Xiufeng Liu

Xiufeng Liu
Technical University of Denmark | DTU · Department of Management Engineering

PhD in Computer Science

About

88
Publications
64,634
Reads
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1,214
Citations
Citations since 2017
58 Research Items
1062 Citations
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2017201820192020202120222023050100150200250300
2017201820192020202120222023050100150200250300
Additional affiliations
October 2018 - present
Technical University of Denmark
Position
  • Professor (Associate)
January 2015 - September 2018
Technical University of Denmark
Position
  • Professor (Assistant)
January 2013 - December 2014
IBM, Canada
Position
  • Reseach scientist
Education
August 2008 - June 2011
Aalborg University
Field of study

Publications

Publications (88)
Article
Energy consumption data are crucial for various smart energy management applications, such as demand forecasting, customer segmentation, and energy efficiency analysis. However, collecting sufficient training data can be a challenge due to data privacy concerns, technical barriers, and cost issues. This paper presents a novel data generation model...
Article
Wind farms are typically located at high latitudes, resulting in a high risk of blade icing. Data-driven approaches offer promising solutions for blade icing detection, but they rely on a considerable amount of data. Data exchange between multiple wind farms would improve the performance of detection models, due to the spatio-temporal dependencies...
Article
Full-text available
Blade icing detection is critical to maintaining the health of wind turbines, especially in cold climates. Rapid and accurate icing detection allows proper control of wind turbines, including shutting down and clearing the ice, thus ensuring turbine safety. This paper presents a wavelet-driven multiscale graph convolutional network (MWGCN), which i...
Article
Understanding energy consumption patterns is crucial for energy demand-side management. Unlike traditional data mining or machine learning-based methods, this paper presents visual analysis methods for exploring energy consumption data from spatial, temporal, and spatiotemporal dimensions, including variability, segmentation, and energy demand shif...
Article
Full-text available
Indoor air quality (IAQ) is an important parameter in protecting the occupants of an indoor environment. Previous studies have shown that an indoor environment with poor ventilation increases airborne virus transmission. Existing research has concluded that high ventilation rates can reduce the risk of individuals in indoor environments being infec...
Article
Full-text available
As an important stage of life cycle management, machinery PHM (prognostics and health management), an emerging subject in mechanical engineering, has seen a huge amount of research. Here the authors present a comprehensive overview that details previous and current efforts in PHM from an industrial big data perspective. The authors first analyze th...
Preprint
Full-text available
With increasing concerns for data privacy and ownership , recent years have witnessed a paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL), has gained great attention and has become a novel design for machine learning implementations. FL enables the ML model training at data silos under the coordination of a cent...
Preprint
Full-text available
With increasing concerns for data privacy and ownership, recent years have witnessed a paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL), has gained great attention and has become a novel design for machine learning implementations. FL enables the ML model training at data silos under the coordination of a centr...
Article
Residential energy consumption data and related sociodemographic information are critical for energy demand management, including providing personalized services, ensuring energy supply, and designing demand response programs. However, it is often difficult to collect sufficient data to build machine learning models, primarily due to cost, technica...
Article
Full-text available
Wind farms are often located at high latitudes, which entails a high risk of icing for wind turbine blades. Traditional anti-icing methods rely primarily on manual observation, the use of special materials, or external sensors/tools, but these methods are limited by human experience, additional costs, and understanding of the mechanical mechanism....
Preprint
Full-text available
In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improvi...
Chapter
With the explosion of social media, the Web, Internet of Things, and the proliferation of smart devices, large amounts of data are being generated each day. However, traditional data management technologies are increasingly inadequate to cope with this growth in data. NoSQL has become increasingly popular as this technology can provide consistent,...
Article
Wind turbines are being designed with increasingly sophisticated electrical and mechanical components, leading to more complicated maintenance procedures and higher failure costs. Much of the research in this area has provided reliable early warning of wind turbine failures in an effort to reduce downtime and maintenance costs. Recently, data-drive...
Chapter
In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improvi...
Conference Paper
Automatic control of energy systems is affected by the uncertainties of multiple factors, including weather, prices and human activities. The literature relies on Markov-based control, taking only into account the current state. This impacts control performance, as previous states give additional context for decision making. We present two ways to...
Article
Understanding urban demand profiles is an important determinant for energy dispatch and the optimization of the electric energy supply. For the design of the energy supply system, an important consideration is, to express the characteristics of urban household energy demand as a function of space and time. However, the focus of most research activi...
Chapter
Today’s data warehousing requires continuous or on-demand data integration through a Change-Data-Capture (CDC) process to extract data deltas from Online Transaction Processing Systems. This paper proposes a workload-aware CDC framework for on-demand data warehousing. This framework adopts three CDC strategies, namely trigger-based, timestamp-based...
Article
Full-text available
Controlling heating, ventilation and air-conditioning (HVAC) systems is crucial to improving demand-side energy efficiency. At the same time, the thermodynamics of buildings and uncertainties regarding human activities make effective management challenging. While the concept of model-free reinforcement learning demonstrates various advantages over...
Article
Wind energy is of great importance for future energy development. In order to fully exploit wind energy, wind farms are often located at high latitudes, a practice that is accompanied by a high risk of icing. Traditional blade icing detection methods are usually based on manual inspection or external sensors/tools, but these techniques are limited...
Article
Full-text available
Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-di...
Article
Energy demand-side management, especially empowered by the fine-grained smart meter data, plays a significant role in the rational allocation of energy, monitoring, and supervision of energy consumption behaviors. Through the in-depth demand analysis including quantification of energy consumption dynamics and consumer preferences, energy decision-m...
Article
Full-text available
Nowadays, smart meters are deployed in millions of residential households to gain significant insights from fine-grained electricity consumption data. The information extracted from smart meter data enables utilities to identify the socio-demographic characteristics of electricity consumers and then offer them diversified services. Traditionally, t...
Preprint
Full-text available
Customer segmentation analysis can give valuable insights into the energy efficiency of residential buildings. This paper presents a mapping system, SEGSys that enables segmentation analysis at the individual and the neighborhood levels. SEGSys supports the online and offline classification of customers based on their daily consumption patterns and...
Chapter
Monitoring abnormal energy consumption is helpful for demand-side management. This paper proposes a framework for contextual anomaly detection (CAD) for residential energy consumption. This framework uses a sliding window approach and prediction-based detection method, along with the use of a concept drift method to identify the unusual energy cons...
Conference Paper
Full-text available
In the context of urbanization and the rapid growth of energy demand, understanding the spatial and temporal dynamics of urban energy use is crucial for identifying energy-saving potentials. In this demo, we present a visual analysis tool, VAP, that allows users to explore the dynamics of urban energy use at different spatial and temporal scales. I...
Article
Full-text available
In recent years, the increasing prevalence of hate speech in social media has been considered as a serious problem worldwide. Many governments and organizations have made significant investment in hate speech detection techniques, which have also attracted the attention of the scientific community. Although plenty of literature focusing on this iss...
Article
Adaptation to climate change is an intricate decision-making process that requires balancing costs and uncertain benefits in a setting with high stakes and low probabilities. Risk preferences then shape the way individuals or groups adapt to these settings, dependent or independent of public policy. With the aim of shedding light on these preferenc...
Preprint
Full-text available
Understanding demand-side energy behaviour is critical for making efficiency responses for energy demand management. We worked closely with energy experts and identified the key elements of the energy demand problem including temporal and spatial demand and shifts in spatiotemporal demand. To our knowledge, no previous research has investigated the...
Chapter
Real-time energy consumption monitoring is becoming increasingly important in smart energy management as it provides the opportunity for novel applications through data analytics, including anomaly detection, energy leakage, and theft. This paper presents a smart non-intrusive load monitoring approach for residential households, collecting fine-gra...
Preprint
Full-text available
Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate differe...
Chapter
Full-text available
The world’s population has been growing continuously, with most people inhabiting urban settlements. Furthermore, air pollution has become a growing concern, mainly in densely populated cities, where human health is threatened by acute air pollution episodes. The H2020 ClairCity project aims to substantially improve future air quality and carbon po...
Article
Full-text available
User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or...
Article
Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and...
Preprint
Full-text available
Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate differe...
Chapter
Data warehousing populates data from different source systems into a central data warehouse (DW) through extraction, transformation, and loading (ETL). Massive transaction data are routinely recorded in a variety of applications such as retail commerce, bank systems, and website management. Transaction data record the timestamp and relevant referen...
Article
The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze residential heating consumption data and evaluate info...
Article
Full-text available
Today smart meters are widely used in the energy sector to record energy consumption in real time. Large amounts of smart meter data have been accumulated and used for diverse analysis purposes. Anomaly detection raises the big data problem, namely the detection of abnormal events or unusual consumption behaviors. However, there is a lack of approp...
Conference Paper
Full-text available
Cities worldwide aim to reduce their greenhouse gas emis- sions and improve air quality for their citizens. Therefore, there is a need to implement smart city approaches to monitor, model, and understand local emissions to better guide these actions. We present our approach that deploys a number of low-cost sensors through a wireless Internet of Th...
Article
Full-text available
Smart city data come from heterogeneous sources including various types of the Internet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper, we propose a f...
Conference Paper
Full-text available
Today, smart meters are being used worldwide. As a matter of fact smart meters produce large volumes of data. Thus, it is important for smart meter data management and analytics systems to process petabytes of data. Benchmarking and testing of these systems require scalable data, however, it can be challenging to get large data sets due to privacy...
Article
Full-text available
Cities are densely populated and heavily equipped areas with a high level of service provision. Smart cities can use these conditions to achieve the goals of a smart society for their citizens. To facilitate such developments, the necessary IT-infrastructure has to be in place for supporting, amongst many other things, the whole lifecycle of big da...
Conference Paper
Full-text available
Air quality monitoring has become an integral part of smart city solutions. This paper presents an air quality monitoring system based on Internet of Things (IoT) technologies, and establishes a cloud-based platform to address the challenges related to IoT data management and processing capabilities, including data collection, storage, analysis, an...
Article
Full-text available
With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has ma...
Conference Paper
Collaborative filtering (CF) is successfully applied to recommendation system by digging the latent features of users and items. However, conventional CF-based models usually suffer from the sparsity of rating matrices which would degrade model’s recommendation performance. To address this sparsity problem, auxiliary information such as labels are...
Article
Full-text available
This research paper proposes a quality framework for higher education that evaluates the performance of institutions on the basis of performance of outgoing students. Literature was surveyed to evaluate existing quality frameworks and develop a framework that provides insights on an unexplored dimension of quality. In order to implement and test th...
Article
Full-text available
Smart electricity meters have been replacing conventional meters worldwide, enabling automated collection of fine-grained (e.g., every 15 minutes or hourly) consumption data. A variety of smart meter analytics algorithms and applications have been proposed, mainly in the smart grid literature. However, the focus has been on what can be done with th...
Article
Full-text available
In data warehousing, the data from source systems are populated into a central data warehouse (DW) through extraction, transformation and loading (ETL). The standard ETL approach usually uses sequential jobs to process the data with dependencies, such as dimension and fact data. It is a non-trivial task to process the so-called early-/late-arriving...
Conference Paper
The paper studies the establishment of offline fingerprint library based on RSSI (Received Signal Strength Indication), and proposes WF-SKL algorithm by introducing the correlation between RSSIs. The correlations can be transformed as AP fingerprint sequence to build the offline fingerprint library. To eliminate the positioning error caused by inst...
Conference Paper
With the prevalence of cloud computing and Internet of Things (IoT), smart meters have become one of the main components of smart city strategy. Smart meters generate large amounts of fine-grained data that is used to provide useful information to consumers and utility companies for decisionmaking. Now-a-days, smart meter analytics systems consist...
Conference Paper
Full-text available
With the widely use of smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of p...
Article
Full-text available
With the widely used smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of par...
Article
Smart meters are increasingly used worldwide. Smart meters are the advanced meters capable of measuring energy consumption at a fine-grained time interval, e.g., every 15 min. Smart meter data are typically bundled with social economic data in analytics, such as meter geographic locations, weather conditions and user information, which makes the da...
Conference Paper
Full-text available
Today smart meters are increasingly used in worldwide. Smart meters are the advanced meters capable of measuring customer energy consumption at a fine-grained time interval, e.g., every 15 minutes. The data are very sizable, and might be from different sources, along with the other social-economic metrics such as the geographic information of meter...
Conference Paper
Full-text available
Today sensors are widely used in many monitoring applications. Due to some random environmental effects and/or sensing failures, the collected sensor data is typically noisy. Thus, it is critical to cleanse the data before using it for answering queries or for data analysis. Popular data cleansing approaches, such as classification, prediction and...
Conference Paper
Full-text available
Extract-Transform-Load (ETL) handles large amounts of data and manages workload through dataflows. ETL dataflows are widely regarded as complex and expensive operations in terms of time and system resources. In order to minimize the time and the re-sources required by ETL dataflows, this paper presents an optimiza-tion framework using partitioning...
Conference Paper
Full-text available
Smart electricity meters are replacing conventional meters worldwide and have enabled a new application domain: smart meter data analytics. In this paper, we introduce SMAS, our smart meter analytics system, which demonstrates the actionable insight that consumers and utilities can obtain from smart meter data. Notably, we implemented SMAS inside a...
Conference Paper
Full-text available
Smart electricity meters have been replacing conventional meters worldwide, enabling automated collection of fine-grained (every 15 minutes or hourly) consumption data. A variety of smart meter analytics algorithms and applications have been proposed, mainly in the smart grid literature, but the focus thus far has been on what can be done with the...