Research on key issues of data integration technology in electric power system in big data environment

Liu, Donglan; Ma, Lei; Liu, Xin; Yu, Hao; H, Tan; Zhao, Xiaohong; Zhao, Yong; Lv, Guodong

doi:10.1109/iccsn.2017.8230333

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Other than that, as information technology has become the enabler in every industry, the volume of data gathered, tabulated and published increased significantly. This also promotes the development of big data [3]. Big data, a new term used to describe sets of data that suits the criteria of: volume, velocity and variety (3Vs) [4].…”

Section: Introductionmentioning

confidence: 99%

Diagnosing the Issues and Challenges in Data Integration Implementation in Public Sector

Hassan¹,

Ahmad²,

Salehuddin³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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A reliable data is like the oxygen to the application systems. It keeps the application systems breathing and producing meaningful information for day to day operation and decision-making purpose in an organization. The increasing demand of reliable data in day to day operation sparks a big challenge in data integration implementation in many domains including public sector. Through a successful data integration implementation, a trustable non duplicate data will be provided to the stakeholders. Public sector as a domain which solely rely on creating value through services to the stakeholders critically in needs of reliable data to serve the reliable information to the stakeholders. However, there is lack of research being done in diagnosing the issues and challenges of data integration implementation in public sector. It is crucial to identify the issues and challenges so that we could come out with the best recommendation to ensure the feasibleness of data integration in public sector. Thus, this research had explored the issues and challenges in data integration implementation in public sector. Data has been collected using the qualitative method through the content analysis and expert interview approaches. Four main issues and challenges were identified, namely; (1) lack of management and organization support, (2) policy, standard and politics, (3) human resource incapability, and (4) lack of governance. The finding of this research enlightens the issues and challenges in data integration implementation in public sector and offers the opportunity for future work to propose the solutions to overcome the identified issues and challenges.

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Section: Introductionmentioning

confidence: 99%

Diagnosing the Issues and Challenges in Data Integration Implementation in Public Sector

Hassan¹,

Ahmad²,

Salehuddin³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Framework of Intelligent Analysis and Mining for Power Big Data

Wang

Zhang

Huang

et al. 2018

IOP Conf. Ser.: Earth Environ. Sci.

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A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems

Czapaj¹,

Kamiński

Sołtysik

2022

Energies

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The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in particular. The annual effectiveness of forecasting power demand for the Polish National Power Grid for the next day is approx. 1%; therefore, the main objective of the review is to verify whether it is possible to improve efficiency while maintaining the minimum financial outlays and time-consuming efforts. The methods that fulfil these conditions are autoregressive methods; therefore, the paper focuses on autoregressive methods, which are less time-consuming and, as a result, cheaper in development and applications. The prepared review ranks the forecasting models in terms of the forecasting effectiveness achieved in the literature on the subject, which enables the selection of models that may improve the currently achieved effectiveness of the transmission system operator. Due to the applied approach, a transparent set of forecasting methods and models was obtained, in addition to knowledge about their potential in the context of the needs for short-term forecasting of electricity demand in the national power system. The articles in which the MAPE error was used to assess the quality of short-term forecasts were analyzed. The investigation included 47 articles, several dozen forecasting methods, and 264 forecasting models. The articles date from 1997 and, apart from the autoregressive methods, also include the methods and models that use explanatory variables (non-autoregressive ones). The input data used come from the period 1998–2014. The analysis included 25 power systems located on four continents (Asia, Europe, North America, and Australia) that were published by 44 different research teams. The results of the review show that in the autoregressive methods applied to forecasting short-term power demand, there is a potential to improve forecasting effectiveness in power systems. The most promising prognostic models using the autoregressive approach, based on the review, include Fuzzy Logic, Artificial Neural Networks, Wavelet Artificial Neural Networks, Adaptive Neurofuse Inference Systems, Genetic Algorithms, Fuzzy Regression, and Data Envelope Analysis. These methods make it possible to achieve the efficiency of short-term forecasting of electricity demand with hourly resolution at the level below 1%, which confirms the assumption made by the authors about the potential of autoregressive methods. Other forecasting models, the effectiveness of which is high, may also prove useful in forecasting by electricity system operators. The paper also discusses the classical methods of Artificial Intelligence, Data Mining, Big Data, and the state of research in short-term power demand forecasting in power systems using autoregressive and non-autoregressive methods and models.

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Research on key issues of data integration technology in electric power system in big data environment

Cited by 3 publications

References 6 publications

Diagnosing the Issues and Challenges in Data Integration Implementation in Public Sector

Diagnosing the Issues and Challenges in Data Integration Implementation in Public Sector

Framework of Intelligent Analysis and Mining for Power Big Data

A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems

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