Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

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

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 15963

Share This Special Issue

Special Issue Editors

Prof. Dr. Adela Bara

E-Mail Website
Guest Editor

Department of Economic Informatics and Cybernetics, University of Economic Studies, 010374 Bucharest, Romania
Interests: database systems; big data; machine learning; decision support systems; energy management systems; IoT; digital twins
Special Issues, Collections and Topics in MDPI journals

Dr. Simona-Vasilica Oprea

E-Mail Website
Guest Editor

Department of Economic Informatics and Cybernetics, University of Economic Studies, 010374 Bucharest, Romania
Interests: IoT; database systems; big data; deep learning; natural language processing; energy management systems; digital twins
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

big data
blockchain
IoT
energy management
sustainable development

Published Papers (4 papers)

Download All Papers

Research

20 pages, 2460 KiB

Open AccessArticle

Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption

by Simona-Vasilica Oprea, Adela Bâra, Florina Camelia Puican and Ioan Cosmin Radu

Sustainability 2021, 13(19), 10963; https://0-doi-org.brum.beds.ac.uk/10.3390/su131910963 - 02 Oct 2021

Cited by 33 | Viewed by 4783

Abstract

When analyzing smart metering data, both reading errors and frauds can be identified. The purpose of this analysis is to alert the utility companies to suspicious consumption behavior that could be further investigated with on-site inspections or other methods. The use of Machine Learning (ML) algorithms to analyze consumption readings can lead to the identification of malfunctions, cyberattacks interrupting measurements, or physical tampering with smart meters. Fraud detection is one of the classical anomaly detection examples, as it is not easy to label consumption or transactional data. Furthermore, frauds differ in nature, and learning is not always possible. In this paper, we analyze large datasets of readings provided by smart meters installed in a trial study in Ireland by applying a hybrid approach. More precisely, we propose an unsupervised ML technique to detect anomalous values in the time series, establish a threshold for the percentage of anomalous readings from the total readings, and then label that time series as suspicious or not. Initially, we propose two types of algorithms for anomaly detection for unlabeled data: Spectral Residual-Convolutional Neural Network (SR-CNN) and an anomaly trained model based on martingales for determining variations in time-series data streams. Then, the Two-Class Boosted Decision Tree and Fisher Linear Discriminant analysis are applied on the previously processed dataset. By training the model, we obtain the required capabilities of detecting suspicious consumers proved by an accuracy of 90%, precision score of 0.875, and F1 score of 0.894. Full article

(This article belongs to the Special Issue Big Data, Blockchain and IoT in Energy Management for Sustainable Development)

► Show Figures

Figure 1

22 pages, 3242 KiB

Open AccessArticle

Sustainable Development with Schumpeter Extended Endogenous Type of Innovation and Statistics in European Countries

by Marian Pompiliu Cristescu and Raluca Andreea Nerișanu

Sustainability 2021, 13(7), 3848; https://0-doi-org.brum.beds.ac.uk/10.3390/su13073848 - 31 Mar 2021

Cited by 6 | Viewed by 2510

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 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

(This article belongs to the Special Issue Big Data, Blockchain and IoT in Energy Management for Sustainable Development)

► Show Figures

Figure 1

20 pages, 8892 KiB

Open AccessArticle

Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities

by Simona-Vasilica Oprea, Adela Bâra, Răzvan Cristian Marales and Margareta-Stela Florescu

Sustainability 2021, 13(4), 1736; https://0-doi-org.brum.beds.ac.uk/10.3390/su13041736 - 05 Feb 2021

Cited by 11 | Viewed by 2226

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 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

(This article belongs to the Special Issue Big Data, Blockchain and IoT in Energy Management for Sustainable Development)

► Show Figures

Figure 1

25 pages, 38470 KiB

Open AccessArticle

A Business Intelligence & Analytics Framework for Clean and Affordable Energy Data Analysis

by Mihaela Muntean, Doina Dănăiaţă, Luminiţa Hurbean and Cornelia Jude

Sustainability 2021, 13(2), 638; https://0-doi-org.brum.beds.ac.uk/10.3390/su13020638 - 11 Jan 2021

Cited by 20 | Viewed by 5234

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 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

(This article belongs to the Special Issue Big Data, Blockchain and IoT in Energy Management for Sustainable Development)

► Show Figures

Figure 1

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI