Special Issue "Big Data, Blockchain and IoT in Energy Management for Sustainable Development"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Adela Bara
E-Mail Website
Guest Editor
University of Economic Studies, Department of Economic Informatics and Cybernetics, Bucharest, Romania
Interests: databases (relational, object-oriented, spatial, NoSQL); data warehousing; big data; data mining; artificial neural networks; machine learning; IoT; business intelligence; informatics solutions for energy systems (data integration, analytics, web-services, cloud-computing development)
Dr. Simona-Vasilica Oprea
E-Mail Website
Guest Editor
University of Economic Studies, Department of Economic Informatics and Cybernetics, Bucharest, Romania
Interests: Informatics solution for energy systems, Power system engineering, IoT, Big Data, Databases, Blockchain, Programming, Information Systems, Data Science and Data Analytics

Special Issue Information

Dear Colleagues,

The advancement of ICT technologies has led to new opportunities for retailers, consumers, prosumers, and owners of distributed energy sources, including storage facilities and grid operators.

The future electricity grids consist in heterogeneous systems with an increasing number of small-scale generators and smart appliances, providing a large amount of data. Hence, the energy sector necessitates big data solutions and architectures for performant energy system management. These solutions are particularly important in the current context of European and national regulations regarding large scale implementation of smart metering systems, reduction of carbon footprint, improvement of energy efficiency, increasing the involvement of the consumers, and new potential of the local electricity trading.

Blockchain technology and the Internet of Things (IoT) enhance trading the surplus of the electricity generated by communities or microgrids. A smart adaptive big data framework for demand side management fostering market strategies, settlement, and grid-efficient operation essentially necessitates big data solutions to extract, process, and analyze a large volume of data generated by consumers/prosumers from various sources: smart appliances (IoT and sensors), small-scale generation, such as photovoltaic panels and micro-wind turbines, integrated with storage devices and electric vehicles.

Consumers’ behavior is changing to a more active role empowered by recent advancements in ICT technologies. Thus, the operation of the smart appliances can be monitored, optimized, and controlled with smart home applications, sensors, and IoT architectures.

Prof. Adela Bara
Dr. Simona-Vasilica Oprea
Guest Editors

Manuscript Submission Information

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Keywords

  • big data
  • blockchain
  • IoT
  • energy management
  • sustainable development

Published Papers (3 papers)

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Research

Article
Sustainable Development with Schumpeter Extended Endogenous Type of Innovation and Statistics in European Countries
Sustainability 2021, 13(7), 3848; https://doi.org/10.3390/su13073848 - 31 Mar 2021
Viewed by 475
Abstract
In the economic growth models, technological progress is either exogenous or endogenous. The endogenized theory is based on analytical modeling of the economic process in order to include the event of innovating. Theory around the subject innovation and economic growth also includes several [...] Read more.
In the economic growth models, technological progress is either exogenous or endogenous. The endogenized theory is based on analytical modeling of the economic process in order to include the event of innovating. Theory around the subject innovation and economic growth also includes several independent parameters that have a strong impact over innovation. However, few of them established creativity as an independent parameter of innovation. The present paper aims to extend the endogenized theory in order to include creativity as an independent parameter of innovation, based on the evidence of a panel data of 28 countries, through 8 years. A theoretical model, a multiple linear regression, an ANOVA analysis and correlational matrixes were used in order to fulfill our purpose. Results show that innovation is determined by the level of knowledge twice as much as the level of creativity. A conceptual framework for an extension of endogenous growth models, in order to include creativity, is presented in the paper. The model can enhance economic growth by fostering creativity or knowledge and thus, the size of innovation, which is the main driver for economic growth in the model presented. Full article
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Article
Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities
Sustainability 2021, 13(4), 1736; https://doi.org/10.3390/su13041736 - 05 Feb 2021
Viewed by 707
Abstract
Demand response (DR) programs were usually designed to provide load peak reduction and flatten the load curve, but in the context of rapid adoption of emerging technologies, such as smart metering and sensors, load flexibility will address current trends and challenges (such as [...] Read more.
Demand response (DR) programs were usually designed to provide load peak reduction and flatten the load curve, but in the context of rapid adoption of emerging technologies, such as smart metering and sensors, load flexibility will address current trends and challenges (such as grid modernization, demand, and renewables growth) encountered by the evolving power systems. The uncertainty of the renewable energy sources (RES) and electric vehicle (EV) fleet operation has increased the importance of load flexibility that can be managed to provide more support for the stable operation of power systems, including balancing. In this paper, we propose a data model to handle load flexibility and take advantage of its benefits. We also develop a methodology to collect and organize data, combining the consumption profile with several auxiliary datasets such as climate characteristics of the location, independent system operator (ISO) to which the consumer is affiliated, geographical coordinates, assessed flexibility coefficients, tariff rates, weather forecast for day-ahead flexibility forecast, DR-enabling technology costs, and DR programs. These multiple features are stored into a flexibility relational database and NoSQL database for large consumption data collections. Then, we propose a data processing flow to obtain valuable insights from numerous .csv files and an algorithm to assess the load flexibility using large residential and commercial profile datasets from the USA, estimating plausible values of the flexibility provided by two categories of consumers. Full article
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Article
A Business Intelligence & Analytics Framework for Clean and Affordable Energy Data Analysis
Sustainability 2021, 13(2), 638; https://doi.org/10.3390/su13020638 - 11 Jan 2021
Cited by 2 | Viewed by 1044
Abstract
Energy is the sector most strongly connected with climate change moderation, and this correlation and interdependency is largely investigated, in particular as regards renewable energy and sustainability issues. The United Nations, European Union, and all countries around the world declare their support for [...] Read more.
Energy is the sector most strongly connected with climate change moderation, and this correlation and interdependency is largely investigated, in particular as regards renewable energy and sustainability issues. The United Nations, European Union, and all countries around the world declare their support for sustainable development, materialized in agreements, strategies, and action plans. This diversity, combined with significant interdependencies between indicators, brings up challenges for data analysis, which we have tackled in order to decide on relevant indicators. We have built a research framework based on Business Intelligence & Analytics for monitoring the SDG7 indicators that aim at “Ensuring access to affordable, reliable, sustainable, and modern energy for all”, in relation with SDG13 indicators targeting the sustainable aspect of energy. In developing the Business Intelligence & Analytics framework, we have considered Design Science Research in information systems guidelines. We have designed a process for carrying out Design Science Research by describing the demarche to develop information artifacts, which are the essence of a Business Intelligence & Analytics system. The information artifacts, such as data source, preprocessed data, initial and final data model, as well as data visualizations, are designed and implemented in order to support clean and affordable energy data analysis. The proposed research model, applied for Romania in this paper, serves as a point of departure for investigating data in a more integrated way, and can be easily applied to another country case study. Full article
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