Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools

El desarrollo industrial y económico de los países industrializados, a partir del siglo XIX, ha ido de la mano del desarrollo de la electricidad, del motor de combustión interna, de los ordenadores, de Internet, de la utilización de datos y del uso intensivo del conocimiento centrado en la ciencia y la tecnología. La mayoría de las fuentes de energía convencionales han demostrado ser finitas y agotables. A su vez, las diferentes actividades de producción de bienes y servicios que utilizan combustibles fósiles y energía convencional, han aumentado significativamente la contaminación del medio ambiente, y con ello, han contribuido al calentamiento global. El objetivo de este trabajo fue realizar una aproximación teórica a las tecnologías de análisis de datos e inteligencia de negocio aplicadas a las redes de sistemas eléctricos inteligentes con energías renovables. Para este trabajo se realizó una revisión bibliométrica y bibliográfica sobre Big Data Analytics, herramientas TIC de la industria 4.0 y Business intelligence en diferentes bases de datos disponibles en el dominio público. Los resultados del análisis indican la importancia del uso de la analítica de datos y la inteligencia de negocio en la gestión de las empresas energéticas. El trabajo concluye señalando cómo se está aplicando la inteligencia de negocio y la analítica de datos en ejemplos concretos de empresas energéticas y su creciente importancia en la toma de decisiones estratégicas y operativasThe industrial and economic development of the industrialized countries, from the nineteenth century, has gone hand in hand with the development of electricity, the internal combustion engine, computers, the Internet, data use and the intensive use of knowledge focused on science and the technology. Most conventional energy sources have proven to be finite and exhaustible. In turn, the different production activities of goods and services using fossil fuels and conventional energy, have significantly increased the pollution of the environment, and with it, contributed to global warming. The objective of this work was to carry out a theoretical approach to data analytics and business intelligence technologies applied to smart electrical-system networks with renewable energies. For this paper, a bibliometric and bibliographic review about Big Data Analytics, ICT tools of industry 4.0 and Business intelligence was carried out in different databases available in the public domain. The results of the analysis indicate the importance of the use of data analytics and business intelligence in the management of energy companies. The paper concludes by pointing out how business intelligence and data analytics are being applied in specific examples of energy companies and their growing importance in strategic and operational decision makinghttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=eshttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961https://orcid.org/0000-0003-1166-198

Big data analytics in smart grids: a review

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A Survey on Deep Learning Role in Distribution Automation System : A New Collaborative Learning-to-Learning (L2L) Concept

This paper focuses on a powerful and comprehensive overview of Deep Learning (DL) techniques on Distribution Automation System (DAS) applications to provide a complete viewpoint of modern power systems. DAS is a crucial approach to increasing the reliability, quality, and management of distribution networks. Due to the importance of development and sustainable security of DAS, the use of DL data-driven technology has grown significantly. DL techniques have blossomed rapidly, and have been widely applied in several fields of distribution systems. DL techniques are suitable for dynamic, decision-making, and uncertain environments such as DAS. This survey has provided a comprehensive review of the existing research into DL techniques on DAS applications, including fault detection and classification, load and energy forecasting, demand response, energy market forecasting, cyber security, network reconfiguration, and voltage control. Comparative results based on evaluation criteria are also addressed in this manuscript. According to the discussion and results of studies, the use and development of hybrid methods of DL with other methods to enhance and optimize the configuration of the techniques are highlighted. In all matters, hybrid structures accomplish better than single methods as hybrid approaches hold the benefit of several methods to construct a precise performance. Due to this, a new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS. The proposed model follows several stages that link different DL algorithms to solve modern power system problems. To show the effectiveness and merit of the L2L based on the proposed framework, it has been tested on a modified reconfigurable IEEE 32 test system. This method has been implemented on several DAS applications that the results prove the decline of mean square errors by approximately 12% compared to conventional LSTM and GRU methods in terms of prediction fields.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Data Consistency for Data-Driven Smart Energy Assessment

In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received. Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects

The Norges Bank’s key rate projections and the news element of monetary policy: a wavelet based jump detection approach

This paper investigates the information content of the Norges Bank’s key rate projections. Wavelet spectrum estimates provide the basis for estimating jump probabilities of short- and long-term interest rates on monetary policy announcement days before and after the introduction of key rate projections. The behavior of short-term interest rates reveals that key rate projections have only little effects on market’s forecasting ability of current target rate changes. In contrast, longer-term interest rates indicate that the announcement of key rate projections has significantly reduced market participants’ revisions of the expected future policy path. Therefore, the announcement of key rate projections further improves central bank communication.Central bank communication, interest rate projections, wavelets, jump probabilities