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Advances in Smart Buildings, Energy Storage, and Sustainable Energy Engineering

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4692

Special Issue Editors


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Guest Editor
ABB Ltd, US Research Center, Raleigh, NC 27606, USA
Interests: smart building; smart grid; home energy management; battery; EV; net-zero energy building

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Guest Editor
Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA
Interests: alternative and renewable energy technologies; electric machines and power electronic drives; electromagnetic devices; electric power systems; energy storage; smart grids and buildings

Special Issue Information

Dear Colleagues,

Building will play a key role in the effort to achieve a net-zero-energy future. Buildings serve as hubs of energy in the form of both electricity and heat. A smart modern building can be a generator as well as a consumer of electricity. Buildings can reserve and supply energy to the grid using electrical energy storage (e.g., batteries and EVs) as well as thermal energy storage (e.g., HVAC and water heaters).

The Special Issue aims to provide an advanced forum for research on smart buildings, energy storage, and renewable energy at both residential and power-system levels. This will add to the value that Sustainability already has as an international, cross-disciplinary, scholarly, peer-reviewed and open-access journal concerned with the environmental, cultural, economic, and social sustainability of human beings.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Building energy modelling;
  • Home energy management;
  • Smart building;
  • Net-zero-energy building;
  • Vehicle-to-grid (V2G) and vehicle-to-home (V2H).

We look forward to receiving your contributions.

Dr. Huangjie Gong
Prof. Dr. Dan M. Ionel
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

  • building energy modelling
  • home energy management
  • smart building
  • net zero energy building
  • vehicle-to-grid (V2G), vehicle-to-home (V2H)

Published Papers (4 papers)

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Research

18 pages, 12026 KiB  
Article
Enhancing Renewable Energy Use in Residential Communities: Analyzing Storage, Trading, and Combinations
by Akhtar Hussain and Hak-Man Kim
Sustainability 2024, 16(2), 891; https://doi.org/10.3390/su16020891 - 20 Jan 2024
Cited by 1 | Viewed by 819
Abstract
Renewable energy resources, especially rooftop solar PV, have gained momentum during the past few years. However, the local consumption of PV power is limited due to the negative correlation between peak PV power and residential loads. Therefore, this study analyzes various cases to [...] Read more.
Renewable energy resources, especially rooftop solar PV, have gained momentum during the past few years. However, the local consumption of PV power is limited due to the negative correlation between peak PV power and residential loads. Therefore, this study analyzes various cases to maximize the consumption of renewables in communities encompassing dwellings both with and without PV installations. The three cases considered in this study are local energy storage, community energy storage, and internal trading. A total of six cases are analyzed by evaluating these cases individually and in combinations. To achieve this, first, a generalized optimization model with specific constraints for each case is developed. Subsequently, different indices are devised to quantitatively measure trading with the grid and the consumption of renewables under varying cases. The performance of these different cases is analyzed for a community comprising five dwellings over a summer week. Furthermore, the performance of each case is evaluated for various seasons throughout the year. Additionally, a sensitivity analysis of different storage capacities (both local and community) is conducted. Simulation results indicate that community storage results in the highest renewable consumption if only one case is considered. However, the overall combination of internal trading and community storage results in the highest cost reduction, lowest dependence on the grid, and the highest consumption of renewables. Finally, a techno-economic analysis is performed on four widely used battery technologies, taking into account diverse cost and technical considerations. Full article
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14 pages, 2674 KiB  
Article
A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images
by Petros Papageorgiou, Dimitra Mylona, Konstantinos Stergiou and Aggelos S. Bouhouras
Sustainability 2023, 15(17), 12957; https://doi.org/10.3390/su151712957 - 28 Aug 2023
Cited by 2 | Viewed by 1188
Abstract
Non-intrusive load monitoring (NILM) has been on the rise for more than three decades. Its main objective is non-intrusive load disaggregation into individual operating appliances. Recent studies have shown that a higher sampling rate in the aggregated measurements allows better performance regarding load [...] Read more.
Non-intrusive load monitoring (NILM) has been on the rise for more than three decades. Its main objective is non-intrusive load disaggregation into individual operating appliances. Recent studies have shown that a higher sampling rate in the aggregated measurements allows better performance regarding load disaggregation. In addition, recent developments in deep learning and, in particular, convolutional neural networks (CNNs) have facilitated load disaggregation using CNN models. Several methods have been described in the literature that combine both a higher sampling rate and a CNN-based NILM framework. However, these methods use only a small number of cycles of the aggregated signal, which complicates the practical application of real-time NILM. In this work, a high sampling rate time-driven CNN-based NILM framework is also proposed. However, a novel current harmonic distortion image extracted from 60 cycles of the aggregated signal is proposed, resulting in 1 s appliance classification with low computational complexity. Appliance classification performance is evaluated using the PLAID3 dataset for both single and combined appliance operation. In addition, a comparison is made with a method from the literature. The results highlight the robustness of the novel feature and confirm the real-time applicability of the proposed NILM framework. Full article
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18 pages, 1159 KiB  
Article
Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-Series Model with Knowledge Injection
by Tan Ngoc Dinh, Gokul Sidarth Thirunavukkarasu, Mehdi Seyedmahmoudian, Saad Mekhilef and Alex Stojcevski
Sustainability 2023, 15(17), 12951; https://doi.org/10.3390/su151712951 - 28 Aug 2023
Cited by 1 | Viewed by 866
Abstract
The COVID-19 pandemic and the subsequent implementation of lockdown measures have significantly impacted global electricity consumption, necessitating accurate energy consumption forecasts for optimal energy generation and distribution during a pandemic. In this paper, we propose a new forecasting model called the multivariate multilayered [...] Read more.
The COVID-19 pandemic and the subsequent implementation of lockdown measures have significantly impacted global electricity consumption, necessitating accurate energy consumption forecasts for optimal energy generation and distribution during a pandemic. In this paper, we propose a new forecasting model called the multivariate multilayered long short-term memory (LSTM) with COVID-19 case injection (mvMLSTMCI) for improved energy forecast during the next occurrence of a similar pandemic. We utilized data from commercial buildings in Melbourne, Australia, during the COVID-19 pandemic to predict energy consumption and evaluate the model’s performance against commonly used methods such as LSTM, bidirectional LSTM, linear regression, support vector machine, and multilayered LSTM (M-LSTM). The proposed forecasting model was analyzed using the following metrics: mean percent absolute error (MPAE), normalized root mean square error (NRMSE), and R2 score values. The model mvMLSTMCI demonstrated superior performance, achieving the lowest mean percentage absolute error values of 0.061, 0.093, and 0.158 for DatasetS1, DatasetS2, and DatasetS3, respectively. Our results highlight the improved precision and accuracy of the model, providing valuable information for energy management and decision making during the challenges posed by the occurrence of a pandemic like COVID-19 in the future. Full article
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20 pages, 8203 KiB  
Article
Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control
by Evan S. Jones, Rosemary E. Alden, Huangjie Gong and Dan M. Ionel
Sustainability 2023, 15(12), 9433; https://doi.org/10.3390/su15129433 - 12 Jun 2023
Cited by 1 | Viewed by 1207
Abstract
Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios [...] Read more.
Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%. Full article
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