Big Data Analytics in China's Electric Power Industry: Modern Information, Communication Technologies, and Millions of Smart Meters

Chen, Kang; Wang, Yi; Xue, Yusheng; Mu, Gang; Liao, Ruijin

doi:10.1109/mpe.2018.2790819

Cited by 41 publications

(13 citation statements)

References 5 publications

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“…With the development of the smart grid, on-line condition monitoring data have shown large-scale and high-dimensional characteristics in recent years. These multivariate time series data provide valuable information for grid operation, load forecasting, and power equipment fault diagnosis [1]. However, missing values inevitably exist in original datasets due to various factors, such as sensor malfunctions or communication interruptions during the data acquisition process.…”

Section: Background and Motivationmentioning

confidence: 99%

Three‐step imputation of missing values in condition monitoring datasets

Liu

Wang

Chen

2020

IET Generation, Transmission & Distribution

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Section: Background and Motivationmentioning

confidence: 99%

Three‐step imputation of missing values in condition monitoring datasets

Liu

Wang

Chen

2020

IET Generation, Transmission & Distribution

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“…A comprehensive survey on the life cycle of data and data trading, such as studying a variety of data pricing models, categorizing them into different groups, and comparison of these models is presented in [7]. Kang et al in [8] introduce some recent progress in the application of big data analytics in China's electric power industry. A study on the compression techniques for smart meter big data is presented in [9].…”

Section: Big Data Concepts In Smart Gridmentioning

confidence: 99%

Evaluation of big data frameworks for analysis of smart grids

Ansari

Vakili

2019

J Big Data

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The smart grid combines the traditional power system with information technology leading to a one of most important modern cyber-physical systems. Smart grid is envisioned to fully integrate high-speed and two-way communication technologies into millions of power equipment to establish a dynamic and interactive infrastructure with new energy management capabilities, such as advanced metering infrastructure (AMI) and demand response. Smart grid heavily relies on information and communication technology to achieve efficient and reliable operation [1]. At the same time, smart grid big data has provided new opportunities for electric load forecasting, anomaly detection (e.g power theft), and demand side-management. However, the high-dimensional and massive smart grid big data creates new challenges in data transmission, data storage, and data analysis. This paper addresses the problem of creating a benchmark for big data frameworks used in smart grid big data analysis. We also develop a realistic smart grid data generator for performance analysis in real conditions. Motivation The rapid growth of smart grid, deployment of modern information and communication technologies and millions of newly deployed smart meters, will generate large amount of smart grid. Smart grid big data analysis is considered to be the key to solving significant problems of this industry. With the exponential growth of data, how to efficiently utilize

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“…Water utilities integration needs to be planned carefully to ensure the durability of the communications network [2]. Extensive research has been done for electricity consumption modelling, which has significant differences [6,7,8]. However, electricity metering can offer measurements of higher granularity and accuracy compared to water metering devices [9].…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

2020

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In recent years, scarce water resources became one of the main problems that endanger human species existence and the advancement of any nation. In this research, smart water meters were implemented, distributed, and installed in a regional area in Cairo while data were collected at uniform intervals then sent to the cloud instantly. The solution paradigm uses an Internet of Things (IoT) based on micro-services and containers. The design incorporates real-time streaming and infrastructure performance optimization to store data. A second layer to analyze the acquired data was used to model water consumption using Long Short-Term Memory (LSTM). The designed LSTM is validated and tested to be utilized in the forecast of future water demand. Moreover, two alternative machine learning methods, namely Support Vector Regression and Random Forest commonly utilized in time series forecasting applications, were used for a comparative analysis of which LSTM has proven to be superior. The proper integration of the system elements is the key to the proposed system success. Based on the success of the designed system, it can be applicable on a national scale. That can enable the optimal management of consumers' demand and improve water infrastructure utilization. The proposed paradigm presents a testbed for various scenarios that can be used in water resources management.

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Big Data Analytics in China's Electric Power Industry: Modern Information, Communication Technologies, and Millions of Smart Meters

Cited by 41 publications

References 5 publications

Three‐step imputation of missing values in condition monitoring datasets

Three‐step imputation of missing values in condition monitoring datasets

Evaluation of big data frameworks for analysis of smart grids

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

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