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Energies
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Advanced Modeling and Optimization Technologies for Building Energy Efficiency
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Advanced Modeling and Optimization Technologies for Building Energy Efficiency

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

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 4164

Special Issue Editors

Prof. Dr. Kangji Li

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: control-oriented modeling and optimization of building environment systems; data-driven modeling and control of energy systems; model reduction theory and its applications to distributed parameter systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xu Chen

E-Mail Website
Guest Editor
School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: optimization technologies for energy systems; computational intelligence; machine learning

Special Issue Information

Dear Colleagues,

With the increase in distributed building photovoltaic, intelligent power consumption and power storage devices (such as charging piles), buildings are not only users of electric energy but also play a role in electric energy production, peak shaving, and storage. Determining how to reasonably dispatch and absorb the energy supply and demand between buildings and reduce power abandonment has posed new challenges to research on building energy systems. For example, two-way interaction between buildings and power grid requires information about instantaneous building energy consumption with high accuracy; to ensure the balance of energy supply and demand within the grid, it is necessary to establish multiscale building community energy demand models. Such problems are supported by a profound information science and engineering background and have important significance in practical applications.

This Special Issue aims to collect scientific contributions presenting results dealing with advanced modeling and optimization technologies for building energy systems, including but not limited to energy management, demand-side modeling, design and optimization of new energy systems within building energy efficiency, or related areas.

Dr. Kangji Li
Dr. Xu Chen
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

  • building energy systems
  • demand side models
  • electrical energy systems
  • electrical load prediction
  • electrical load dispatch
  • system modeling
  • global optimization
  • transfer learning
  • swarm intelligence

Published Papers (4 papers)

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Research

25 pages, 2866 KiB  
Article
Simulation of a Building with Hourly and Daily Varying Ventilation Flow: An Application of the Simulink S-Function
by Piotr Michalak
Energies 2023, 16(24), 7958; https://doi.org/10.3390/en16247958 - 7 Dec 2023
Viewed by 703
Abstract
This paper presents an application of the Simulink stvmgain S-function for the thermal modelling of a building zone based on the resistance–capacitance scheme of EN ISO 13790. That model in the form of the state-space matrix with time-varying elements was used in simulations [...] Read more.
This paper presents an application of the Simulink stvmgain S-function for the thermal modelling of a building zone based on the resistance–capacitance scheme of EN ISO 13790. That model in the form of the state-space matrix with time-varying elements was used in simulations of a building with hourly and, suggested in that standard, daily averaged ventilation airflow in five European cities. The following two ventilation schedules were used: occupancy-based; and wind-dependent. Comparative simulations were conducted in EnergyPlus. In general, the results obtained for the annual heating and cooling demand were better for hourly than daily averaged ventilation with an error below 10%. However, in several cases of cooling, the error was above 30%. When considering hourly indoor air temperatures, the proposed method provided very good results with MAE of up to 0.52 °C and 0.46 °C, RMSE < 0.69 °C and 0.62 °C, and CV(RMSE) < 3.09% and 2.75% for the daily averaged and hourly ventilation flow, respectively. For wind-driven ventilation, the temperatures were as follows: MAE < 0.49 °C and 0.48 °C; RMSE < 0.69 °C and 0.68 °C; and CV(RMSE) < 3.01% and 2.97%. Full article
(This article belongs to the Special Issue Advanced Modeling and Optimization Technologies for Building Energy Efficiency)
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18 pages, 2467 KiB  
Article
DelayNet: Enhancing Temporal Feature Extraction for Electronic Consumption Forecasting with Delayed Dilated Convolution
by Le Hoang Anh, Gwang-Hyun Yu, Dang Thanh Vu, Hyoung-Gook Kim and Jin-Young Kim
Energies 2023, 16(22), 7662; https://doi.org/10.3390/en16227662 - 20 Nov 2023
Cited by 1 | Viewed by 632
Abstract
In the face of increasing irregular temperature patterns and climate shifts, the need for accurate power consumption prediction is becoming increasingly important to ensure a steady supply of electricity. Existing deep learning models have sought to improve prediction accuracy but commonly require greater [...] Read more.
In the face of increasing irregular temperature patterns and climate shifts, the need for accurate power consumption prediction is becoming increasingly important to ensure a steady supply of electricity. Existing deep learning models have sought to improve prediction accuracy but commonly require greater computational demands. In this research, on the other hand, we introduce DelayNet, a lightweight deep learning model that maintains model efficiency while accommodating extended time sequences. Our DelayNet is designed based on the observation that electronic series data exhibit recurring irregular patterns over time. Furthermore, we present two substantial datasets of electricity consumption records from South Korean buildings spanning nearly two years. Empirical findings demonstrate the model’s performance, achieving 21.23%, 43.60%, 17.05% and 21.71% improvement compared to recurrent neural networks, gated-recurrent units, temporal convolutional neural networks and ARIMA models, as well as greatly reducing model complexity and computational requirements. These findings indicate the potential for micro-level power consumption planning, as lightweight models can be implemented on edge devices. Full article
(This article belongs to the Special Issue Advanced Modeling and Optimization Technologies for Building Energy Efficiency)
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20 pages, 7032 KiB  
Article
Study of Internal Flow Heat Transfer Characteristics of Ejection-Permeable FADS
by Kai Yang, Tianhao Shi, Tingzhen Ming, Yongjia Wu, Yanhua Chen, Zhongyi Yu and Mohammad Hossein Ahmadi
Energies 2023, 16(11), 4377; https://doi.org/10.3390/en16114377 - 28 May 2023
Viewed by 1015
Abstract
A fabric air dispersion system (FADS) is a type of flexible air supply system that integrates air transmission and distribution. This innovative system has the potential to address common issues such as uneven air supply and surface condensation, which are often associated with [...] Read more.
A fabric air dispersion system (FADS) is a type of flexible air supply system that integrates air transmission and distribution. This innovative system has the potential to address common issues such as uneven air supply and surface condensation, which are often associated with traditional ventilation systems. Existing numerical simulation studies on fiber ducts have encountered problems with mesh generation and simulation accuracy. This work develops a simulation method based on the equivalent discounting method to overcome these challenges. The proposed method is utilized to investigate the flow and heat transfer characteristics inside fiber ducts while also examining the effects of various shapes and opening configurations. The findings indicate that the temperature rise inside the duct is positively correlated with flow rate, with higher temperatures resulting from faster flow speeds. The temperature rise of FADS with four rows of openings increased by 0.4 k compared to other opening methods. Additionally, the study shows that increasing the number of rows of openings in the fiber duct leads to a faster decay of flow velocity and a higher temperature rise. At the same time, increasing the number of openings in the duct slightly reduces flow velocity while slightly increasing the temperature rise. The presence of more fiber duct elbows leads to greater local resistance, which accelerates the decay of the flow velocity and increases the temperature rise. Compared to the “1”-shaped FADS, the temperature rises of the “L”-shaped and “U”-shaped systems have increased by 0.9 k and 2.9 k, respectively. Full article
(This article belongs to the Special Issue Advanced Modeling and Optimization Technologies for Building Energy Efficiency)
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23 pages, 1483 KiB  
Article
Reinforcement-Learning-Based Multi-Objective Differential Evolution Algorithm for Large-Scale Combined Heat and Power Economic Emission Dispatch
by Xu Chen, Shuai Fang and Kangji Li
Energies 2023, 16(9), 3753; https://doi.org/10.3390/en16093753 - 27 Apr 2023
Cited by 9 | Viewed by 1168
Abstract
As social and environmental issues become increasingly serious, both fuel costs and environmental impacts should be considered in the cogeneration process. In recent years, combined heat and power economic emission dispatch (CHPEED) has become a crucial optimization problem in power system management. In [...] Read more.
As social and environmental issues become increasingly serious, both fuel costs and environmental impacts should be considered in the cogeneration process. In recent years, combined heat and power economic emission dispatch (CHPEED) has become a crucial optimization problem in power system management. In this paper, a novel reinforcement-learning-based multi-objective differential evolution (RLMODE) algorithm is suggested to deal with the CHPEED problem considering large-scale systems. In RLMODE, a Q-learning-based technique is adopted to automatically adjust the control parameters of the multi-objective algorithm. Specifically, the Pareto domination relationship between the offspring solution and the parent solution is used to determine the action reward, and the most-suitable algorithm parameter values for the environment model are adjusted through the Q-learning process. The proposed RLMODE was applied to solve four CHPEED problems: 5, 7, 100, and 140 generating units. The simulation results showed that, compared with four well-established multi-objective algorithms, the RLMODE algorithm achieved the smallest cost and smallest emission values for all four CHPEED problems. In addition, the RLMODE algorithm acquired better Pareto-optimal frontiers in terms of convergence and diversity. The superiority of RLMODE was particularly significant for two large-scale CHPEED problems. Full article
(This article belongs to the Special Issue Advanced Modeling and Optimization Technologies for Building Energy Efficiency)
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