Industrial power demand forecasting based on big data technology orienting to energy internet: A case study of Hunan Province

With the coming of big data age, Internet, finance and other industries have launched in-depth studies on big data technology. They hope to grasp the opportunities that big data brings to enterprises. Smart gird construction generated massive and heterogeneous data in the process of electricity generation, electricity transmission and electricity consumption, thus electricity big data took shape. Based on the analysis of Big Data characteristics of Smart gird user-side, this paper describes the risks that big data reduces on smart gird user-side from the perspectives of demand forecasting, customer complaint and operation risk that grid peak valley load brings. Meanwhile, it also expounds the risks that big data brings to Smart gird user-side from the perspectives of technology and user information security. Hope to provide some relevant materials of the Smart gird user-side risk management for our country.

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Construction of Ecological Environment Information System Based on Big Data: A Case Study on Dongting Lake Ecological Area

Mobile Information Systems ◽

10.1155/2021/3885949 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Diandi Wan ◽

Shaohua Yin

Keyword(s):

Information System ◽

Big Data ◽

Rapid Development ◽

Dongting Lake ◽

Ecological Environment ◽

Ecological Problem ◽

Collection Data ◽

Technology Trend ◽

Big Data Technology

With the rapid development of cloud computing, Internet of Things, and other technologies, the information technology trend led by “big data” has an impact on all fields. The application of big data technology in the field of ecological environmental protection enables accurate and comprehensive ecological information collection, data analysis, and mining, accurate ecological problem identification, and effective solution. Taking Dongting Lake Ecological Area as an example, this paper constructs an ecological environment information system based on big data and expounds its specific application in water, atmosphere, soil environment monitoring, and pollution control, aiming to provide a reference for the application of big data technology in the field of ecological environment protection in Dongting Lake Ecological Area and more effectively maintain the ecological environmental quality and safety in the area.

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Social Media Big Data Analytics for Demand Forecasting

Journal of Global Information Management ◽

10.4018/jgim.2020010106 ◽

2020 ◽

Vol 28 (1) ◽

pp. 103-120 ◽

Cited By ~ 4

Author(s):

Rehan Iftikhar ◽

Mohammad Saud Khan

Keyword(s):

Social Media ◽

Big Data ◽

Supply Chain ◽

Demand Forecasting ◽

Big Data Analytics ◽

Predictive Modelling ◽

Product Demand ◽

Emotion Model ◽

Improving Accuracy

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

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Practical Recommendation for the Use of Big Data in Strategic Management: Case Study of a Kazakh Telecommunications Company

Journal of Management and Strategy ◽

10.5430/jms.v9n3p88 ◽

2018 ◽

Vol 9 (3) ◽

pp. 88

Author(s):

Gulnara Z. Karimova ◽

Yevgeniya Kim

Keyword(s):

Big Data ◽

Strategic Management ◽

Economic Benefits ◽

Practical Recommendation ◽

Tactical Decisions ◽

Management Case Study ◽

Decision Making Tool ◽

Big Data Technology ◽

Telecommunications Company

This study analyzes the dynamics in the development and use of new, innovative technologies based on big data as they allow companies to expand their range of services, using large amounts of data, which in turn enables them to obtain economic benefits. Big data is often used as a tool for undertaking managerial tactical decisions rather than strategic. Using the practices of a Kazakh telecommunications company as an example, this study demonstrates how the potential of big data is limited to the decision-making tool and suggests how big data technology can improve the efficiency of the strategic management.

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Analysis on the influence of big data era on enterprise financial accounting -- a case study of HUA WEI technologies co., LTD

E3S Web of Conferences ◽

10.1051/e3sconf/202021401004 ◽

2020 ◽

Vol 214 ◽

pp. 01004

Author(s):

Wang Yang

Keyword(s):

Big Data ◽

Financial Accounting ◽

Work Flow ◽

Electronic Document ◽

Application Process ◽

Financial Decision Making ◽

Financial Decision ◽

The Impact ◽

Big Data Technology

”Big data” is the product of the integration of the highly developed Internet innovation function and various economic fields in today’s society. The development of “big data” is bound to bring significant changes in the economic development of today’s society. Taking HUA WEI technologies co., LTD., financial aspects based on the development of big data, found big data technology in the application process of the impact of the financial accounting, this era of big data work flow for the company in China, the impact of financial decision-making and financial personnel, and the company response to this phenomenon and make a change, and to analyze its causes and solutions. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

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The application framework of big data technology in the COVID-19 epidemic emergency management in local government—a case study of Hainan Province, China

BMC Public Health ◽

10.1186/s12889-021-12065-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zijun Mao ◽

Qi Zou ◽

Hong Yao ◽

Jingyi Wu

Keyword(s):

Big Data ◽

Emergency Management ◽

Local Governments ◽

Management Strategies ◽

Application Framework ◽

Hainan Province ◽

Technology Application ◽

Management Measures ◽

Big Data Technology

Abstract Background As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China’s SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. Methods This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan’s application of big data technology in its COVID-19 epidemic emergency management. Results Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. Conclusions This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.

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An approach of electric power demand forecasting using data-mining method: a case study of application of data-mining technique to improve decision making

International Journal of Management and Decision Making ◽

10.1504/ijmdm.2006.008173 ◽

2006 ◽

Vol 7 (1) ◽

pp. 88 ◽

Cited By ~ 3

Author(s):

Toshio Sugihara

Keyword(s):

Data Mining ◽

Decision Making ◽

Electric Power ◽

Demand Forecasting ◽

Mining Method ◽

Power Demand ◽

Data Mining Technique ◽

Mining Technique ◽

Using Data

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Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant

Energies ◽

10.3390/en14216915 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6915

Author(s):

Seung-Mo Je ◽

Hyeyoung Ko ◽

Jun-Ho Huh

Keyword(s):

Big Data ◽

Renewable Energy ◽

Power Plant ◽

Electric Power ◽

Demand Forecasting ◽

Power Production ◽

Supply System ◽

Power Demand ◽

Photovoltaic Power ◽

Electric Power Production

This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the form of energy that is produced through ESS or that is pumped through water power generation without taking the amount of new renewable energy that is generated from sources such as thermal power, nuclear power, and hydropower into consideration. This occurs due to the difficulty of predicting power production using new renewable energy and the absence of a prediction system, which is a problem due to the inefficiency of changing energy types. Therefore, using game theory, the theoretical foundation of a power demand forecasting model based on big data-based renewable energy production forecasting was prepared.

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Managing extracted knowledge from big social media data for business decision making

Journal of Knowledge Management ◽

10.1108/jkm-07-2015-0296 ◽

2017 ◽

Vol 21 (2) ◽

pp. 275-294 ◽

Cited By ~ 36

Author(s):

Wu He ◽

Feng-Kwei Wang ◽

Vasudeva Akula

Keyword(s):

Social Media ◽

Big Data ◽

Business Intelligence ◽

Business Decisions ◽

Business Decision ◽

Social Media Data ◽

Content Type ◽

Media Data ◽

Big Data Technology

Purpose This paper aims to propose a knowledge management (KM) framework for leveraging big social media data to help interested organizations integrate Big Data technology, social media and KM systems to store, share and leverage their social media data. Specifically, this research focuses on extracting valuable knowledge on social media by contextually comparing social media knowledge among competitors. Design/methodology/approach A case study was conducted to analyze nearly one million Twitter messages associated with five large companies in the retail industry (Costco, Walmart, Kmart, Kohl’s and The Home Depot) to extract and generate new knowledge and to derive business decisions from big social media data. Findings This case study confirms that this proposed framework is sensible and useful in terms of integrating Big Data technology, social media and KM in a cohesive way to design a KM system and its process. Extracted knowledge is presented visually in a variety of ways to discover business intelligence. Originality/value Practical guidance for integrating Big Data, social media and KM is scarce. This proposed framework is a pioneering effort in using Big Data technologies to extract valuable knowledge on social media and discover business intelligence by contextually comparing social media knowledge among competitors.

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