 111211, 2021. [PDF]
  25. [c] W. Qu, X. Liu*, S. Dessloch. A workload-aware change data capture framework for data warehousing. Proc. of the 23rd International Conference on Big Data Analytics and Knowledge Discovery (DaWaK), pp. 222–231, 2021. [PDF]
  26. [j] M. Biemann, F. Scheller, X. Liu*, L. Huang. Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control. Applied Energy, 298:117164, 2021 [PDF].
  27. [j] Y. Wang, I. L. Bennani, X. Liu, M. Sun, Y. Zhou. Electricity Consumer Characteristics Identification: A Federated Learning Approach. IEEE Transaction on Smart Grid, 12(4):3637 - 3647, 2021 [PDF].
  28. [j] Z. Niu, J. Wu, X. Liu*, L. Huang, P. S. Understanding Energy Demand Behaviors through Spatio-temporal Smart Meter Data Analysis. Energy, V226:120493, 2021 [PDF].

    2020:

  29. [j] C.W. Bülow and X. Liu*. CAT: A Ready-to-use Tool for Studying Climate Change Disaster Adaptation Behavior, Journal of behavioral and experimental economics, 101590, 2020 [PDF].
  30. [c] J. Wu, Z. Niu, J. Wu, X. Liu, and J. Zhang. $E^3$: Visual Exploration of Spatiotemporal Energy Demand. arXiv preprint arXiv:2006.09487, 2020 [PDF].
  31. [J] Y. Zhou, Y. Yang, H. Liu, X. Liu, N. Savage. Deep Learning Based Fusion Approach for Hate Speech Detection. IEEE Access, 2020 [PDF].
  32. [c] X. Liu, Z. Lai, L. Huang, X. Wang, and PS Nielsen. A Contextual Anomaly Detection Framework for Energy Smart Meter Data Stream. Liu, X., Lai, Z., Wang, X., Huang, L., & Nielsen, P. S. (2020, November). A Contextual Anomaly Detection Framework for Energy Smart Meter Data Stream. Proc. of International Conference on Neural Information Processing (pp. 733-742). 2020. [PDF]
  33. [c] X. Wang, X. Liu, X. Zhong and P. Cheng. View Selection for Graph Pattern Matching, Proc. of DEXA, pp. 93-110, 2020. [PDF]
  34. [c] X. Liu, Z. Niu, Y. Yang, J. Wu, D. Cheng, and X. Wang. VAP: A Visual Analysis Tool for Energy Consumption Spatio-temporal Pattern Discovery (Demo). Proc. of EDBT, pp. 579-582, 2020. [PDF] [Video]

    2019:

  35. [j] X. Liu, Y. Yang, R. Li, and P. S. Nielsen. A stochastic model for residential user activity simulation. Energies, 12(17):3326, 2019. [PDF]
  36. [j] S. Khan, M. Alam, X. Liu, and K.A. Shakil. Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems. International Journal of Advanced Computer Science and Applications, 10(6):292-304, 2019. [PDF]
  37. [j] X. Liu, H. Huo, N. Iftikhar, and P.S. Nielsen. A Two-Tiered Segmentation Approach for Transaction Data Warehousing. Emerging Perspectives in Big Data Warehousing, pp. 1-27, IGI Global, 2019. [PDF]
  38. [c] X. Liu, R. Li and P. S. Nielsen. SEGSys: A mapping system for segmentation analysis in energy, Proc. of the 14th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), 2019.
  39. [c] X. Liu, S. Bolwig, and P.S. Nielsen. SmartM: A Non-intrusive Load Monitoring Platform, Proc. of the 22nd Business Information System (Workshop), pp. 424-434, 2019. [PDF]
  40. [j] X. Liu, N. Iftikhar, H. Huo, R. Li, P.S. Nielsen. Two Approaches for Synthesising Scalable Residential Energy Consumption Data. Future Computer Systems, vol. 95, pp. 586-600, 2019. [PDF]

    2018 & before:

  41. [c] A. Kewo, P. Manembu, X. Liu, P.S. Nielsen. Statistical Analysis for Factors Influencing Electricity Consumption at Regional Level. Proc. of IEEE 7th International Conference on Power and Energy (PECon), pp. 132-137, 2018. [PDF]
  42. [c] P. Manembu, A. Kewo, X. Liu, P.S. Nielsen. Multi-grained Household Load Profile Analysis using Smart Meter Data: The Case of Indonesia. Proc. of IEEE 2nd Borneo International Conference on Applied Mathematics and Engineering (BICAME), pp. 213-217, 2018. [PDF]
  43. [c] P. Manembu, A. Kewo, P.S. Nielsen, X. Liu, B. Welang, and A. Lapu. Architecture Design of Smart Meter Controlling System for Dynamic IP Environments. Proc. of International Conference on Intelligent Autonomous Systems (ICoIAS), pp. 175-179, 2018. [PDF]
  44. [j] X. Liu, P. S. Nielsen. Scalable prediction-based online anomaly detection for smart meter data. Information Systems, vol. 77, pp. 34-47, 2018. [PDF]
  45. [c] L. Liu, H. Huo, X. Liu, V. Palade. Recognizing Textual Entailment with Attentive Reading and Writing Operations. Proc. of DASFAA, pp. 847-860, 2018. [PDF]
  46. [j] P. Gianniou, X. Liu*, A. Heller, P. S. Nielsen, and C. Rode. Clustering-based Analysis for Residential District Heating Data. Energy Conversion & Management, vol. 165, pp. 840-850, 2018. [PDF]
  47. [c] D. Ahlers, F. Kraemer, A. E. Bråten, X. Liu, F. Anthonisen, P. Driscoll and J. Krogstie. Analysis and Visualization of Urban Emission Measurements in Smart Cities (demo). Proc. of EDBT, pp. 698-701, 2018. [PDF]
  48. [c] A. Kewo, P. Manembu, P. S. Nielsen, and X. Liu. Modelling of electricity consumption in one of Asia’s most populous cities - Jakarta, Indonesia. Proc. of the 12th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), 2017. [PDF]
  49. [c] H. Huo, X. Liu, D. Zheng, Z. Wu, S. Yu, L. Liu. Collaborative Filtering Fusing Label Features Based on SDAE. Proc. of ICDM, pp. 223-236, 2017. [PDF]
  50. [j] A. Heller, X. Liu, and P. Gianniou. Science Cloud for Smart Cities Research. Energy Procedia, vol. 122, pp. 679-684. [PDF]
  51. [c] X. Liu, P. S. Nielsen, A. Heller, and P. Gianniou. SciCloud: A Scientific Cloud and Management Platform for Smart City Data. Proc. of DEXA Workshop, pp. 27-31, 2017. [PDF] [Slides]
  52. [c] X. Liu, P. S. Nielsen, and A. Heller. An ICT-based Anomaly Detection Method for Smart Meter Data. Proc. of the 12th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), 2017. [PDF]
  53. [c] N. Iftikhar, X. Liu, F. E. Nordbjerg, S. Danalachi, and J. H. Vollesen. A Scalable Smart Meter Data Generator Using Spark. Proc. of the 25th international Conference on Cooperative Information Systems, pp. 21-36, 2017.[PDF] [Slides]
  54. [c] X. Liu, and P. S. Nielsen. Air Quality Monitoring System and Benchmarking. Proc. of the 19th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK), pp. 459-470, 2017. [PDF] [Slides]
  55. [j] X. Liu, P. S. Nielsen, and A. Heller. CITIESData: A Framework for Research Data Management for Smart CITIES. Knowledge and Information Systems (KAIS), 53(3):699-722, 2017. [PDF]
  56. [c] P. Manembu, B. Welang, A. Lapu, A. Kewo, P. S. Nielsen, and X. Liu. A Novel Smart Meter Controlling System with Dynamic IP Addresses. Proc. of the 26th IEEE International Symposium on Industrial Electronics (ISIE 2017). [PDF]
  57. [j] S. Khan, X. Liu, K. A. Shkil, and M. Alam. A Survey of Scholarly Data: From Big Data Perspective. Information Processing and Management (IPM), 53(4):923-944, 2017. [PDF]
  58. [c] X. Liu, N. Iftikhar, P. S. Nielsen, and A. Heller. Online Anomaly Energy Consumption Detection Using Lambda Architecture. Proc. of the 18th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK), pp. 193-209, 2016. [PDF] [Slides]
  59. [c] N. Iftikhar, X. Liu, F. E. Nordbjerg, and S. Danalachi. A Prediction-based Smart Meter Data Generator. Proc. of the 19th International Conference of Network-based Information Systems (NBIS), 2016. [PDF] [Slides]
  60. [j] X. Liu, L. Golab, W. Golab, I. F. Ilyas, and S. Jin. Smart Meter Data Analytics: Systems, Algorithms and Benchmarking. ACM Transaction on Database Systems (TODS), 42(1), 2016. [PDF]
  61. [j] X. Liu, P. S. Nielsen. An ICT-Solution for Smart Meter Data Analytics. Journal of Energy, 115(3):1710-1722, 2016. [PDF]
  62. [j] X. Liu, N. Iftikhar, and P. S. Nielsen. Optimizing ETL by a Two-level Data Staging Method. International Journal of Data Warehousing and Mining (IJDWM), 12(3):32-50, 2016. [PDF]
  63. [c] H. Huo, X. Liu, J. Li, and H. Yang. A Weighted K-AP Query Method for RSSI based Indoor Positioning. Proc. of the 27th Australasian Database Conference (ADC), pp. 150–163, 2016. [PDF]
  64. [c] X. Liu, Per Sieverts Nielsen. Streamlining Smart Meter Data Analytics. Proc. of the 10th Conference on Sustainable Development of Energy, Water and Environment Systems, SDEWES2015.0558,1-14, 2015. [PDF]
  65. [c] N. Iftikhar, X. Liu, and F. E. Nordbjerg. Relational-Based Sensor Data Cleansing. Proc. of 19th East-European Conference on Advances in Databases and Information Systems (ADBIS), pp. 108-118, 2015. [PDF]
  66. [c] X. Liu, N. Iftikhar. An ETL Optimization Framework Using Partitioning and Parallelization. Proc. of the 30th ACM/SIGAPP Symposium On Applied Computing (SAC), pp. 1015-1022, 2015. [PDF]
  67. [c] X. Liu, L. Golab, I. F. Ilyas. SMAS: A Smart Meter Data Analysis System (demo). Proc. of the 31st International Conference on Data Engineering (ICDE), pp. 1476-1479, 2015. [PDF] [Video]
  68. [c] X. Liu, L. Golab, W. Golab, Ihab F. Ilyas. Benchmarking Smart Meter Data Analytics. Proc. of the 18th International Conference on Extending Database Technology (EDBT), pp. 385-396, 2015 (Paper of the best). [PDF]
  69. [c] X. Liu, N. Iftikhar, and X. Xie, Survey of Real-time Processing Systems for Big Data. Proc. of the 18th International Database Engineering, Applications Symposium (IDEAS), pp. 356-361, 2014. [PDF]
  70. [c] X. Liu, C. Thomsen, and T. B. Pedersen, CloudETL: Scalable Dimensional ETL for Hive. Proc. of the 18th International Database Engineering, Applications Symposium (IDEAS), pp.195-206, 2014. [PDF]
  71. [c] X. Liu and N. Iftikhar. Ontology-based Big Dimension modelling in Data Warehouse Schema Design. Proc. of the 12th Business Information System (BIS), pp. 75-87, 2013. [PDF]
  72. [j] X. Liu, C. Thomsen, and T. B. Pedersen, ETLMR: A Highly Scalable Dimensional ETL Framework Based on MapReduce. TLDKS III, LNCS 7790, 8:1-31, 2013. [PDF]
  73. [c] X. Liu, C. Thomsen, and T. B. Pedersen. MapReduce-based Dimensional ETL Made Easy (demo). PVLDB 5(12): 1882-1885, 2012. [PDF]
  74. [c] X. Liu, C. Thomsen, and T. B. Pedersen. 3XL: An Efficient DBMS-Based Triple-Store. Proc. of DEXA Workshops, pp. 284-288, 2012. [PDF]
  75. [j] X. Liu, C. Thomsen, and T. B. Pedersen. 3XL: Supporting efficient operations on very large OWL Lite triple-stores. Information Systems, 36(4):765-781, 2011. [PDF]
  76. [c] X. Liu, C. Thomsen, and T. B. Pedersen. ETLMR: A Highly Scalable Dimensional ETL Framework Based on MapReduce. Proc. of the 13th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK), pp. 96-111, 2011 [PDF].
  77. [c] X. Liu, C. Thomsen, and T. B. Pedersen. The ETLMR MapReduce-Based ETL Framework. Proc. of the 23rd Scientific and Statistical Database Management Conference (SSDBM), pp. 586-588, 2011. [PDF]
  78. [j] X. Liu. A Data Warehouse Solution for E-Government. International Journal of Research and Reviews in Applied Sciences, 4(1):120-128, 2010. [PDF]
  79. [j] S. Kang and X. Liu. Online Model-based Testing Design Using the Qtronic Tool. Journal of Northeast Normal University (Natural Science Edition), 42(4), 2010.

    Technical Reports:

  80. [t] Bergaentzlé, C., Bolwig, S., Juhler-Verdoner, H., Kubeczko, K., Liu, X., Nørregaard, K., Rossi, J., Steen, D., Stengel, A., & Wieczorek, A. (2021). A Transition Perspective on Demand-Side Flexibility in the Integrated Energy System. Insights from the Danish ISGAN Annex 7 Project 2017-2021. [PDF]
  81. [t] Ben Amer, S., Hjøllund, T., Nielsen, P. S., Madsen, H., Bergsteinsson, H. G., & Liu, X. (2021). Energy data: mapping, barriers and value creation. Technical Report, Technical Unviersity of Denmark. [PDF]
  82. [t] X. Liu, C. Thomsen, and T. B. Pedersen. ETLMR: A Highly Scalable Dimensional ETL Framework Based on MapReduce, TR-29, Department of Computer Science, Aalborg University, 2011. [PDF]
  83. [t] X. Liu, C. Thomsen, and T. B. Pedersen. CloudETL: Scalable Dimensional ETL for Hadoop and Hive, TR-30, Department of Computer Science, Aalborg University, 2012. [PDF]
  84. [t] X. Liu, D. Truscan, and L. Lilius. Online Testing of the ABOT Game Server Using the Qtronic Tool, Turku Centre for Computer Science, 2008. [PDF]

    Other publications:

  85. [o] X. Liu. Regression-based Online Anomaly Detection for Smart Grid Data. Arxiv, CoRR abs/1606.05781, 2016 [PDF].
  86. [o] X. Liu. Optimizing ETL Dataflow Using Shared Caching and Parallelization Methods. Arxiv, CoRR abs/1409.1639, 2014 [PDF].
  87. [o] X. Liu. Two-level Data Staging ETL for Transaction Data. Arxiv, CoRR abs/1409.1636, 2014 [PDF].
  88. [o] X. Liu, C. Thomsen, and T. B. Pedersen. All-RiTE: Right-Time ETL for Live DW Data, 12 pages, 2014 [PDF].

    PhD Thesis:

  89. [o] X. Liu, Data Warehousing Technologies for Large-scale and Right-time Data, defence on June 2012 [PDF]

Teaching

Supervision

Research Projects

I am working on the following research projects, with the role of PI/WP Lead/Participant:

Open Source Software

I developed the following open source software systems in my previous research work (Github: https://github.com/xiufengliu):

  1. FlexSUS: A flexible decision platform or urban energy systems
  2. SEGSys: An online mapping system for segmentation analysis of energy consumption.
  3. CATool: A ready-to-use experiment tool for climate researchers to understand the pitfalls in adaptation decisions in behavioral economics
  4. IoTDashboard: This project is to create a dashboard that can collect indoor and outdoor climate data, and visualize the time series in real time.
  5. K-SC Clustering Algorithm on Spark
  6. DataGenerator-Cluster-Version: A scalable smart meter data generator on Spark
  7. CITIESData: A scalable smart city data management platform
  8. SMAS: A smart meter data analytics system
  9. CloudETL: A Scalable Dimensional ETL for Hive
  10. BigETL: A unified ETL platform for supporting various data processing technologies, including Spark, Hive, Hadoop, Python, Linux Shell script, etc.
  11. 3XL: An efficient OWL/RDF Triple Store Supporting Bulk Operations
  12. ETLMR: A Python-based Dimensional ETL Framework based on MapReduce (Obsolete)
  13. All-RiTE: A Right-time ETL middle-ware Supporting Insert, Update and Delete Operations (not released yet)

trackgit-views _Last updated 2023-01-27