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Energies
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Big Data Applications for Intelligent Energy Management in Buildings
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Big Data Applications for Intelligent Energy Management in Buildings

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 13745

Special Issue Editor

Dr. Vangelis Marinakis

E-Mail Website
Guest Editor
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Interests: energy management; sustainable energy planning; smart cities; decision support systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The constantly increasing momentum of big data, and the increasing adoption of leading-edge information and communication technologies (ICTs), such as internet of things (IoT), artificial intelligence (AI), distributed ledger technology (DLT)/blockchain, constitute an unprecedented market opportunity for improving the energy efficiency along the building sector and its lifecycle, and for better managing energy consumption and generation at building level.

This special issue is devoted to the latest developments in the field of big data and aims to provide valuable insights into the most effective applications for intelligent energy management and holistic energy services in buildings.

Examples of topics appropriate to the theme of this special issue, include, but are not limited to:

  • Data-driven architectures for buildings data exchange, management and real-time processing;
  • Data analytics techniques and algorithms for smart energy-efficient buildings;
  • Digital building twins to support building related processes;
  • Innovative applications and services for: (a) energy management and energy-efficient buildings; (b) design, refurbishment and development of building infrastructure; (c) policy making and policy impact assessment; (d) enhanced reliability and reduced risks of energy efficiency investments.

This special issue is also based on the research activities of the H2020 MATRYCS project (http://matrycs.eu/) and we seek high-quality papers that capitalise and combine modern technological breakthroughs in the area of the big data driven economy, in order to support improved decision-making at different scales around buildings.

Dr. Vangelis Marinakis
Guest Editor

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. Energies 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 2600 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

  • Buildings semantic interoperability
  • Data services and semantic enrichment
  • Big data management and AI services
  • Data analytics techniques for buildings
  • Intelligent energy management
  • Energy performance of buildings
  • Policy making and policy impact assessment
  • Energy efficiency investment de-risking

Published Papers (4 papers)

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Research

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22 pages, 2154 KiB  
Article
MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain
by Marco Pau, Panagiotis Kapsalis, Zhiyu Pan, George Korbakis, Dario Pellegrino and Antonello Monti
Energies 2022, 15(7), 2568; https://doi.org/10.3390/en15072568 - 1 Apr 2022
Cited by 17 | Viewed by 2466
Abstract
The building sector is undergoing a deep transformation to contribute to meeting the climate neutrality goals set by policymakers worldwide. This process entails the transition towards smart energy-aware buildings that have lower consumptions and better efficiency performance. Digitalization is a key part of [...] Read more.
The building sector is undergoing a deep transformation to contribute to meeting the climate neutrality goals set by policymakers worldwide. This process entails the transition towards smart energy-aware buildings that have lower consumptions and better efficiency performance. Digitalization is a key part of this process. A huge amount of data is currently generated by sensors, smart meters and a multitude of other devices and data sources, and this trend is expected to exponentially increase in the near future. Exploiting these data for different use cases spanning multiple application scenarios is of utmost importance to capture their full value and build smart and innovative building services. In this context, this paper presents a high-level architecture for big data management in the building domain which aims to foster data sharing, interoperability and the seamless integration of advanced services based on data-driven techniques. This work focuses on the functional description of the architecture, underlining the requirements and specifications to be addressed as well as the design principles to be followed. Moreover, a concrete example of the instantiation of such an architecture, based on open source software technologies, is presented and discussed. Full article
(This article belongs to the Special Issue Big Data Applications for Intelligent Energy Management in Buildings)
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12 pages, 2438 KiB  
Article
Leveraging Graph Analytics for Energy Efficiency Certificates
by Panagiotis Kapsalis, Giorgos Kormpakis, Konstantinos Alexakis and Dimitrios Askounis
Energies 2022, 15(4), 1500; https://doi.org/10.3390/en15041500 - 17 Feb 2022
Cited by 10 | Viewed by 2551
Abstract
As energy efficiency is becoming a subject of utter importance in today’s societies, the European Union and a vast number of organizations have put a lot of focus on it. As a result, huge amounts of data are generated at an unprecedented rate. [...] Read more.
As energy efficiency is becoming a subject of utter importance in today’s societies, the European Union and a vast number of organizations have put a lot of focus on it. As a result, huge amounts of data are generated at an unprecedented rate. After thorough analysis and exploration, these data could provide a variety of solutions and optimizations regarding the energy efficiency subject. However, all the potential solutions that could derive from the aforementioned procedures still remain untapped due to the fact that these data are yet fragmented and highly sophisticated. In this paper, we propose an architecture for a Reasoning Engine, a mechanism that provides intelligent querying, insights and search capabilities, by leveraging technologies that will be described below. The proposed architecture has been developed in the context of the H2020 project called MATRYCS. In this paper, the reasons that resulted from the need of efficient ways of querying and analyzing the large amounts of data are firstly explained. Subsequently, several use cases, where related technologies were used to address real-world challenges, are presented. The main focus, however, is put in the detailed presentation of our Reasoning Engine’s implementation steps. Lastly, the outcome of our work is demonstrated, showcasing the derived results and the optimizations that have been implemented. Full article
(This article belongs to the Special Issue Big Data Applications for Intelligent Energy Management in Buildings)
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14 pages, 520 KiB  
Article
AI and Data Democratisation for Intelligent Energy Management
by Vangelis Marinakis, Themistoklis Koutsellis, Alexandros Nikas and Haris Doukas
Energies 2021, 14(14), 4341; https://doi.org/10.3390/en14144341 - 19 Jul 2021
Cited by 22 | Viewed by 4203
Abstract
Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data [...] Read more.
Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation framework for intelligent energy management is presented. In doing so, it highlights the need for the democratisation of data and analytics in the energy sector, toward making data available for the right people at the right time, allowing them to make the right decisions, and eventually facilitating the adoption of decentralised, decarbonised, and democratised energy business models. Full article
(This article belongs to the Special Issue Big Data Applications for Intelligent Energy Management in Buildings)
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Review

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21 pages, 358 KiB  
Review
Big Data Value Chain: Multiple Perspectives for the Built Environment
by Gema Hernández-Moral, Sofía Mulero-Palencia, Víctor Iván Serna-González, Carla Rodríguez-Alonso, Roberto Sanz-Jimeno, Vangelis Marinakis, Nikos Dimitropoulos, Zoi Mylona, Daniele Antonucci and Haris Doukas
Energies 2021, 14(15), 4624; https://doi.org/10.3390/en14154624 - 30 Jul 2021
Cited by 13 | Viewed by 3109
Abstract
Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new [...] Read more.
Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new technologies, such as artificial intelligence, Internet of things, blockchain, and the exploitation of big data towards solving real life problems, the way could be paved towards smart and energy-aware buildings. In this context, the aim of this paper is to present a critical review and an in-detail definition of the big data value chain for the built environment in Europe, covering multiple needs and perspectives: “policy”, “technology” and “business”, in order to explore the main challenges and opportunities in this area. Full article
(This article belongs to the Special Issue Big Data Applications for Intelligent Energy Management in Buildings)
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