Smart Meter Data Analytics

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):

2024:

  1. [j] Liu, C., Yang, Y., & Liu, X. (2024) A holistic sustainability framework for remanufacturing under uncertainty. Journal of Manufacturing Systems, 76: 540-552. [PDF]
  2. [j] Jia, C., Shi, F., Liu, X., Cheng, X., Zhang, Z., Zhao, M., & Chen, S. (2024). Prompt Learning for Light Field Semantic Segmentation in the Consumer-Centric Internet of Intelligent Computing Things. IEEE Transactions on Consumer Electronics.[PDF]
  3. [c] Liu, R., Liu, Z., HAOYU, Z., Zhang, G., Zhang, J., Sun, B., Liu, X. & Jin, Y. ColVO: Colonoscopic Visual Odometry Considering Geometric and Photometric Consistency. In ACM Multimedia 2024. [PDF]
  4. [j] Peng, J., Kimmig, A., Wang, D., Niu, Z., Liu, X., Tao, X., & Ovtcharova, J. (2024). Carbon footprint tracing and pattern recognition framework based on visual analytics. Sustainable Production and Consumption.[PDF]
  5. [j] Peng, J., Kimmig, A., Wang, D., Niu, Z., Liu, X., Tao, X., & Ovtcharova, J. (2024). Energy consumption forecasting based on spatio-temporal behavioral analysis for demand-side management. Applied Energy, 374, 124027. [PDF]
  6. [j] Wang, R., Qiu, H., Jiang, G., Liu, X., & Cheng, X. (2024). Class-Imbalanced Spatial–Temporal Feature Learning for Blade Icing Recognition of Wind Turbine. IEEE Transactions on Industrial Informatics. [PDF]
  7. [j] Wang, R., Zhao, X., Qiu, H., Cheng, X., & Liu, X. (2024). Uncovering urban water consumption patterns through time series clustering and entropy analysis. Water Research, 262, 122085.[PDF]
  8. [j] Wang, Z., Liu, H., Wu, S., Liu, N., Liu, X., Hu, Y., & Fu, Y. (2024). Explainable time-varying directional representations for photovoltaic power generation forecasting. Journal of Cleaner Production, 143056. [PDF]
  9. [j] Zhao, Y., Zhang, W., Gong, X., & Liu, X. (2024). Carbon futures return forecasting: A novel method based on decomposition-ensemble strategy and Markov process. Applied Soft Computing, 111869. [PDF]
  10. [j] Huang, Y., Zhao, Y., Wang, Z., Liu, X., & Fu, Y. (2024). Sparse dynamic graph learning for district heat load forecasting. Applied Energy, 371, 123685. [PDF]
  11. [j] Zhao, Y., Huang, Y., Wang, Z., & Liu, X.. (2024). Carbon Futures Price Forecasting Based on Feature Selection. Engineering Applications of Artificial Intelligence, 135, 108646 [PDF]
  12. [j] Qiu, H., Shi, K., Wang, R., Zhang, L., Liu, X., & Cheng, X. (2024). A novel temporal-spatial graph neural network for wind power forecasting considering blockage effects. Renewable Energy, 120499. [PDF]
  13. [j] Jiang, G., Zhao, K., Liu, X., Cheng, X., & Xie, P. (2024). A Federated Learning Framework for Cloud-Edge Collaborative Fault Diagnosis of Wind Turbines. IEEE Internet of Things Journal. [PDF]
  14. [j] Zhao, Y., Huang, Y., Wang, Z., & Liu, X.. (2024). A new feature selection method based on importance measures for crude oil return forecasting. Neurocomputing, 127470. [PDF]
  15. [j] Wang, R., Liu, X., Zhao, X., Cheng, X., & Qiu, H. (2024). A novel entropy-based method for quantifying urban energy demand aggregation: Implications for urban planning and policy. Sustainable Cities and Society, 105284. [PDF]
  16. [j] Zhu, J., and Liu, X.. (2024). An integrated intrusion detection framework based on subspace clustering and ensemble learning. Computers and Electrical Engineering. [PDF]
  17. [j] Zhao, Y., Zhang, W., Liu, X.. (2024). Grid Search with a Weighted Error Function: Hyper-Parameter Optimization for Financial Time Series Forecasting, Applied Soft computing. [PDF]
  18. [j] Meng, F., Lu, Z., Li, X., Han, W., Peng, J., Liu, X.*, & Niu, Z. (2024). Demand-Side Energy Management Reimagined: A Comprehensive Literature Analysis Leveraging Large Language Models, Energy, 130303. [PDF]
  19. [j] Hu, Y., Liu, H., Wu, S., Zhao, Y., Wang, Z., & Liu, X.* (2024). Temporal collaborative attention for wind power forecasting. Applied Energy, 357, 122502. [PDF]
  20. [j] Wu, J., Niu, Z., & Liu, X.*. (2024). Understanding Epidemic Spread Patterns: A Visual Analysis Approach, Health Systems, pp. 1-17. [PDF]
  21. [j] Liu, M., Cheng, X., Shi, F., Liu, X., Dai, H., & Chen, S. (2024). A Prototype-empowered Kernel-varying Convolutional Model for Imbalanced Sea State Estimation in IoT-enabled Autonomous Ship. IEEE Transactions on Sustainable Computing [PDF]
  22. [j] Yin, Y., Cheng, X., Shi, F., Liu, X., Huo, H., & Chen, S. (2024). High-order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-based Small Ship Detection. IEEE Transactions on Geoscience and Remote Sensing (Early access). [PDF]
  23. [j] Wen, H., Liu, X., Yang, M., Lei, B., Cheng, X., & Chen, Z. (2024). A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data. Energy，286, 129593. [PDF]

    2023:

  24. [j] Cheng, X., He, T., Shi, F., Zhao, M., Liu, X., & Chen, S. (2023). Selective feature fusion and irregular-aware network for pavement crack detection. IEEE Transactions on Intelligent Transportation Systems (Early access). [PDF]
  25. [c] Huang, T., Xie, X., & Liu, X. (2023, October). Multi-level Correlation Matching for Legal Text Similarity Modeling with Multiple Examples. In International Conference on Web Information Systems Engineering (pp. 621-632). Singapore: Springer Nature Singapore. [PDF]
  26. [j] Cheng, X., Wang, K.,Liu, X., Yu, Q., Shi, F., Ren, Z., & Chen, S. (2023). A Novel Class-Imbalanced Ship Motion Data-Based Deep Learning Model for Sea State Estimation. IEEE Transactions on Intelligent Transportation Systems (Early access). [PDF]
  27. [j] Wang, R., Wu, H., Qiu, H., Wang, F., Liu, X., & Cheng, X. (2023). A Difference Enhanced Neural Network for Semantic Change Detection of Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. [PDF]
  28. [j] Huang, Y., Zhao, Y., Wang, Z., Liu, X., Liu, H., & Fu, Y. (2023). Explainable district heat load forecasting with active deep learning. Applied Energy, 350, 121753. [PDF]
  29. [j] Murino, T., Monaco, R., Nielsen, P. S., Liu, X., Esposito, G., & Scognamiglio, C. (2023). Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations. Energies, 16(15), 5764. [PDF]
  30. [j] Wang, R., Qiu, H., Xu, C., & Liu, X. (2023). Anomaly detection with a container-based stream processing framework for industrial internet of things. Journal of Industrial Information Integration, 100507. [PDF]
  31. [c] Qiu, H., Jin, H., Wang, R., Liu, X., & Gao, G. (2023). Tensor Nuclear Norm Based Matrix Regression Based Projections for Feature Extraction of Hyperspectral Images. In Proceedings of the 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 1037-1042). Rio de Janeiro, Brazil. [PDF]
  32. [c] Wang, R., Yang, Z., Qiu, H., Liu, X., & Wu, D. (2023). Spatial and Channel Exchange based on EfficientNet for Detecting Changes of Remote Sensing Images. In Proceedings of the 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 1595-1600). Rio de Janeiro, Brazil. [PDF]
  33. [c] Cheng, X., Liu, X., Ilieva, I., & Redhu, S. (2023). Temporal-spatial graph neural network for wind power forecasting considering the blockage effects. In Proceedings of the 3rd International Conference on Applied Artificial Intelligence (pp. 1-6). IFE, Halden, Norway. [PDF]
  34. [j] Monaco, R., Liu, X., Murino, T., Cheng, X., & Nielsen, P. S. (2023). A non-functional requirements-based ontology for supporting the development of industrial energy management systems. *Journal of Cleaner Production, 137614. [PDF]
  35. [j] Wang, Z., Liu, X.*, Huang, Y., Zhang, P., & Fu, Y. (2023). A multivariate time series graph neural network for district heat load forecasting. Energy, 127911. [PDF]
  36. [j] Liu, Y., Huang, L., Liu, X., Ji, G., Chen, X., & Onstein, E. (2023). A late-mover genetic algorithm for resource-constrained project-scheduling problems. Information Sciences, 119164. [PDF]
  37. [j] Xiong, B., Guo, Y., Zhang, L., Li, J., Liu, X., & Cheng, L. (2023). Optimizing Electricity Demand Scheduling in Microgrids Using Deep Reinforcement Learning for Cost-Efficiency. IET Generation, Transmission & Distribution. [PDF]
  38. [j] Zhang, D., Cheng, X., Tian, W., Shi, F., Qiu, H., Liu, X., & Chen, S. (2023). FedBIP: A Federated Learning Based Model for Wind Turbine Blade Icing Prediction. IEEE Transactions on Instrumentation & Measurement. doi: 10.1109/TIM.2023.3273675. [PDF]
  39. [j] Biemann, M., Gunkel, P. A., Scheller, F., Huang, L., & Liu, X.* (2023). Data centre HVAC control harnessing flexibility potential via real-time pricing cost optimisation using reinforcement learning. IEEE Internet of Things Journal. doi: 10.1109/JIOT.2023.3263261. [PDF]
  40. [j] Wen, H., Liu, X., Yang, M., Lei, B., Cheng, X., & Chen, Z. (2023). An energy demand-side management and net metering decision framework. Energy, 271, 127075. [PDF]
  41. [j] Chen, Z., Li, J., Cheng, L., & Liu, X. (2023). Federated-WDCGAN: A Federated Smart Meter Data Sharing Framework for Privacy Preservation. Applied Energy, 334, 120711. [PDF]
  42. [c] Liu, X., Cheng, X., Yang, Y., Huo, H., Liu, Y., & Nielsen, P.S. (2023). Understanding crowd energy consumption behaviors (demo). EDBT, pp. 799-802. [PDF]
  43. [j] Wang, S., Gao, J., Lu, F., Wang, F., You, Z., Huang, M., Liu, X., Li, Y., & Liu, Y. (2023). Human motion recognition by a shoes-floor triboelectric nanogenerator and its application in fall detection. Nano Energy, 108230. [PDF]
  44. [j] Ezhilarasi, P., Ramesh, L., Liu, X., & Holm-Nielsen, J.B. (2023). Smart Meter Synthetic Data Generator development in python using FBProphet. Software Impacts, 100468. [PDF]

    2022:

  45. [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]
  46. [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]
  47. [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]
  48. [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]
  49. [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]
  50. [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]
  51. [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]
  52. [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. [PDF]
  53. [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]
  54. [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]
  55. [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]
  56. [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:

  57. [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].
  58. [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]
  59. [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.
  60. [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.
  61. [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]
  62. [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]
  63. [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]
  64. [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]
  65. [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]
  66. [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].
  67. [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].
  68. [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:

  69. [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].
  70. [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].
  71. [J] Y. Zhou, Y. Yang, H. Liu, X. Liu, N. Savage. Deep Learning Based Fusion Approach for Hate Speech Detection. IEEE Access, 2020 [PDF].
  72. [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]
  73. [c] X. Wang, X. Liu, X. Zhong and P. Cheng. View Selection for Graph Pattern Matching, Proc. of DEXA, pp. 93-110, 2020. [PDF]
  74. [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:

  75. [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]
  76. [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]
  77. [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]
  78. [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.
  79. [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]
  80. [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:

  81. [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]
  82. [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]
  83. [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]
  84. [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]
  85. [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]
  86. [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]
  87. [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]
  88. [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]
  89. [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]
  90. [j] A. Heller, X. Liu, and P. Gianniou. Science Cloud for Smart Cities Research. Energy Procedia, vol. 122, pp. 679-684. [PDF]
  91. [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]
  92. [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]
  93. [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]
  94. [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]
  95. [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]
  96. [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]
  97. [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]
  98. [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]
  99. [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]
  100. [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]
  101. [j] X. Liu, P. S. Nielsen. An ICT-Solution for Smart Meter Data Analytics. Journal of Energy, 115(3):1710-1722, 2016. [PDF]
  102. [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]
  103. [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]
  104. [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]
  105. [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]
  106. [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]
  107. [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]
  108. [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]
  109. [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]
  110. [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]
  111. [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]
  112. [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]
  113. [c] X. Liu, C. Thomsen, and T. B. Pedersen. MapReduce-based Dimensional ETL Made Easy (demo). PVLDB 5(12): 1882-1885, 2012. [PDF]
  114. [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]
  115. [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]
  116. [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].
  117. [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]
  118. [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]
  119. [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:

  120. [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]
  121. [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]
  122. [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]
  123. [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]
  124. [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:

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

    PhD Thesis:

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

trackgit-views _Last updated 2024-08-30