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Energies
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Smart Forecasting of Building and District Energy Management
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Smart Forecasting of Building and District Energy Management

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 October 2020) | Viewed by 15744

Special Issue Editors

Prof. Dr. Yacine Rezgui

E-Mail Website
Guest Editor
BRE Institute of Sustainable Engineering, Cardiff University, Cardiff CF10 3AT, UK
Interests: architectural and civil engineering; computing; artificial intelligence; building energy; energy and environment; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Sylvain Kubicki

E-Mail Website
Guest Editor
Luxembourg Institute of Science and Technology, Avenue des Hauts-Fourneaux L-4362 Esch-sur-Alzette, Luxembourg
Interests: architecture; engineering; construction; building information modeling; BIM for achieving energy efficiency; smart buildings and urban development

Special Issue Information

Dear Colleagues,

Forecasting models are widely used in different domains, including in the context of building and district energy management. Moreover, forecasting plays an essential role in the control of power plants and electric power exchange in interconnected systems. It also supports energy planners in understanding the influence of some variables on energy consumption, and thus inform decision making. On a temporal scale, forecasting can be short-term, for instance, for balancing electricity supply, and long-term, including, for capacity expansion, capital investment return studies, and revenue analysis. Over the years, many different forecasting models have been applied for electricity and power predictions, such as multivariate and multiple regression, SVM, time series, and the autoregressive moving average. Equally, artificial neural networks (ANN) have become widely used for prediction scenarios. ANN has been used for various tasks, such as (a) short-term load forecasting in microgrids; (b) optimization scenarios at building level; and (c) long term horizon scenarios to determine the annual electricity consumption of a region, district, or building. Conversely, recent advances in information and communication technologies in areas such the Internet of things, semantics (including building information modelling), and artificial intelligence have paved the way to new promising methods, techniques, and tools. The adoption and impact of these technologies can only be achieved if adapted education and training strategies are implemented. 

This Special Issue aims to publish high-quality research articles on the latest developments in the smart forecasting of building and district energy management spanning the whole lifecycle—from design to operation and reuse/recycle, focusing on technology, policies, training and education, and practices. Articles addressing the interrelationships between traditionally disparate domains are particularly welcome. The topics include, but are not limited to, the following:

  • Building simulation and optimization
  • Distributed energy resources and storage
  • Smart buildings, neighborhoods, and districts
  • Computational intelligence and data analytics
  • Policies and regulations
  • Energy and water nexus
  • Heating, cooling, and thermal comfort
  • Climate change adaptation
  • Behavioral aspects
  • Building stock modelling and refurbishment
  • Education and training in building energy
  • Building information modelling for energy efficiency
  • Training and education for energy efficiency

Prof. Dr. Yacine Rezgui
Dr. Sylvain Kubicki
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. 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

  • energy consumption and load prediction
  • forecasting models
  • machine learning
  • Internet of things
  • semantics
  • BIM
  • training.

Published Papers (5 papers)

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Research

29 pages, 6890 KiB  
Article
Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach
by Junhwa Hwang, Dongjun Suh and Marc-Oliver Otto
Energies 2020, 13(22), 5885; https://doi.org/10.3390/en13225885 - 11 Nov 2020
Cited by 17 | Viewed by 3092
Abstract
Article [...] Full article
(This article belongs to the Special Issue Smart Forecasting of Building and District Energy Management)
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16 pages, 7359 KiB  
Article
A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques
by Jamer Jiménez Mares, Loraine Navarro, Christian G. Quintero M. and Mauricio Pardo
Energies 2020, 13(16), 4040; https://doi.org/10.3390/en13164040 - 05 Aug 2020
Cited by 6 | Viewed by 1898
Abstract
The electrical sector needs to study how energy demand changes to plan the maintenance and purchase of energy assets properly. Prediction studies for energy demand require a high level of reliability since a deviation in the forecasting demand could affect operation costs. This [...] Read more.
The electrical sector needs to study how energy demand changes to plan the maintenance and purchase of energy assets properly. Prediction studies for energy demand require a high level of reliability since a deviation in the forecasting demand could affect operation costs. This paper proposed a short-term forecasting energy demand methodology based on hierarchical clustering using Dynamic Time Warp as a similarity measure integrated with Artificial Neural Networks. Clustering was used to build the typical curve for each type of day, while Artificial Neural Networks handled the weather sensibility to correct a preliminary forecasting curve obtained in the clustering stage. A statistical analysis was carried out to identify those significant factors in the prediction model of energy demand. The performance of this proposed model was measured through the Mean Absolute Percentage Error (MAPE). The experimental results show that the three-stage methodology was able to improve the MAPE, reaching values as good as 2%. Full article
(This article belongs to the Special Issue Smart Forecasting of Building and District Energy Management)
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22 pages, 4910 KiB  
Article
Developing Smart Energy Communities around Fishery Ports: Toward Zero-Carbon Fishery Ports
by Ateyah Alzahrani, Ioan Petri, Yacine Rezgui and Ali Ghoroghi
Energies 2020, 13(11), 2779; https://doi.org/10.3390/en13112779 - 01 Jun 2020
Cited by 17 | Viewed by 3458
Abstract
Air quality and energy consumption are among the top ten environmental priorities in seaports as stated by the European Sea Ports Organization. Globally, it is estimated that 15% of energy consumption can be attributed to refrigeration and air conditioning systems in fishing activities. [...] Read more.
Air quality and energy consumption are among the top ten environmental priorities in seaports as stated by the European Sea Ports Organization. Globally, it is estimated that 15% of energy consumption can be attributed to refrigeration and air conditioning systems in fishing activities. There is a real need to understand energy usage in fishery ports to help identify areas of improvements, with a view to optimize energy usage and minimize carbon emissions. In this study, we elaborate on ways in which a simulation capability can be developed at the community level with a fishery port, using a real-world case study seaport in Milford Heaven (Wales, UK). This simulation-based strategy is used to investigate the potential of renewable energy, including local solar farms, to meet the local power demand. This has informed the development of a simulation-based optimization strategy meant to explore how smart energy communities can be formed at the port level by integrating the smart grid with the local community energy storage. The main contribution of the paper involves a co-simulation environment that leverages calibrated energy simulation models to deliver an optimization capability that (a) manages electrical storage within a district an environment, and (b) promotes the formation of energy communities in a fishery port ecosystem. This is paving the way to policy implications, not only in terms of carbon and energy reduction, but also in the formation and sustained management of energy communities. Full article
(This article belongs to the Special Issue Smart Forecasting of Building and District Energy Management)
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24 pages, 4774 KiB  
Article
Promoting Energy Efficiency in the Built Environment through Adapted BIM Training and Education
by Ali Alhamami, Ioan Petri, Yacine Rezgui and Sylvain Kubicki
Energies 2020, 13(9), 2308; https://doi.org/10.3390/en13092308 - 06 May 2020
Cited by 24 | Viewed by 3995
Abstract
The development of new climate change policies has increased the motivation to reduce energy use in buildings, as reflected by a stringent regulatory landscape. The construction industry is expected to adopt new methods and strategies to address such requirements, focusing primarily on reducing [...] Read more.
The development of new climate change policies has increased the motivation to reduce energy use in buildings, as reflected by a stringent regulatory landscape. The construction industry is expected to adopt new methods and strategies to address such requirements, focusing primarily on reducing energy demand, improving process efficiency and reducing carbon emissions. However, the realisation of these emerging requirements has been constrained by the highly fragmented nature of the industry, which is often portrayed as involving a culture of adversarial relationships and risk avoidance, which is exacerbated by a linear workflow. Recurring problems include low process efficiency, delays and construction waste. Building information modelling (BIM) provides a unique opportunity to enhance building energy efficiency (EE) and to open new pathways towards a more digitalised industry and society. BIM has the potential to reduce (a) waste and carbon emissions, (b) the endemic performance gap, (c) in-use energy and (d) the total lifecycle impact. BIM also targets to improve the whole supply chain related to the design, construction as well as the management and use of the facility. However, the construction workforce is required to upgrade their skills and competencies to satisfy new requirements for delivering BIM for EE. Currently, there is a real gap between the industry expectations for employees and current training and educational programmes. There is also a set of new requirements and expectations that the construction industry needs to identify and address in order to deliver more informed BIM for EE practices. This paper provides an in-depth analysis and gap identification pertaining to the skills and competencies involved in BIM training for EE. Consultations and interviews have been used as a method to collect requirements, and a portfolio of use cases have been created and analysed to better understand existing BIM practices and to determine current limitations and gaps in BIM training. The results show that BIM can contribute to the digitalisation of the construction industry in Europe with adapted BIM training and educational programmes to deliver more informed and adapted energy strategies. Full article
(This article belongs to the Special Issue Smart Forecasting of Building and District Energy Management)
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20 pages, 5583 KiB  
Article
Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection
by Jihoon Jang, Joosang Lee, Eunjo Son, Kyungyong Park, Gahee Kim, Jee Hang Lee and Seung-Bok Leigh
Energies 2019, 12(21), 4187; https://doi.org/10.3390/en12214187 - 02 Nov 2019
Cited by 16 | Viewed by 2809
Abstract
Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total [...] Read more.
Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved. Full article
(This article belongs to the Special Issue Smart Forecasting of Building and District Energy Management)
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