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Data-Driven Energy-Cost Analysis of HVAC System for Buildings
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Data-Driven Energy-Cost Analysis of HVAC System for Buildings

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 September 2021) | Viewed by 14367

Special Issue Editors

Prof. Dr. Junemo Koo

E-Mail Website
Guest Editor
College of Engineering, Kyung Hee University, Seoul 130-701, Korea
Interests: building energy analysis; heat and mass transfer; exhaust gas abatement systems
Prof. Dr. Jinkyun Cho

E-Mail Website
Guest Editor
Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea
Interests: building science; HVAC; energy system

Special Issue Information

Dear Colleagues,

As the Guest Editor, I kindly invite you to submit your papers to be published in a Special Edition of Energies, "Data-driven Energy-cost Analysis of HVAC System for Buildings". This Special Issue focuses on recent research developments and applications on how to analyze building heating and cooling energy consumption, design of passive and active parts in buildings, and optimization of building operations for energy conservations based on the related design and operation data. 

Main topics include:

  • Research on the building heating and cooling load estimations and optimization;
  • Analysis of building energy consumption based on real-time operation histories from the HVAC facilities, and/or energy simulation using machine learning techniques;
  • Optimum design of building HVAC systems accounting for the initial capital and running energy costs;
  • Optimum design of building HVAC systems considering renewable energy;
  • HVAC energy costs and consumptions analysis handling of the heating and cooling loads of buildings;
  • Integrated analysis of building envelope and HVAC system designs.

Prof. Dr. Junemo Koo
Prof. Dr. Jinkyun Cho
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 consumption
  • HVAC
  • Heating and cooling loads
  • Energy conservation
  • Data-driven

Published Papers (5 papers)

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Research

28 pages, 2312 KiB  
Article
Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning
by Sarra Houidi, Dominique Fourer, François Auger, Houda Ben Attia Sethom and Laurence Miègeville
Energies 2021, 14(9), 2726; https://0-doi-org.brum.beds.ac.uk/10.3390/en14092726 - 10 May 2021
Cited by 14 | Viewed by 2540
Abstract
Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for [...] Read more.
Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for future smart grid applications. Here, we are interested in an event-based NILM pipeline, and particularly in the HEAs’ recognition step. This paper focuses on the selection of relevant and understandable features for efficiently discriminating distinct HEAs. Our contributions are manifold. First, we introduce a new publicly available annotated dataset of individual HEAs described by a large set of electrical features computed from current and voltage measurements in steady-state conditions. Second, we investigate through a comparative evaluation a large number of new methods resulting from the combination of different feature selection techniques with several classification algorithms. To this end, we also investigate an original feature selection method based on a deep neural network architecture. Then, through a machine learning framework, we study the benefits of these methods for improving Home Electrical Appliance (HEA) identification in a supervised classification scenario. Finally, we introduce new transfer learning results, which confirm the relevance and the robustness of the selected features learned from our proposed dataset when they are transferred to a larger dataset. As a result, the best investigated methods outperform the previous state-of-the-art results and reach a maximum recognition accuracy above 99% on the PLAID evaluation dataset. Full article
(This article belongs to the Special Issue Data-Driven Energy-Cost Analysis of HVAC System for Buildings)
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17 pages, 2369 KiB  
Article
A Powerful Tool for Optimal Control of Energy Systems in Sustainable Buildings: Distortion Power Bivector
by Castilla Manuel V. and Martin Francisco
Energies 2021, 14(8), 2177; https://0-doi-org.brum.beds.ac.uk/10.3390/en14082177 - 13 Apr 2021
Cited by 1 | Viewed by 1599
Abstract
In the field of building constructions, there is undeniably a growing need to optimize the energy systems which are a key target in new modern constructions and industrial buildings. In this sense, energy systems are being traced for the development of energy distribution [...] Read more.
In the field of building constructions, there is undeniably a growing need to optimize the energy systems which are a key target in new modern constructions and industrial buildings. In this sense, energy systems are being traced for the development of energy distribution networks that are increasingly smart, efficient, and sustainable. Modern generation and distribution energy systems, such as microgrids control systems, are being affected by the presence of linear and nonlinear loads, resulting a distorted voltage and current waveforms. Thus, it is stated that industrial and residential building heating and cooling loads behave essentially like sources of harmonics. This paper presents a new framework based on geometric algebra (GA) to the definition of a multivectorial distortion power concept, which is represented by a bivector that is geometrically interpreted to distinguish the rotated distortion and distortion power bivectors in these kinds of loads. Both bivectors, and their relations to the phase angles of distorted voltage are the main subject of this paper to interpret an optimal control of building energy. Numerical examples are used to illustrate of the suggested distortion power concept, as well as the information it provides for energy control in new buildings in a more sustainable way. Full article
(This article belongs to the Special Issue Data-Driven Energy-Cost Analysis of HVAC System for Buildings)
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19 pages, 7356 KiB  
Article
Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads
by Chanuk Lee, Dong Eun Jung, Donghoon Lee, Kee Han Kim and Sung Lok Do
Energies 2021, 14(3), 756; https://0-doi-org.brum.beds.ac.uk/10.3390/en14030756 - 01 Feb 2021
Cited by 7 | Viewed by 2040
Abstract
In Korea apartment buildings, most energy is consumed as heating energy. In order to reduce heating energy in apartment buildings, it is required to reduce the amount of energy used in heating systems. Energy saving in heating systems can be achieved through operation [...] Read more.
In Korea apartment buildings, most energy is consumed as heating energy. In order to reduce heating energy in apartment buildings, it is required to reduce the amount of energy used in heating systems. Energy saving in heating systems can be achieved through operation and control based on efficient operation plans. The efficient operation plan of the heating system should be based on the predicted heating load. Thus, various methods have been developed for predicting heating loads. Recently, artificial intelligence techniques (e.g., ANN: artificial neural network) have been used to predict heating loads. The process for determination of input data variables is necessary to obtain the accuracy of predicted results using an ANN model. However, there is a lack of studies to evaluate the accuracy level of the predicted results caused by the selection and combination of input variables. There is a need to evaluate the performance of an ANN model for prediction of residential heating loads. Therefore, the purpose of this study is, for a residential building, to evaluate the accuracy levels of predicted heating loads using an ANN model with various combinations of input variables. To achieve the study purpose, each case was classified according to the combination of the input variables and the prediction results were analyzed. Through this, the worst, mean, and best were selected according to the predicted performance. In addition, an actual case was selected consisting of variables that can be measured in an actual building. The derived cv(RMSE) of each case resulted in a percentage value of 38.2% for the worst, 7.3% for the mean, 3.0% for the best, and 5.4% for the actual. The largest difference between the best and worst resulted in 33.2%, and thus the precision of the predicted heating loads was highly affected by the selection and combination of the input variables used for the ANN model. Full article
(This article belongs to the Special Issue Data-Driven Energy-Cost Analysis of HVAC System for Buildings)
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23 pages, 8222 KiB  
Article
Comparison of Factorial and Latin Hypercube Sampling Designs for Meta-Models of Building Heating and Cooling Loads
by Younhee Choi, Doosam Song, Sungmin Yoon and Junemo Koo
Energies 2021, 14(2), 512; https://0-doi-org.brum.beds.ac.uk/10.3390/en14020512 - 19 Jan 2021
Cited by 25 | Viewed by 5179
Abstract
Interest in research analyzing and predicting energy loads and consumption in the early stages of building design using meta-models has constantly increased in recent years. Generally, it requires many simulated or measured results to build meta-models, which significantly affects their accuracy. In this [...] Read more.
Interest in research analyzing and predicting energy loads and consumption in the early stages of building design using meta-models has constantly increased in recent years. Generally, it requires many simulated or measured results to build meta-models, which significantly affects their accuracy. In this study, Latin Hypercube Sampling (LHS) is proposed as an alternative to Fractional Factor Design (FFD), since it can improve the accuracy while including the nonlinear effect of design parameters with a smaller size of data. Building energy loads of an office floor with ten design parameters were selected as the meta-models’ objectives, and were developed using the two sampling methods. The accuracy of predicting the heating/cooling loads of the meta-models for alternative floor designs was compared. For the considered ranges of design parameters, window insulation (WDI) and Solar Heat Gain Coefficient (SHGC) were found to have nonlinear characteristics on cooling and heating loads. LHS showed better prediction accuracy compared to FFD, since LHS considers the nonlinear impacts for a given number of treatments. It is always a good idea to use LHS over FFD for a given number of treatments, since the existence of nonlinearity in the relation is not pre-existing information. Full article
(This article belongs to the Special Issue Data-Driven Energy-Cost Analysis of HVAC System for Buildings)
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14 pages, 2804 KiB  
Article
Recognition of Variable-Speed Equipment in an Air-Conditioning System Using Numerical Analysis of Energy-Consumption Data
by Rongjiang Ma, Xianlin Wang, Ming Shan, Nanyang Yu and Shen Yang
Energies 2020, 13(18), 4975; https://0-doi-org.brum.beds.ac.uk/10.3390/en13184975 - 22 Sep 2020
Cited by 4 | Viewed by 1871
Abstract
Motor-driven equipment (ME) is one of the key components in an air-conditioning system, which contributes to the vast majority of the total energy consumption by air-conditioning systems. Distinguishing variable- and constant-speed equipment is important since the energy simulation models of the two types [...] Read more.
Motor-driven equipment (ME) is one of the key components in an air-conditioning system, which contributes to the vast majority of the total energy consumption by air-conditioning systems. Distinguishing variable- and constant-speed equipment is important since the energy simulation models of the two types differ. Traditionally, types of ME are known in advance, and energy consumption data are consequently analyzed. However, in the application scenarios of energy consumption data mining, precedent information on the ME type could be missing. Thus, this study applies this process in reverse, providing new insight into energy consumption data of ME to recognize variable-speed ME in an air-conditioning system. The energy consumption data of ME in an air-conditioning system implemented in a commercial building were collected and numerically analyzed. A proposed simple parameter, coefficient of the median, and several numerical parameters were calculated and used to distinguish variable- from constant-speed ME. Results showed that the energy consumption data distributions of the two types of ME differed. The proposed coefficient of the median could successfully distinguish variable- from constant-speed ME, and it could be applied as an important step in energy consumption data mining of air-conditioning systems. Full article
(This article belongs to the Special Issue Data-Driven Energy-Cost Analysis of HVAC System for Buildings)
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