Special Issue "Big Data Applications for Intelligent Energy Management in Buildings"

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

Deadline for manuscript submissions: 31 October 2021.

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 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 papers will be 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 2000 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 (1 paper)

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Research

Article
AI and Data Democratisation for Intelligent Energy Management
Energies 2021, 14(14), 4341; https://doi.org/10.3390/en14144341 - 19 Jul 2021
Viewed by 222
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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