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18 pages, 4530 KiB

Open AccessArticle

A Novel Virtual Sensor Modeling Method Based on Deep Learning and Its Application in Heating, Ventilation, and Air-Conditioning System

by Delin Wang and Xiangshun Li

Energies 2022, 15(15), 5743; https://doi.org/10.3390/en15155743 - 08 Aug 2022

Cited by 1 | Viewed by 1242

Abstract

Realizing the dynamic redundancy of sensors is of great significance to ensure the energy saving and normal operation of the heating, ventilation, and air-conditioning (HVAC) system. Building a virtual sensor model is an effective method of redundancy and fault tolerance for hardware sensors. [...] Read more.

Realizing the dynamic redundancy of sensors is of great significance to ensure the energy saving and normal operation of the heating, ventilation, and air-conditioning (HVAC) system. Building a virtual sensor model is an effective method of redundancy and fault tolerance for hardware sensors. In this paper, a virtual sensor modeling method combining the maximum information coefficient (MIC) and the spatial–temporal attention long short-term memory (STA-LSTM) is proposed, which is named MIC-STALSTM, to achieve the dynamic and nonlinear modeling of the supply and return water temperature at both ends of the chiller. First, MIC can extract the influencing factors highly related to the target variables. Then, the extracted impact factors via MIC are used as the input variables of the STA-LSTM algorithm in order to construct an accurate virtual sensor model. The STA-LSTM algorithm not only makes full use of the LSTM algorithm’s advantages in handling historical data series information, but also achieves adaptive estimation of different input variable feature weights and different hidden layer temporal correlations through the attention mechanism. Finally, the effectiveness and feasibility of the proposed method are verified by establishing two virtual sensors for different temperature variables in the HVAC system. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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21 pages, 5724 KiB

Open AccessArticle

Thermal Draft Load Coefficient for Heating Load Differences Caused by Stack-Driven Infiltration by Floor in Multifamily High-Rise Buildings

by Juhyun Bak, Jabeom Koo, Sungmin Yoon and Hyunwoo Lim

Energies 2022, 15(4), 1386; https://doi.org/10.3390/en15041386 - 14 Feb 2022

Cited by 3 | Viewed by 1859

Abstract

The stack effect is dominant in multifamily high-rise buildings (MFHRBs) in winter because of the considerable height of MFHRBs, which causes a difference in the infiltration amount between floors. This difference causes a heating load difference between floors in a MFHRB. However, there [...] Read more.

The stack effect is dominant in multifamily high-rise buildings (MFHRBs) in winter because of the considerable height of MFHRBs, which causes a difference in the infiltration amount between floors. This difference causes a heating load difference between floors in a MFHRB. However, there are no indicators to quantify the heating load differences in previous studies. In this article, an indicator—the thermal draft load coefficient (TDLC)—is proposed that can be used to estimate and evaluate the differences between floors in a MFHRB. The TDLC is built on a theoretical model of the stack effect and leakage area of the airflow paths, considering the entire building airflow in a MFHRB. The theoretical model was validated by comparison with a simulation model. The winter average coefficient of variation of the root mean square error and the normalized mean bias error of the theoretical model were acceptable (17.1% and 9.3%, respectively). The TDLC resulted in a maximum of 2.5 and a minimum of approximately 0.1 in the target MFHRB. The TDLC can pre-evaluate the load difference in the building design stage and can be utilized to build design standards or guidelines. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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15 pages, 13895 KiB

Open AccessArticle

Demonstration of Optimal Scheduling for a Building Heat Pump System Using Economic-MPC

by Parantapa Sawant, Oscar Villegas Mier, Michael Schmidt and Jens Pfafferott

Energies 2021, 14(23), 7953; https://doi.org/10.3390/en14237953 - 28 Nov 2021

Cited by 4 | Viewed by 1676

Abstract

It is considered necessary to implement advanced controllers such as model predictive control (MPC) to utilize the technical flexibility of a building polygeneration system to support the rapidly expanding renewable electricity grid. These can handle multiple inputs and outputs, uncertainties in forecast data, [...] Read more.

It is considered necessary to implement advanced controllers such as model predictive control (MPC) to utilize the technical flexibility of a building polygeneration system to support the rapidly expanding renewable electricity grid. These can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints, amongst other features. One of the main issues identified in the literature regarding deploying these controllers is the lack of experimental demonstrations using standard components and communication protocols. In this original work, the economic-MPC-based optimal scheduling of a real-world heat pump-based building energy plant is demonstrated, and its performance is evaluated against two conventional controllers. The demonstration includes the steps to integrate an optimization-based supervisory controller into a typical building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms to solve a mixed integer quadratic problem. Technological benefits in terms of fewer constraint violations and a hardware-friendly operation with MPC were identified. Additionally, a strong dependency of the economic benefits on the type of load profile, system design and controller parameters was also identified. Future work for the quantification of these benefits, the application of machine learning algorithms, and the study of forecast deviations is also proposed. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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19 pages, 3244 KiB

Open AccessArticle

Integrated Modelling of Decentralised Energy Supply in Combination with Electric Vehicle Charging in a Real-Life Case Study

by Georg Göhler, Anna-Lena Klingler, Florian Klausmann and Dieter Spath

Energies 2021, 14(21), 6874; https://doi.org/10.3390/en14216874 - 20 Oct 2021

Cited by 3 | Viewed by 1580

Abstract

Intelligent integration of decentralised energy resources, local storage and direct consumption are key factors in achieving the transformation of the energy system. In this study, we present a modular simulation concept that allows the planning of decentralised energy systems for buildings and building [...] Read more.

Intelligent integration of decentralised energy resources, local storage and direct consumption are key factors in achieving the transformation of the energy system. In this study, we present a modular simulation concept that allows the planning of decentralised energy systems for buildings and building blocks. In comparison to related studies, we use a simulation model for energy planning with a high time-resolution from the perspective of the energy system planner. In this study, we address the challenges of the grid connection in combination with an increasing number of electric vehicles (EV) in the future. The here developed model is applied for an innovative building block in Germany with a photovoltaic (PV) system, a combined heat and power (CHP) unit, battery storage and electric vehicles. The results of the simulation are validated with real-life data to illustrate the practical relevance and show that our simulation model is able to support the planning of decentralised energy systems. We demonstrate that without anticipating future electric vehicle charging, the system configurations could be sub-optimal if complete self-sufficiency is the objective: in our case study, the rate of self-sufficiency of the net-zero energy building will be lowered from 100% to 91% if considering electric vehicles. Furthermore, our simulation shows that a peak minimising operation strategy with a battery can prevent grid overloads caused by EV charging in the future. Simulating different battery operation strategies can further help to implement the most useful strategy, without interruption of the current operation. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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22 pages, 6812 KiB

Open AccessArticle

Use of BIM-FM to Transform Large Conventional Public Buildings into Efficient and Smart Sustainable Buildings

by Rubén Muñoz Pavón, Marcos García Alberti, Antonio Alfonso Arcos Álvarez and Isabel del Rosario Chiyón Carrasco

Energies 2021, 14(11), 3127; https://doi.org/10.3390/en14113127 - 27 May 2021

Cited by 21 | Viewed by 3084

Abstract

New technologies regarding construction, materials and facility management have led to the successful implementation of smart and more sustainable buildings. This is of special interest for the management of large and complex public buildings. However, most of these types of buildings were built [...] Read more.

New technologies regarding construction, materials and facility management have led to the successful implementation of smart and more sustainable buildings. This is of special interest for the management of large and complex public buildings. However, most of these types of buildings were built in Europe during the previous century, when those technologies were still a matter of research. The appearance of Building Information Modelling (BIM) and the combined use of it with other advances in Facility Management (FM) as well as Internet of Things (IoT), Big Data and others, has opened the door to the possible transformation of such type of buildings into more efficient smart buildings without very large investments. In this study, this was studied thoroughly. In addition, the advantages and possibilities were assessed on a case study performed in the Civil Engineering School at Universidad Politécnica de Madrid built in 1969. The main objective of the paper was to show the details and possibilities to transform the building into a smart and more sustainable building by using BIM-FM techniques and self-designed sensors. The conclusions showed that using a three-dimensional model as the center of the management together with the connection with other applications, databases and facility management tools can transform the building into a Smart Building. In addition, the management of the system can be done from the web, nearing the information to the management staff and to the user. All advances were self-developed in order to satisfy the specific needs of the building. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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16 pages, 4659 KiB

Open AccessArticle

A CFD-Based Optimization of Building Configuration for Urban Ventilation Potential

by Jongyeon Lim and Ryozo Ooka

Energies 2021, 14(5), 1447; https://doi.org/10.3390/en14051447 - 06 Mar 2021

Cited by 5 | Viewed by 1808

Abstract

In this paper, we present a performance-based approach to building configuration design to improve the urban ventilation potential at the conceptual design stage, and we demonstrate its application through a case study. The target performance optimized was the ventilation potential of a district, [...] Read more.

In this paper, we present a performance-based approach to building configuration design to improve the urban ventilation potential at the conceptual design stage, and we demonstrate its application through a case study. The target performance optimized was the ventilation potential of a district, including a region of interest at a spatial scale of hundreds of meters. To estimate this performance, we used computational fluid dynamics (CFD), coupled with an evolutionary algorithm, to optimize the design alternatives to produce the building configuration most suitable for a given set of site conditions. Three calculation components must be assembled for a CFD-based design optimization: an optimizer, a geometry/mesh generator, and a CFD solver. To provide links between the calculation components, we utilized an in-house parametric design program. A case study was conducted to test the applicability of the proposed design method to identify the optimal solutions that minimize adverse effects on the ventilation potential of the surrounding area. For a configuration of buildings in a dense urban area, the proposed design method successfully improved the design alternatives. The results show that the urban ventilation potential in the case of the optimized building configuration is 16% greater than that of the initial building configuration. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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23 pages, 4101 KiB

Open AccessArticle

Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

by Brett Bass, Joshua New and William Copeland

Energies 2021, 14(1), 132; https://doi.org/10.3390/en14010132 - 29 Dec 2020

Cited by 10 | Viewed by 3486

Abstract

Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of utility-defined [...] Read more.

Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of utility-defined use cases was established to clarify the initial business case for specific energy efficient building technologies. In partnership with a municipal utility, 178,337 OpenStudio and EnergyPlus models of buildings in the utility’s 1400 km² service area were created, simulated, and assessed with measures for quantifying energy, demand, cost, and emissions reductions of each building. The method of construction and assumptions behind these models is discussed, definitions of example measures are provided, and distribution of savings across the building stock is provided under a maximum technical adoption scenario. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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13 pages, 6026 KiB

Open AccessArticle

Virtual Sensors for Estimating District Heating Energy Consumption under Sensor Absences in a Residential Building

by Sungmin Yoon, Youngwoong Choi, Jabeom Koo, Yejin Hong, Ryunhee Kim and Joowook Kim

Energies 2020, 13(22), 6013; https://doi.org/10.3390/en13226013 - 18 Nov 2020

Cited by 17 | Viewed by 2461

Abstract

District heating (DH) is an energy efficient building heating system that entails low primary energy consumption and reduced environmental impact. The estimation of the required heating load provides information for operators to control district heating systems (DHSs) efficiently. It also yields historical datasets [...] Read more.

District heating (DH) is an energy efficient building heating system that entails low primary energy consumption and reduced environmental impact. The estimation of the required heating load provides information for operators to control district heating systems (DHSs) efficiently. It also yields historical datasets for intelligent management applications. Based on the existing virtual sensor capabilities to estimate physical variables, performance, etc., and to detect the anomaly detection in building energy systems, this paper proposes a virtual sensor-based method for the estimation of DH energy consumption in a residential building. Practical issues, including sensor absences and limited datasets corresponding to actual buildings, were also analyzed to improve the applicability of virtual sensors in a building. According to certain virtual sensor development processes, model-driven, data-driven, and grey-box virtual sensors were developed and compared in a case study. The grey-box virtual sensor surpassed the capabilities of the other virtual sensors, particularly for operation patterns corresponding to low heating, which were different from those in the training dataset; notably, a 16% improvement was observed in the accuracy exhibited by the grey-box virtual sensor, as compared to that of the data-driven virtual sensor. The former sensor accounted for a significantly wider DHS operation range by overcoming training data dependency when estimating the actual DH energy consumption. Finally, the proposed virtual sensors can be applied for continuous commissioning, monitoring, and fault detection in the building, since they are developed based on the DH variables at the demand side. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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22 pages, 2391 KiB

Open AccessArticle

Development and Application of a Flexible Modeling Approach to Reference Buildings for Energy Analysis

by Younghoon Kwak, Jeonga Kang, Sun-Hye Mun, Young-Sun Jeong and Jung-Ho Huh

Energies 2020, 13(21), 5815; https://doi.org/10.3390/en13215815 - 06 Nov 2020

Cited by 3 | Viewed by 1809

Abstract

This paper proposes a flexible modeling approach to develop a theoretical reference building (RB) for energy analysis. We designed an RB for five non-residential buildings, using dynamic simulation from statistically analyzed data of building stock in South Korea. For modeling, four subsets of [...] Read more.

This paper proposes a flexible modeling approach to develop a theoretical reference building (RB) for energy analysis. We designed an RB for five non-residential buildings, using dynamic simulation from statistically analyzed data of building stock in South Korea. For modeling, four subsets of data—form, envelope, system, and operation—were assessed. This study uses the autosizing function within EnergyPlus, to develop the RB. The proposed approach allows for a flexible design where capacities and flow rates of the heating, ventilation, and air-conditioning (HVAC) system match the newly defined RB model. This approach would be ideal for closing the gap between the architectural data and equipment elements. The RB developed in this study allows for performing energy performance analysis by end-use. The analysis results by the end-use can provide support for country-level greenhouse gas (GHG)-mitigation-strategy development. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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15 pages, 3730 KiB

Open AccessArticle

Development of Changeover Operating Method Based on Performance Prediction of Hybrid Geothermal Heat Pump Systems through Field Test and Numerical Analysis

by Ji-Hyun Shin, Yoon-Bok Seong, Yong-In Kim and Young-Hum Cho

Energies 2020, 13(20), 5333; https://doi.org/10.3390/en13205333 - 13 Oct 2020

Cited by 4 | Viewed by 1586

Abstract

The installation and operation of geothermal systems increased due to the expectation of good cooling and heating performance due to stable heat source temperatures. In actual geothermal system operations, heat source temperature rises or falls due to an imbalance of heating and cooling [...] Read more.

The installation and operation of geothermal systems increased due to the expectation of good cooling and heating performance due to stable heat source temperatures. In actual geothermal system operations, heat source temperature rises or falls due to an imbalance of heating and cooling energy usage. Problems of source side temperature result in reduced geothermal system performance. The purpose of this study is to develop hybrid geothermal system operation technology to stabilize temperature and improve system performance by utilizing auxiliary heat source system. The auxiliary heat source system is operated by comparing the performance when operating the geothermal heat pump system alone and the performance when operating the hybrid geothermal heat pump system. The performance of a hybrid geothermal system is determined by the circulating water temperature of the geothermal system and the circulating water temperature of the auxiliary heat source system. Hybrid geothermal heat pump system performance is predicted through numerical analysis and collection of hybrid geothermal system performance data at various temperature ranges through field test. An operating method was developed using the predicted performance as the changeover operating point of the hybrid geothermal heat pump system. When applying the development and operation technology, it handled about 11% more load than the existing geothermal system operation. The addition of an auxiliary heat source increases the initial investment cost compared to the existing geothermal system, but decreases energy consumption, confirming that the initial investment cost of 15.3 years is recovered. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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15 pages, 4100 KiB

Open AccessArticle

Parameter Calibration for a TRNSYS BIPV Model Using In Situ Test Data

by Sang-Woo Ha, Seung-Hoon Park, Jae-Yong Eom, Min-Suk Oh, Ga-Young Cho and Eui-Jong Kim

Energies 2020, 13(18), 4935; https://doi.org/10.3390/en13184935 - 20 Sep 2020

Cited by 5 | Viewed by 2339

Abstract

Installing renewable energy systems for zero-energy buildings has become increasingly common; building integrated photovoltaic (BIPV) systems, which integrate PV modules into the building envelope, are being widely selected as renewable systems. In particular, owing to the rapid growth of information and communication technology, [...] Read more.

Installing renewable energy systems for zero-energy buildings has become increasingly common; building integrated photovoltaic (BIPV) systems, which integrate PV modules into the building envelope, are being widely selected as renewable systems. In particular, owing to the rapid growth of information and communication technology, the requirement for appropriate operation and control of energy systems has become an important issue. To meet these requirements, a computational model is essential; however, some unmeasurable parameters can result in inaccurate results. This work proposes a calibration method for unknown parameters of a well-known BIPV model based on in situ test data measured over eight days; this parameter calibration was conducted via an optimization algorithm. The unknown parameters were set such that the results obtained from the BIPV simulation model are similar to the in situ measurement data. Results of the calibrated model indicated a root mean square error (RMSE) of 3.39 °C and 0.26 kW in the BIPV cell temperature and total power production, respectively, whereas the noncalibrated model, which used typical default values for unknown parameters, showed an RMSE of 6.92 °C and 0.44 kW for the same outputs. This calibration performance was quantified using measuring data from the first four days; the error increased slightly when data from the remaining four days were compared for the model tests. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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16 pages, 367 KiB

Open AccessArticle

A Novel Approach to Enhance the Generalization Capability of the Hourly Solar Diffuse Horizontal Irradiance Models on Diverse Climates

by Raghuram Kalyanam and Sabine Hoffmann

Energies 2020, 13(18), 4868; https://doi.org/10.3390/en13184868 - 17 Sep 2020

Cited by 3 | Viewed by 1527

Abstract

Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few [...] Read more.

Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few studies cover the generalizability of such models on varying climates. Our study investigates how well these models generalize and also show how to enhance their generalizability on different climates. Since machine learning approaches are known to generalize well, we apply them to truly understand how well they perform on different climates than they are originally trained. Therefore, we trained them on datasets from the U.S. and tested on several European climates. The machine learning model that is developed for U.S. climates not only showed low mean absolute error (MAE) of 23 W/m², but also generalized very well on European climates with MAE in the range of 20 to 27 W/m². Further investigation into the factors influencing the generalizability revealed that careful selection of the training data can improve the results significantly. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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18 pages, 5031 KiB

Open AccessArticle

Development of a Predictive Model for a Photovoltaic Module’s Surface Temperature

by Dong Eun Jung, Chanuk Lee, Kee Han Kim and Sung Lok Do

Energies 2020, 13(15), 4005; https://doi.org/10.3390/en13154005 - 03 Aug 2020

Cited by 8 | Viewed by 2135

Abstract

PV (photovoltaic) systems are receiving the spotlight in Korea due to the Renewable Energy 3020 Implementation Plan (RE3020), which has the goal of reaching 20% for the proportion of renewable energy generation by 2030. Accordingly, the actual performance evaluation of PV systems to [...] Read more.

PV (photovoltaic) systems are receiving the spotlight in Korea due to the Renewable Energy 3020 Implementation Plan (RE3020), which has the goal of reaching 20% for the proportion of renewable energy generation by 2030. Accordingly, the actual performance evaluation of PV systems to achieve the RE3020 has become more important. PV efficiency is mainly determined by various weather conditions (e.g., solar radiation) that affect the power generation of PV systems. However, the efficiency is also affected by changes in module surface temperature. In particular, the efficiency decreases when the module surface temperature rises. That is, the actual PV efficiency falls short of the rated efficiency. The estimation of module surface temperature is critical for evaluating the actual performance of PV systems. Many studies have been conducted to calculate the surface temperature. However, most of the previous studies focused on calculations of current surface temperatures using current environment data, which means that the previous studies have limitations related to timestep. That is, there is a lack of predictive models that calculate the future surface temperatures by using the current measured data. Therefore, this study developed a predictive model using an ANN (artificial neural network) algorithm to determine the surface temperature of PV modules for a future period of time. Then, this study evaluated the actual performance (i.e., power generation) with the predicted surface temperatures. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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26 pages, 7805 KiB

Open AccessArticle

Retrofit Methodology Based on Energy Simulation Modeling Applied for the Enhancement of a Historical Building in L’Aquila

by Mariangela De Vita, Giulia Massari and Pierluigi De Berardinis

Energies 2020, 13(12), 3289; https://doi.org/10.3390/en13123289 - 26 Jun 2020

Cited by 7 | Viewed by 2236

Abstract

Energy loss has not been addressed effectively by policies introduced to encourage the preservation and enhancement of historical structures. Material and other constraints, together with safety standard improvements, do not always guarantee adequate levels of environmental performance. An optimization of retrofit measures to [...] Read more.

Energy loss has not been addressed effectively by policies introduced to encourage the preservation and enhancement of historical structures. Material and other constraints, together with safety standard improvements, do not always guarantee adequate levels of environmental performance. An optimization of retrofit measures to align with new uses, new standards of comfort, and energy saving are needed, as are studies based on new best practices for the enhancement of architectural heritage. This paper presents a method that uses dynamic models tared on non-destructive surveys, and based on compatible energy and structural interventions derived from preliminary analyses integrated into special design tools. Energy simulations were carried out using Design Builder (6.1.5.002, Designbuilder Software Ltd, Stroud, UK) software. The case study is a former hospital, S. Salvatore, in L’Aquila, an architecturally important building, severely damaged by an earthquake in 2009. The methodology presented in this research includes in-depth investigations coherently systematized into a multi-scenario output using simulation software. The results guarantee a high level of compatibility with restoration and seismic guidelines, and new building environmental performance requirements. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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24 pages, 4126 KiB

Open AccessArticle

Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data

by Sukjoon Oh, Chul Kim, Joonghyeok Heo, Sung Lok Do and Kee Han Kim

Energies 2020, 13(12), 3189; https://doi.org/10.3390/en13123189 - 19 Jun 2020

Cited by 4 | Viewed by 1829

Abstract

Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural [...] Read more.

Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural gas and water monitoring to be included in SMs. In this study, we analyze heating energy use data obtained from SMs for short-term monitoring and annual predictions using change-point models for the coefficient checking method. It was found that 9-month periods were required to search the best short-term heating energy monitoring periods when non-weather-related and weather-related heating loads and heating change-point temperatures are considered. In addition, the 9-month to 11-month periods were needed for the analysis to apply to other case study residences in the same high-rise apartment. For the accurate annual heating prediction, 11-month periods were necessary. Finally, the results from the heating performance analysis of this study were compared with the cooling performance analysis from a previous study. This study found that the coefficient checking method is a simple and easy-to-interpret approach to analyze interval heating energy use in multi-family residential buildings. It was also found that the period of short-term energy monitoring should be carefully selected to effectively collect targeted heating and cooling data for an energy audit or annual prediction. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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17 pages, 6161 KiB

Open AccessArticle

Fault Detection Methodology for Secondary Fluid Flow Rate in a Heat Pump Unit

by Samuel Boahen, Kwesi Mensah, Yujin Nam and Jong Min Choi

Energies 2020, 13(11), 2974; https://doi.org/10.3390/en13112974 - 09 Jun 2020

Cited by 3 | Viewed by 2189

Abstract

Fault detection and diagnosis (FDD) has become an important subject in heat pumps due to its potential for energy savings. However, research on multiple faults occurring at the secondary fluid side of heat pumps is rare in the open literature. This study experimentally [...] Read more.

Fault detection and diagnosis (FDD) has become an important subject in heat pumps due to its potential for energy savings. However, research on multiple faults occurring at the secondary fluid side of heat pumps is rare in the open literature. This study experimentally examined single secondary fluid flow rate faults (SSFF) and multiple-simultaneous secondary fluid flow rate faults (MSSFF) and their effects on the performance of a heat pump unit, which is a core component of ground source heat pump systems, and proposed FDD methodology to detect these faults. The secondary fluid flow rate faults were simulated in cooling mode by varying the evaporator and condenser secondary fluid flow rates at 60%, 80%, 100%, 120%, and 140% of the reference value according to varying outdoor entering water temperature conditions. Condenser secondary fluid flow rate faults affected the heat pump coefficient of performance(COP) significantly more than the evaporator secondary fluid flow rate fault in SSFF. Cooling capacity was highly dependent on the evaporator secondary fluid flow rate fault while COP was greatly affected by the condenser secondary fluid flow rate fault in MSSFF. The FDD methodology was modeled using correlations and performance trends of the heat pump and can detect SSFF and MSSFF within an error threshold of ±1.6% and ±6.4% respectively. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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15 pages, 2579 KiB

Open AccessArticle

Potential Effects of Vacuum Insulating Glazing Application for Reducing Greenhouse Gas Emission (GHGE) from Apartment Buildings in the Korean Capital Region

by Sanghoon Baek and Sangchul Kim

Energies 2020, 13(11), 2828; https://doi.org/10.3390/en13112828 - 02 Jun 2020

Cited by 9 | Viewed by 2060

Abstract

Korea has set a goal of reducing greenhouse gas emissions (GHGEs) to levels 37% below the “business as usual (BAU)” level by 2030, and the building sector, in particular, aims to reduce GHGEs by 45,000,000-ton CO₂-eq by 2020. In order to [...] Read more.

Korea has set a goal of reducing greenhouse gas emissions (GHGEs) to levels 37% below the “business as usual (BAU)” level by 2030, and the building sector, in particular, aims to reduce GHGEs by 45,000,000-ton CO₂-eq by 2020. In order to reach this goal, it is crucial to reduce GHGEs that result from energy consumption in apartment buildings, which account for approximately 65% of all buildings in the capital region where the population is concentrated. Moreover, as apartment buildings not only have high window-to wall area ratios, but also use insulating glazing (IG) with low thermal performance, an advanced window system with low heat transmittance (U-value), such as a concrete structure, is necessary for effective GHGE reduction. Therefore, this study aims to evaluate the GHGE reduction effects from replacing existing IG vacuum insulating glazing (VIG) with low U-values in the apartment housing located in the capital region. The analysis revealed the possibility of a GHGE reduction by 45%–79% with the application of commercial VIG with U-values of 0.7 W/m²·K in lieu of the existing IG with U-values ranging from 1.2 to 3.3 W/m²·K for all apartment buildings located in the capital region. Furthermore, GHGEs could be reduced by 82%–93% by replacing the existing IG with VIG with U-values of 0.2 W/m²·K. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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21 pages, 6626 KiB

Open AccessArticle

A Comprehensive Analysis of Energy and Daylighting Impact of Window Shading Systems and Control Strategies on Commercial Buildings in the United States

by Niraj Kunwar and Mahabir Bhandari

Energies 2020, 13(9), 2401; https://doi.org/10.3390/en13092401 - 11 May 2020

Cited by 17 | Viewed by 3315

Abstract

Commercial buildings consume approximately 1.9 EJ of energy in the United States, 50% of which is for heating, cooling, and lighting applications. It is estimated that windows contribute up to 34% of the energy used for heating and cooling. However, window retrofits are [...] Read more.

Commercial buildings consume approximately 1.9 EJ of energy in the United States, 50% of which is for heating, cooling, and lighting applications. It is estimated that windows contribute up to 34% of the energy used for heating and cooling. However, window retrofits are not often undertaken to increase energy efficiency because of the high cost and disruptive nature of window installation. Highly efficient window technologies would also need shading devices for glare prevention and visual comfort. An automated window shading system with an appropriate control strategy is a technology that can reduce energy demand, maintain occupant comfort, and enhance the aesthetics and privacy of the built environment. However, the benefits of the automated shades currently used by the shading industry are not well studied. The topic merits an analysis that will help building owners, designers and engineers, and utilities make informed decisions using knowledge of the impact of this technology on energy consumption, peak demand, daylighting, and occupant comfort. This study uses integrated daylight and whole-building energy simulation to evaluate the performance of various control strategies that the shading industry uses in commercial office buildings. The analysis was performed for three different vintages of medium office buildings at six different locations in United States. The results obtained show the control strategies enabled cooling energy savings of up to 40% using exterior shading, and lighting energy savings of up to 25%. The control strategies described can help building engineers and researchers explore different control methods used to control shading in actual buildings but rarely discussed in the literature. This information will give researchers the opportunity to investigate potential improvements in current technologies and their performance. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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27 pages, 7412 KiB

Open AccessArticle

Power Substation Construction and Ventilation System Co-Designed Using Particle Swarm Optimization

by Jau-Woei Perng, Yi-Chang Kuo, Yao-Tsung Chang and Hsi-Hsiang Chang

Energies 2020, 13(9), 2314; https://doi.org/10.3390/en13092314 - 06 May 2020

Cited by 4 | Viewed by 5094

Abstract

This study discusses a numerical study that was developed to optimize the ventilation system in a power substation prior to its installation. We established a multiobjective particle swarm optimizer to identify the best approach for simultaneously improving, first, the ventilation performance considering the [...] Read more.

This study discusses a numerical study that was developed to optimize the ventilation system in a power substation prior to its installation. We established a multiobjective particle swarm optimizer to identify the best approach for simultaneously improving, first, the ventilation performance considering the most appropriate inlet size and outlet openings and second, the reduction of the synthetic noise of the ventilation and power consumption from the exhaust fan equipment and its operation. The study used building information modeling to construct indoor and outdoor models of the substation building and verified the overall performance using ANSYS FLUENT 18.0 software to simulate the air velocity and air temperature distribution within the building. Results show that the exhaust fan of the B1F cable finishing room and the 23 kV gas insulated switchgear (GIS) room optimize the reduction of horsepower by approximately 1 Hp and 0.5 Hp. The combined noise is reduced by 4 dBA and 2 dBA; the exhaust fan runs for 30 min, and the two equipment rooms can cool down by 2.9 °C and 1.7 °C, respectively. Therefore, it is confirmed that the MOPSO algorithm provides a more energy-efficient and environmentally friendly building ventilation environment. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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21 pages, 5005 KiB

Open AccessArticle

Characterizing Variations in the Indoor Temperature and Humidity of Guest Rooms with an Occupancy-Based Climate Control Technology

by Hyojin Kim and Emily Oldham

Energies 2020, 13(7), 1575; https://doi.org/10.3390/en13071575 - 01 Apr 2020

Cited by 5 | Viewed by 2454

Abstract

This paper characterizes variations in the indoor temperature and humidity profiles of actual guest rooms equipped with Occupancy-Based Climate Control (OBCC) systems that were used to initiate a temperature setback to 15.6 °C in the winter and to 26.7 °C in the summer [...] Read more.

This paper characterizes variations in the indoor temperature and humidity profiles of actual guest rooms equipped with Occupancy-Based Climate Control (OBCC) systems that were used to initiate a temperature setback to 15.6 °C in the winter and to 26.7 °C in the summer in the guest rooms. Empirical knowledge of these conditions can provide useful insights for an improved field demonstration and optimization of OBCC, as well as for a more realistic temperature and occupancy input for building simulations for hotel guest rooms. As a result, one year of one minute temperatures and humidity data was characterized against outdoor climate for three different occupancy modes, which was useful to identify the observed room-to-room variations in heat losses and resultant indoor temperatures during the heating season due to the different dynamic heat balance conditions of the guest rooms. This indicated potential discomfort in the rooms that appeared to have a stronger association between outdoor and indoor temperatures, which was also identified from the thermal comfort survey indicating thermostat-related discomfort sources. Interestingly enough, the guests who stayed in these rooms tended to set their thermostat at higher setpoint temperatures when they occupied the room, which appeared to compensate for the low balance-point temperatures of these rooms. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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15 pages, 4745 KiB

Open AccessArticle

The Necessity of Improving the Standard for Thermal Environment in Korean Public Facilities

by Yong-Joon Jun, Jin-Ha Yoon, Shin Kim, Young-Hak Song and Kyung-Soon Park

Energies 2020, 13(3), 523; https://doi.org/10.3390/en13030523 - 21 Jan 2020

Cited by 2 | Viewed by 2107

Abstract

As one of the energy saving policies, the Korean government has been regulating the indoor thermal environment of public office facilities in Korea, starting with energy conservation measures in 1980. This policy, which is above 28 °C in summer and below 18 °C [...] Read more.

As one of the energy saving policies, the Korean government has been regulating the indoor thermal environment of public office facilities in Korea, starting with energy conservation measures in 1980. This policy, which is above 28 °C in summer and below 18 °C in winter, is causing discomfort among the occupants. The purpose of this study is to support the need to improve temperature limitation standards of the Korean public office facilities. For this purpose, the standards for the thermal environment in offices of major countries and associations were examined. Subsequently, they were compared with the Korean standards. Additionally, nine buildings of public office facilities in Korea were surveyed on the thermal environment, and PMV measurement was carried out. As a result, most of the buildings that complied with the cooling temperature standard as well as most of the buildings that did not comply were found to be uncomfortable. In conclusion, to improve the comfort of Korean public office facilities in the heating and cooling period, it is necessary to mitigate temperature regulation and regulate additional environmental factors. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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16 pages, 4196 KiB

Open AccessFeature PaperArticle

Development of Occupant Pose Classification Model Using Deep Neural Network for Personalized Thermal Conditioning

by Eun Ji Choi, Yongseok Yoo, Bo Rang Park, Young Jae Choi and Jin Woo Moon

Energies 2020, 13(1), 45; https://doi.org/10.3390/en13010045 - 20 Dec 2019

Cited by 12 | Viewed by 2146

Abstract

This study aims to propose a pose classification model using indoor occupant images. For developing the intelligent and automated model, a deep learning neural network was employed. Indoor posture images and joint coordinate data were collected and used to conduct the training and [...] Read more.

This study aims to propose a pose classification model using indoor occupant images. For developing the intelligent and automated model, a deep learning neural network was employed. Indoor posture images and joint coordinate data were collected and used to conduct the training and optimization of the model. The output of the trained model is the occupant pose of the sedentary activities in the indoor space. The performance of the developed model was evaluated for two different indoor environments: home and office. Using the metabolic rates corresponding to the classified poses, the model accuracy was compared with that of the conventional method, which considered the fixed activity. The result showed that the accuracy was improved by as much as 73.96% and 55.26% in home and office, respectively. Thus, the potential of the pose classification model was verified for providing a more comfortable and personalized thermal environment to the occupant. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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17 pages, 4484 KiB

Open AccessArticle

Factors Affecting Energy Performance of Large-Scale Office Buildings: Analysis of Benchmarking Data from New York City and Chicago

by ChungYeon Won, SangTae No and Qamar Alhadidi

Energies 2019, 12(24), 4783; https://doi.org/10.3390/en12244783 - 15 Dec 2019

Cited by 10 | Viewed by 3698

Abstract

Buildings in high-income, industrialized cities are responsible for more than 50% of global energy consumption; consequently, many developed cities have legislated energy benchmarking and disclosure policies to understand their buildings’ energy-use dynamics better. By utilizing these benchmarking data and additional information taken from [...] Read more.

Buildings in high-income, industrialized cities are responsible for more than 50% of global energy consumption; consequently, many developed cities have legislated energy benchmarking and disclosure policies to understand their buildings’ energy-use dynamics better. By utilizing these benchmarking data and additional information taken from 3D models, this paper presents a comprehensive analysis of large-scale office buildings located in New York and Chicago, with respect to their energy use intensity (EUI). To identify the primary factors affecting the EUI, Spearman’s correlation analysis and multiple variate regression tests were performed on office buildings over 500,000 ft² (46,452 m²) gross floor area. The results showed the number of floors, construction year, window-to-wall ratio (WWR), and source-to-site ratio statistically significant, while morphological factors such as the relative compactness and surface-to-volume ratio showed limited relation to EUI. In New York City, the smallest EUI median was found in the buildings with 20 to 30 floors, and in Chicago, the buildings with 60 floors or more. A higher source-to-site ratio generally had lower overall EUI in both cities. Despite the high correlation, different kinds of dependency were found for window-to-wall ratio (WWR) and construction year between NYC and Chicago. These findings highlight the relative role that each building’s characteristics play concerning the EUI, depending on the particular building’s typology, scale, and the urban context. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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Review

Jump to: Research

30 pages, 3176 KiB

Open AccessReview

Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends

by Dongsu Kim, Jongman Lee, Sunglok Do, Pedro J. Mago, Kwang Ho Lee and Heejin Cho

Energies 2022, 15(19), 7231; https://doi.org/10.3390/en15197231 - 01 Oct 2022

Cited by 14 | Viewed by 4086

Abstract

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas [...] Read more.

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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17 pages, 2545 KiB

Open AccessReview

Design and Implementation of Smart Buildings: A Review of Current Research Trend

by Dongsu Kim, Yeobeom Yoon, Jongman Lee, Pedro J. Mago, Kwangho Lee and Heejin Cho

Energies 2022, 15(12), 4278; https://doi.org/10.3390/en15124278 - 10 Jun 2022

Cited by 30 | Viewed by 5494

Abstract

The building sector is one of the largest contributors to the world’s total energy use and greenhouse gas emissions. Advancements in building energy technologies have played a critical role in enhancing the energy sustainability of the built environment. Extensive research and new techniques [...] Read more.

The building sector is one of the largest contributors to the world’s total energy use and greenhouse gas emissions. Advancements in building energy technologies have played a critical role in enhancing the energy sustainability of the built environment. Extensive research and new techniques in energy and environmental systems for buildings have recently emerged to address the global challenges. This study reviews existing articles in the literature, mostly since 2000, to explore technological advancement in building energy and environmental systems that can be applied to smart homes and buildings. This review study focuses on an overview of the design and implementation of energy-related smart building technologies, including energy management systems, renewable energy applications, and current advanced smart technologies for optimal function and energy-efficient performance. To review the advancement in building energy-related technologies, a systematic review process is adopted based on available published reviews and research types of articles. Review-type articles are first assessed to explore the current literature on the relevant keywords and to capture major research scopes. Research-type papers are then examined to investigate associated keywords and work scopes, including objectives, focuses, limitations, and future needs. Throughout the comprehensive literature review, this study identifies various techniques of smart home/building applications that have provided detailed solutions or guidelines in different applications to enhance the quality of people’s daily activities and the sustainability of the built environmental system. This paper shows trends in human activities and technology advancements in digital solutions with energy management systems and practical designs. Understanding the overall energy flow between a building and its environmentally connected systems is also important for future buildings and community levels. This paper assists in understanding the pathway toward future smart homes/buildings and their technologies for researchers in related research fields. Full article

(This article belongs to the Special Issue Designing, Modeling and Optimizing Energy and Environmental Systems for Buildings)

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