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Monitoring Application for Smart Buildings: Challenge and Research Trends

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

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 2027
Submit your paper and select the Journal "Energies" and the Special Issue "Monitoring Application for Smart Buildings: Challenge and Research Trends" via: https://susy.mdpi.com/user/manuscripts/upload?journal=energies. Please contact the guest editor or the journal editor ([email protected]) for any queries.

Special Issue Editor


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Guest Editor
Institute of Informatics and Applications, Department of Electrical Engineering, Electronics and Automatics, Campus Montilivi, Politécnic Building IV, 17003 Girona, Spain
Interests: data-driven methods for energy monitoring and optimisation; application to smart grids, smart buildings and comunities

Special Issue Information

Dear Colleagues,

I would like to draw your attention to this Special Issue on new trends of energy monitoring and management in buildings to evaluate the role of buildings in the energy transition. This Special Issue calls for papers on the following topics:

  • Methods to automate data exploitation and decision making in the context of smart buildings with a high degree of automation, and the deployment of sensors in technical equipment for more energy-efficient management.
  • Sensing and computing strategies to characterize the user behavior (occupancy and use of spaces, usage and typology of activities, etc.) and the impact of such activities and behavior in the building energy account.
  • Optimal management and scheduling of energy assets, including self-generation, storage for efficient operation, and grid interaction. Buildings as flexibility providers.
  • Energy communities and neighborhoods: energy interaction and sharing. Integrated energy management in communities and local energy trade.

The focus of this Special Issue is on strategies to accelerate the role of buildings in the energy transition. With this aim, papers examining the integration of legacy systems (BMS/BEMS, SCADAs) with new sensing (IoT, WSN, crowdsensing) and computing (cloud/edge/fog) strategies are welcomed and appreciated. Greater interest is also paid to scalable solutions rather than individual experiments in this Special Issue.

Prof. Dr. Joaquim Melendez
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

  • Smart buildings 
  • Energy monitoring 
  • User behaviour 
  • Optimal energy management 
  • Grid interaction and flexibility

Published Papers (1 paper)

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Research

15 pages, 3596 KiB  
Article
Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis
by Mario Flor, Sergio Herraiz and Ivan Contreras
Energies 2021, 14(20), 6565; https://doi.org/10.3390/en14206565 - 12 Oct 2021
Cited by 3 | Viewed by 1598
Abstract
This study presents a novel approach for discovering actionable knowledge and exploring data-based models from data recorded by household smart meters. The proposed framework is supported by a machine learning architecture based on the application of data mining methods and spatial analysis to [...] Read more.
This study presents a novel approach for discovering actionable knowledge and exploring data-based models from data recorded by household smart meters. The proposed framework is supported by a machine learning architecture based on the application of data mining methods and spatial analysis to extract temporal and spatial restricted clusters of characteristic monthly electricity load profiles. In addition, it uses these clusters to perform short-term load forecasting (1 week) using recurrent neural networks. The approach analyses a database with measurements of 1000 smart meters gathered during 4 years in Guayaquil, Ecuador. Results of the proposed methodology led us to obtain a precise and efficient stratification of typical consumption patterns and to extract neighbour information to improve the performance of residential energy consumption forecasting. Full article
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