Research

12 pages, 3365 KiB

Open AccessArticle

Experimental and Numerical Examination of Naturally-Aged Foam-VIP Composites

by Kaushik Biswas, Rohit Jogineedi and Andre Desjarlais

Energies 2019, 12(13), 2539; https://doi.org/10.3390/en12132539 - 02 Jul 2019

Cited by 2 | Viewed by 2316

This article describes an aging study of a foam-vacuum insulation panel (VIP) composite insulation board installed on a test wall in a natural exposure test facility through a 30-month period. Silica-based VIPs with a polymeric barrier film were used in this study. The [...] Read more.

This article describes an aging study of a foam-vacuum insulation panel (VIP) composite insulation board installed on a test wall in a natural exposure test facility through a 30-month period. Silica-based VIPs with a polymeric barrier film were used in this study. The study results showed the effectiveness of a VIP-based insulation to reduce the heat gains and losses through a wall compared to regular rigid foam insulation of the same thickness. However, the long-term performance monitoring indicated a gradual decline in the thermal performance of the foam-VIP composite. In addition, one-dimensional numerical models were created to simulate the in situ behavior of the foam-VIP composite. One model utilized constant thermal conductivities of the test wall components and another utilized temperature-dependent thermal conductivities; the latter used measurements of conductivity over temperatures ranging from −15 to 55 °C. The results of the simulations emphasized the need to use both temperature and time-dependent material properties for accurately predicting the long-term performance of VIP-based insulation systems. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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15 pages, 3656 KiB

Open AccessArticle

Numerical Simulation of Temperature Decrease in Greenhouses with Summer Water-Sprinkling Roof

by Jiaming Guo, Yanhua Liu and Enli Lü

Energies 2019, 12(12), 2435; https://doi.org/10.3390/en12122435 - 24 Jun 2019

Cited by 16 | Viewed by 3680

Abstract

Decreasing the temperature of a greenhouse in summer is very important for the growth of plants. To investigate the effects of a roof sprinkler on the heat environment of a greenhouse, a three-dimensional symmetrical model was built, in which a k-ε [...] Read more.

Decreasing the temperature of a greenhouse in summer is very important for the growth of plants. To investigate the effects of a roof sprinkler on the heat environment of a greenhouse, a three-dimensional symmetrical model was built, in which a k-ε (k-epsilon) turbulent model, a DO (Discrete Ordinates) irrational model, a Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm, and a multiphase model were used to simulate the effects of the roof sprinkler, at different flow rates. Based on the simulation results, it was found that the temperature could be further reduced under a proper sprinkle rate, and the temperature distribution in the film on the roof was more uniform. A test was conducted to verify the accuracy of the model, which proved the validity of the numerical results. The simulation results of this study will be helpful for controlling and optimizing the heat environment of a greenhouse. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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22 pages, 8024 KiB

Open AccessArticle

Adaptive Comfort Control Implemented Model (ACCIM) for Energy Consumption Predictions in Dwellings under Current and Future Climate Conditions: A Case Study Located in Spain

by Daniel Sánchez-García, David Bienvenido-Huertas, Mónica Tristancho-Carvajal and Carlos Rubio-Bellido

Energies 2019, 12(8), 1498; https://doi.org/10.3390/en12081498 - 20 Apr 2019

Cited by 36 | Viewed by 3602

Abstract

Currently, the knowledge of energy consumption in buildings of new and existing dwellings is essential to control and propose energy conservation measures. Most of the predictions of energy consumption in buildings are based on fixed values related to the internal thermal ambient and [...] Read more.

Currently, the knowledge of energy consumption in buildings of new and existing dwellings is essential to control and propose energy conservation measures. Most of the predictions of energy consumption in buildings are based on fixed values related to the internal thermal ambient and pre-established operation hypotheses, which do not reflect the dynamic use of buildings and users’ requirements. Spain is a clear example of such a situation. This study suggests the use of an adaptive thermal comfort model as a predictive method of energy consumption in the internal thermal ambient, as well as several operation hypotheses, and both conditions are combined in a simulation model: the Adaptive Comfort Control Implemented Model (ACCIM). The behavior of ACCIM is studied in a representative case of the residential building stock, which is located in three climate zones with different characteristics (warm, cold, and mild climates). The analyses were conducted both in current and future scenarios with the aim of knowing the advantages and limitations in each climate zone. The results show that the average consumption of the current, 2050, and 2080 scenarios decreased between 23% and 46% in warm climates, between 19% and 25% in mild climates, and between 10% and 29% in cold climates by using such a predictive method. It is also shown that this method is more resilient to climate change than the current standard. This research can be a starting point to understand users’ climate adaptation to predict energy consumption. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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17 pages, 7002 KiB

Open AccessArticle

Energy Performance Assessment According to Data Acquisition Levels of Existing Buildings

by Kyung Hwa Cho and Sun Sook Kim

Energies 2019, 12(6), 1149; https://doi.org/10.3390/en12061149 - 25 Mar 2019

Cited by 13 | Viewed by 4729

Abstract

Existing buildings are likely to consume more energy and emit more greenhouse gases than new buildings because of inevitable deteriorations in physical performance. Accordingly, retrofitting of existing buildings is considered essential to reduce energy consumption and greenhouse gas emissions from the building sector. [...] Read more.

Existing buildings are likely to consume more energy and emit more greenhouse gases than new buildings because of inevitable deteriorations in physical performance. Accordingly, retrofitting of existing buildings is considered essential to reduce energy consumption and greenhouse gas emissions from the building sector. However, assessing the energy performance of existing buildings accurately has limitations because building materials undergo physical deterioration and the actual operational conditions differ from as-built documentation. There is also a difference in the level of data acquisition required for building energy performance assessment depending on the conditions of the building. The aim of this paper is to present types of methods for energy performance assessment of existing buildings considering this data acquisition level. We analyzed various assessment methods, which were classified into three prototypes of methods according to the required level of data acquisition. Type 1 assessed the target building based on literature sources. Type 2 conducted on-site audit and assessed the target building based on additional collected data. Type 3 assessed the target building by further estimating the building properties through analysis of the measured energy data. The applicability of the proposed methods were demonstrated using case studies of three buildings located in Seoul, South Korea. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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16 pages, 1293 KiB

Open AccessArticle

Fixing Efficiency Values by Unfixing Compressor Speed: Dynamic Test Method for Heat Pumps

by Carsten Palkowski, Andreas Zottl, Ivan Malenkovic and Anne Simo

Energies 2019, 12(6), 1045; https://doi.org/10.3390/en12061045 - 18 Mar 2019

Cited by 6 | Viewed by 4075

Abstract

The growing market penetration of heat pumps indicates the need for a performance test method that better reflects the dynamic behavior of heat pumps. In this contribution, we developed and implemented a dynamic test method for the evaluation of the seasonal performance of [...] Read more.

The growing market penetration of heat pumps indicates the need for a performance test method that better reflects the dynamic behavior of heat pumps. In this contribution, we developed and implemented a dynamic test method for the evaluation of the seasonal performance of heat pumps by means of laboratory testing. Current standards force the heat pump control inactive by fixing the compressor speed. In contrast, during dynamic testing, the compressor runs unfixed while the heat pump is subjected to a temperature profile. The profile consists of the different outdoor temperatures of a typical heating season based on the average European climate and also includes temperature changes to reflect the dynamic behavior of the heat pump. The seasonal performance can be directly obtained from the measured heating energy and electricity consumption making subsequent data interpolation and recalculation with correction factors obsolete. The method delivers results with high precision and high reproducibility and could be an appropriate method for a fair rating of heat pumps. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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22 pages, 1513 KiB

Open AccessArticle

Optimization of a New Phase Change Material Integrated Photovoltaic/Thermal Panel with The Active Cooling Technique Using Taguchi Method

by Xiaohong Liu, Yuekuan Zhou, Chun-Qing Li, Yaolin Lin, Wei Yang and Guoqiang Zhang

Energies 2019, 12(6), 1022; https://doi.org/10.3390/en12061022 - 15 Mar 2019

Cited by 38 | Viewed by 5010

Abstract

This paper investigates the energy performances of a hybrid system composed of a phase change materials-ventilated Trombe wall (PCMs-VTW) and a photovoltaic/thermal panel integrated with phase change material (PV/T-PCM). Equivalent overall output energy (Q_E) was proposed for energy performance evaluation regarding [...] Read more.

This paper investigates the energy performances of a hybrid system composed of a phase change materials-ventilated Trombe wall (PCMs-VTW) and a photovoltaic/thermal panel integrated with phase change material (PV/T-PCM). Equivalent overall output energy (Q_E) was proposed for energy performance evaluation regarding different energy forms, diversified conversions and hybrid thermal storages. This study focuses on parameters’ optimization of the PV/T-PCM system and parameters in the PCMs-VTW are kept optimal. Based on the experimentally validated numerical modelling, nine trial experiments have been conducted following Taguchi L₉ (3⁴) standard orthogonal array. The higher the better concept was implemented and the optimal combination of operating parameters was thereafter identified by using signal-to-noise (S/N) ratio and Analysis of Variance (ANOVA) method. The results show that Q_E is highly dependent on the mass flow rate, followed by the diameter of active cooling water pipe. However, the inlet cooling water temperature and the thickness of PCM have limited influence on Q_E. The optimal combination of each factor was identified as B3A3C2D1 (mass flow rate of 1 kg/s, diameter of water pipe of 0.6 m, inlet cooling water temperature of 15 °C and the thickness of PCM of 20 mm) with the highest Q_E of 20,700 kWh. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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11 pages, 2322 KiB

Open AccessArticle

Modelling Load Profiles of Heat Pumps

by Jochen Conrad and Simon Greif

Energies 2019, 12(4), 766; https://doi.org/10.3390/en12040766 - 25 Feb 2019

Cited by 11 | Viewed by 7919

Abstract

Approximately one quarter of energy-related emissions in Germany are caused by the domestic sector. At 81%, the largest share of these emissions is due to heat supply. Many measures are available to reduce these emissions. One of these measures, which is considered to [...] Read more.

Approximately one quarter of energy-related emissions in Germany are caused by the domestic sector. At 81%, the largest share of these emissions is due to heat supply. Many measures are available to reduce these emissions. One of these measures, which is considered to play an important role in many studies, is the replacement of fossil-fired boilers with electric heat pumps. In order to be able to analyse the impact of high penetrations of heat pumps on the energy system, the coefficient of performance (COP) must be modelled with high temporal resolution. In this study, a methodology is presented on how to calculate high-resolution COPs and the electrical load of heat pumps based on thermal load curves and temperature time series. The COP is determined by the reciprocal Carnot factor. Since heat pumps are often designed bivalently due to the cost structure, the methodology described can also be used for evaluating the combination of immersion heater and heat pump (here for the air/water heat pump). As a result the theoretical hourly COPs determined are calibrated with annual performance factors from field tests. The modelled COPs show clear differences. Currently, mostly air source heat pumps are installed in Germany. If this trend continues, the maximum electrical load of the heat supply will increase more than would be the case with higher shares of ground source heat pumps. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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16 pages, 6913 KiB

Open AccessArticle

Proposed Strategies for Improving Poor Hygrothermal Conditions in Museum Exhibition Rooms and Their Impact on Energy Demand

by Joanna Ferdyn-Grygierek and Krzysztof Grygierek

Energies 2019, 12(4), 620; https://doi.org/10.3390/en12040620 - 15 Feb 2019

Cited by 7 | Viewed by 3282

Abstract

In museums, poor microclimate conditions, especially large changes in relative humidity and temperature, can lead to serious deterioration of the exhibits. Properly designed heating, ventilation, and air conditioning (HVAC) systems for precise control of the air parameters are required. However, due to the [...] Read more.

In museums, poor microclimate conditions, especially large changes in relative humidity and temperature, can lead to serious deterioration of the exhibits. Properly designed heating, ventilation, and air conditioning (HVAC) systems for precise control of the air parameters are required. However, due to the financial restrictions of museums, complex air-conditioning systems are often not feasible. In this study, we tested and propose novel methods to reduce the short- and long-term fluctuations in the relative humidity in exhibition rooms of a Polish museum. The methods only include indoor temperature and ventilation airflow control strategies, without the use of (de)humidification equipment. The analysis is based on simulations using EnergyPlus software. A multi-zone thermal model of the museum building was validated and calibrated with measured data. A full calendar year was simulated for five control cases (including the current method used) and two internal heat gain schedules. The energy demand for heating and cooling for each case was calculated. The combination of temperature control and adequate ventilation using ambient airflow allows for dramatic improvement in the microclimate conditions. The proportion of the year when the instantaneous indoor relative humidity is ±5% from set point decreased from 85% to 20%. A significant effect was obtained over the summer months. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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19 pages, 435 KiB

Open AccessEditor’s ChoiceArticle

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

by Sunyong Kim and Hyuk Lim

Energies 2018, 11(8), 2010; https://doi.org/10.3390/en11082010 - 02 Aug 2018

Cited by 121 | Viewed by 10315

Abstract

A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a [...] Read more.

A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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14 pages, 2482 KiB

Open AccessArticle

Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

by Yaolin Lin, Shiquan Zhou, Wei Yang, Long Shi and Chun-Qing Li

Energies 2018, 11(6), 1570; https://doi.org/10.3390/en11061570 - 14 Jun 2018

Cited by 15 | Viewed by 4207

Abstract

Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin [...] Read more.

Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM) is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR), chi-square automatic interaction detector (CHAID), exhaustive CHAID (ECHAID), back-propagation neural network (BPNN), radial basis function network (RBFN), classification and regression trees (CART), and support vector machines (SVM). It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN) and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour. Full article

(This article belongs to the Special Issue Energy Efficiency in Plants and Buildings 2019)

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