Data Mining - Concepts and Techniques.

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Experimental And Quasi Experimental Designs For Research

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

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ACM Transactions on Database Systems

In recent years, real-time processing and analytics systems for big data--in the context of Business Intelligence (BI)--have received a growing attention. The traditional BI platforms that perform regular updates on daily, weekly or monthly basis are no longer adequate to satisfy the fast-changing business environments. However, due to the nature of big data, it has become a challenge to achieve the real-time capability using the traditional technologies. The recent distributed computing technology, MapReduce, provides off-the-shelf high scalability that can significantly shorten the processing time for big data; Its open-source implementation such as Hadoop has become the de-facto standard for processing big data, however, Hadoop has the limitation of supporting real-time updates. The improvements in Hadoop for the real-time capability, and the other alternative real-time frameworks have been emerging in recent years. This paper presents a survey of the open source technologies that support big data processing in a real-time/near real-time fashion, including their system architectures and platforms.

Survey of real-time processing systems for big data

Survey of big scholarly data with respect to the different phases of the big data lifecycle.Identifies the different big data tools and technologies that can be used for development of scholarly applications.Investigates research challenges and limitations specific to big scholarly data and its applications.Provides research directions and paves way towards the development of a generic and comprehensive big scholarly data platform. Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle.

/pdf/a-survey-on-scholarly-data-4n3kfnn85f.pdf

A survey on scholarly data

https://backend.orbit.dtu.dk/ws/files/147058836/clusteranalysis.pdf

Clustering-based analysis for residential district heating data

/pdf/clustering-based-analysis-clustering-based-analysis-for-161l6sodzf.pdf

Clustering-Based Analysis: clustering-based analysis for residential district heating data

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, this task is implemented in a centralized manner with the assumption that utilities have access to all the smart meter data. However, smart meter data are measured and owned by different retailers in the retail market who may not be willing to share their data. To this end, a distributed electricity consumer characteristics identification method is proposed based on federated learning, which can preserve the privacy of retailers. Specifically, privacy-perseverance principal component analysis (PCA) is exploited to extract features from smart meter data. On this basis, an artificial neural network is trained in a federated manner with three weighted averaging strategies to bridge between smart meter data and the socio-demographic characteristics of consumers. Case studies on the Irish Commission for Energy Regulation (CER) dataset verify that the proposed federated method has comparable performance with the centralized model on both balanced and unbalanced datasets.

Xiufeng Liu

Papers

Survey of real-time processing systems for big data

A survey on scholarly data

Clustering-based analysis for residential district heating data

Clustering-Based Analysis: clustering-based analysis for residential district heating data

Electricity Consumer Characteristics Identification: A Federated Learning Approach