Outdoor Dry Bulb Temperature Research Articles

Machine learning-based predicting of PCM-integrated building thermal performance: An application under various weather conditions in Morocco

As is now stands, the building sector is among the “big three” significant energy consumer and greenhouse gas contributor in the world. Consequently, there has been a growing movement towards the development and adoption of renewable energy sources, energy efficient measures and energy management strategies. Phase change materials (PCMs) are considered as an alluring option for energy efficiency measures in building. In this paper, a case study building, based on typical residential construction in Morocco, was selected to assess the potential energy savings of adding PCM to the roof for twenty-four cities using the dynamic simulation tool, TRNSYS. Thereafter, thirteen machine learning techniques, including ANN, DT, SVM, ELM, GB, RF, TB, GLRM, GPR, LR, GAM, KRRM and LRR were assessed for predicting the hourly heating, cooling, and total energy consumptions. The models were trained and tested on a dataset that was gathered from the simulations in twenty-four locations in Morocco. The outdoor dry-bulb temperature, the relative humidity, the wind velocity, the wind direction, and the total solar radiation were considered as the key features. The obtained results revealed that using PCM, can effectively lower the total energy demand for all the cities under study, with exception for very cold climate given by Ifrane and Midelt, where the annual total energy consumption shows an increasing trend. Furthermore, comparing the statistical results for each machine learning model, it is found that the SVM model consistently outperforms all models and can be successfully employed to predict the hourly network's outputs of the building.

Experimental and theoretical study on cooling performance of membrane roof with circulated water film

Membrane roofs are widely used in buildings for excellent daylight and visual effects. Nevertheless, excessive solar penetration results in an overheated indoor thermal environment. The experimental and theoretical study on the cooling performance of circulated water film technology on membrane roofs was conducted. The scale experiment results indicated that with circulated water film the membrane surface temperatures decreased rapidly to a level slightly above the water temperature, where the polyvinylidene fluoride (PVDF) and ethylene tetrafluoroethylene (ETFE) membranes were 0.4 and 2.4 °C higher than water temperature. While the indoor air temperature gradually decreased in instances where PVDF roofs were utilized, the application of ETFE roofs did not yield a significant decrease in temperature. Subsequently, we derived an analytical solution for the dynamic two-dimensional heat and mass transfer process of the water film flowing on the membrane with incident solar radiation, which was validated by experiment. The analysis of influential factors indicated that the temperature drop increased with absorbed solar radiation and outdoor dry-bulb temperature, and decreased with the ambient wet-bulb temperature. Conversely, the cooling performance exhibited minimal sensitivity to variations in water film thickness. The indoor operative temperature dropped by 0.5–0.8 °C in the membrane roofed atrium with water film. The water film effectively cools the indoor thermal environment with membrane roofs.

A Review of Data-Driven Building Energy Prediction

Building energy consumption prediction has a significant effect on energy control, design optimization, retrofit evaluation, energy price guidance, and prevention and control of COVID-19 in buildings, providing a guarantee for energy efficiency and carbon neutrality. This study reviews 116 research papers on data-driven building energy prediction from the perspective of data and machine learning algorithms and discusses feasible techniques for prediction across time scales, building levels, and energy consumption types in the context of the factors affecting data-driven building energy prediction. The review results revealed that the outdoor dry-bulb temperature is a vital factor affecting building energy consumption. In data-driven building energy consumption prediction, data preprocessing enables prediction across time scales, energy consumption feature extraction enables prediction across energy consumption types, and hyperparameter optimization enables prediction across time scales and building layers.

Impact of Location and Insulation Material on Energy Performance of Residential Buildings as per Saudi Building Code (SBC) 601/602 in Saudi Arabia

In hot and humid climates, a significant part of the energy is used to cool the building. There are several ways to reduce this air conditioning load, but one standout is through the selection and design of the right building envelope and its components. The thermal characteristics of the building envelope, in particular the thermal resistance of the insulation used, have an impact on the thermal and energy performance of building structures. Thermal conductivity, which indicates the ability of heat to move through a material given a temperature difference, is the primary factor affecting the performance of a thermal insulation material. Both temperature and humidity changes can affect a material's thermal conductivity value, which can then change. In fact, due to the fluctuating ambient air temperature and solar radiation, thermal insulation in buildings is susceptible to significant and continuous temperature variations. Thermal insulation used in building walls and roofs helps to reduce the energy demand of the building. It improves thermal comfort and, if used correctly, reduces the operational cost of the building. The present study has focused on the effects of location and insulation material on the energy performance of a residential building by considering five climatic locations in the Kingdom of Saudi Arabia (KSA). Five commonly used insulation materials with different thermal characteristics, namely polyurethane board (PU), expanded polystyrene (EPS), glass wool (GW), urea-formaldehyde foam (UFF), and expanded perlite (EP), were analyzed under various climatic zones as per the Saudi Building Code 601/602. The selected cities were categorized based on cooling degree days (CDD) and outdoor dry bulb temperature (DBT) as hot, very hot, and extremely hot climatic zones. Insulation improves thermal comfort and, if used correctly, reduces running costs. Experiments were conducted to determine the thermal conductivity, and the energy simulation was performed by employing IES-VE software for various insulation options. The findings indicate that the location has a significant impact on the energy performance of the insulating materials. The energy saving potential of polyurethane board (PU) insulation is more attractive in cities with higher DBTs and CDDs than in cities with lower DBTs and CDDs. The benefit of installing insulation ranged from a 2 to 14% decrease in energy demand for the climate zones studied. The sensitivity analysis showed that the energy saving potential of the insulation materials is sensitive to the set-point temperature (ST) band.

Optimal design of complex dynamic shadings: Towards sustainable built environment

Complex dynamic shading is one of many innovative technologies widely used to improve comfort and energy saving for the construction of a sustainable built environment for modern cities. However, the dynamic mechanism of the complex shading design has not always been well understood, which plays significant importance in the building performance of cities especially considering the complex environmental conditions. In this study, we aim to optimize the annual daylighting and energy performance of a complex, dynamic, origami-based shading system based case studies of a typical city building, which is located in the hot summer and cold winter (HSCW) zone in China. A modified parametric behavior maps (PBM) method was utilized for performance evaluation. A multivariate function was used to describe the interaction between the annual operation schedule and three environmental stimuli, i.e., hourly solar radiation reaching the south-facing window, hourly mean illuminance of the room, and hourly outdoor dry-bulb temperature. Radiance and EnergyPlus were integrated into the annual performance simulation of the dynamic shading. Experimental data has been used for validation of the dynamic strategy. The results indicated that the performance of the complex dynamic shading compared to the conventional static overhang shading depends largely on the annual operation schedule on an hourly basis. Indeed, the dynamic behaviors of complex shadings can improve the daylighting performance by 91.5% and energy performance by 19.9%, respectively, for office buildings with a large window-to-wall ratio in China's HSCW zone. This study will contribute to the performance-based design of complex dynamic shading, and further help to improve sustainability in urban design for modern cities.

다층신경망 학습 알고리즘 변화에 따른 건물 냉방부하 예측 모델의 성능 비교 평가

Purpose: In this study, among the methods of applying machine learning when predicting the load of a building, the cooling load of a building was predicted using a neural network model. To investigate the appropriateness of the learning algorithm of the multi-layer neural network model, the main purpose is to compare the predictive performance according to the change in the learning algorithm. Method: Among the learning algorithms applicable to multilayer neural networks, a total of 16 algorithms were used to predict the cooling load and compare the prediction results. The input variables of the input layer of the neural network model are outdoor dry bulb temperature, outdoor humidity, and Seasonally Data. The training period is 70% and the test period was 30%. The number of layers in the hidden layer is 3, the number of neurons is 20, and Epochs is 100. CvRMSE and MBE are used as performance index of the prediction model. The maximum, minimum, average, and standard deviation of the 20 prediction results are calculated, and the prediction performance according to the change in the learning algorithm was compared. Result: As a result of analyzing the predictive performance for each learning algorithm, the predictive performance according to the learning algorithm was different. Considering the results and deviations of the two indicators of predictive performance comprehensively, the model using the Levenberg-Marquardt (LM) learning algorithm is judged to have the best predictive performance.

Numerical simulation of a mechanical ventilation heat supply tower

AbstractThis study put forward a scheme of heating the groundwater by using the heat supply tower (HST) when the temperature of the groundwater drops yearly. Fluent was used to conduct a 3‐D numerical simulation of heat and mass transfer between air and water in the tower. Taking the Shenyang area as an example, the influence of different air parameters was analyzed. When air velocity was 2.0 m/s and water flow velocity was 1.0 m/s, the heat supply performance was the best. When outdoor dry bulb temperature was 34°C, the contact heat transfer increased underground water temperature by 1.4°C. When wet bulb temperature of air was 23.7°C and the dew point temperature of water vapor was 20.0°C, the condensation heat transfer increased underground water temperature by 2.0 4.0°C. The total temperature rise of water in HST was 3.0 ~ 5.0°C when it was in contact with air, which can effectively alleviate the problem of groundwater temperature decrease. In this study, the static analysis method was used to analyze the economy of the tower and solar energy heat supplement. The conclusion was that the heating cost of the heating tower is lower.

Indoor Thermal Environment Challenges of Light Steel Framing in the Southern European Context

Over the past decades, Southern European residential architecture has been typically associated with heavyweight hollow brick masonry and reinforced concrete construction systems; however, more industrialised alternative systems have been gaining a significant market share, such as the light steel framing (LSF). Regardless of the proliferation of LSF buildings, a lack of experimental research studies have been performed on this construction system in terms of the indoor thermal environment and thermal comfort in the Southern European climate context. Moreover, a research gap also exists regarding experimental comparisons with typical brick masonry buildings. The present study focused on this research gap by characterising and comparing the performance of these two construction systems. A long-term experimental campaign was carried out, involving the construction and monitoring of two identical test cells, differing only by construction system. The test cells were located in Portugal and were monitored over an entire year. The results revealed that the LSF experimental test cell presented higher daily indoor air temperature fluctuations, leading to more extreme maximum and minimum values, closely following the outdoor dry bulb temperature variations. The more responsive behaviour was also reflected in the indoor thermal comfort analysis, with the LSF cell presenting slightly worse performance; however, some advantages were also observed regarding the LSF construction system, which could provide benefits during intermittent residential occupation, especially in mild climates, in which overheating is not a major concern.

The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway

This paper presents a statistical model for predicting the time-averaged total power consumption of an indoor swimming facility. The model can be a powerful tool for continuous supervision of the facility’s energy performance that can quickly disclose possible operational disruptions/irregularities and thus minimize annual energy use. Multiple linear regression analysis is used to analyze data collected in a swimming facility in Norway. The resolution of the original training dataset was in 1 min time steps and during the investigation was transposed both by time-averaging the data, and by treating part of the dataset exclusively. The statistically significant independent variables were found to be the outdoor dry-bulb temperature and the relative pool usage factor. The model accurately predicted the power consumption in the validation process, and also succeeded in disclosing all the critical operational disruptions in the validation dataset correctly. The model can therefore be applied as a dynamic energy benchmark for fault detection in swimming facilities. The final energy prediction model is relatively simple and can be deployed either in a spreadsheet or in the building automation reporting system, thus the method can contribute instantly to keep the operation of any swimming facility within the optimal individual energy performance range.

Opportunities of Light Steel Framing towards thermal comfort in southern European climates: Long-term monitoring and comparison with the heavyweight construction

In a climate such as the southern European, heavyweight buildings have been associated with improved indoor comfort conditions. The heavyweight reinforced concrete and brick masonry construction is the most widespread and technically known constructive system. However, alternative lightweight systems have been gaining market share, such as the Light Steel Framing (LSF). Experimental research focused on the comparison between the two constructive systems is yet scarce.The present work intends to focus on this research gap by analysing and comparing the performance of the two constructive systems in terms of indoor thermal environment and thermal comfort, considering an intermittent residential occupation. To this end, a robust experimental campaign was carried out, based on the construction and monitoring of two identical experimental test cells, differing only in the constructive system. The test cells are located in Portugal and were monitored for one full year.It is shown that the LSF constructive system presents higher daily indoor temperature fluctuation and more expressive peak values, following closer the outdoor dry-bulb temperature variations. The obtained results also evidence the opportunities of the LSF constructive system in terms of indoor thermal comfort, presenting, in general terms, a more promising scenario on the performed annual analyses.

Investigation on Current Control Defrosting Method of Multi-split Variable Refrigerant Flow System

There are many indoor units in the multi-split variable refrigerant flow (VRF) system. The continuous operation of heating in winter is longer than that of ordinary household air conditioning, so that the precise defrosting control is energy-saving and improves the comfort for users. In this paper, during the experiment, the indoor temperature was 20℃, the outdoor dry bulb temperature was 7℃, 0℃, -5℃ and -7℃, and the wet bulb temperature was 6℃, 0℃, -6℃ and -8℃ respectively. The defrosting temperature, liquid pipe temperature, defrosting time and the current value change rate of the condenser fan were compared when the heating capacity was reduced by 15%. The experiments showed that the current change rate was little affected by the outdoor temperature. It was only related to the frost mass, which could reflect the real frosting situation. Then through the maximum cycle heating capacity, air supply temperature and capacity decay rate, the most reasonable defrosting time could be seen when the fan current change rate was 15% - 20%. Compared with the traditional defrosting, the current control defrosting had a higher heating capacity. Under the four working conditions, the heating cycle capacity was increased by 2.3%, 4%, 2.8% and 5.8% in turn. At the same time, the control was precise. The phenomenon of thermosensor falling off and the uneven frost formation had little impact on the current control defrosting.

Energy efficiency of end-user groups for personalized HVAC control in multi-zone buildings

Abstract HVAC control strategies have been considered as a means to reduce energy consumption in buildings. Personalizing the operation of HVAC systems depending on thermal zone could reduce energy consumption while minimizing the thermal discomfort of occupants in multi-zone buildings. Integrating smart metering technologies with building energy management systems in multi-zone buildings makes it possible to categorize individual rooms into several meaningful end-user groups (i.e., thermal zones) based on energy use behaviors. Unfortunately, it still remains unclear how energy efficient end-user groups are in multi-zone buildings. Therefore, this study evaluates the energy efficiency of end-user groups in multi-zone buildings. An energy efficiency assessment framework for end-user groups is developed using energy benchmarking data from seven dormitory buildings in Seoul, Korea. The results show that during the heating season, consistent energy users have the lowest energy efficiency among all end-user groups, especially during unoccupied hours. Also, the energy efficiency of nighttime-peak energy users is low when the outdoor dry-bulb temperature is higher than 0 °C. Last, the occupied buildings have a higher potential for energy savings at over 0 °C. The proposed framework could allow facility managers to determine appropriate control strategies for HVAC systems and personalize the control parameters depending on end-user group.

Climatic division based on frosting characteristics of air source heat pumps

The applicability of common defrost technologies depends on how easy it is for air source heat pumps to frost under local climate conditions. At the same time, the degree of frost formation of the air source heat pumps outdoor units are mainly affected by local climatic conditions. Therefore, this study proposes the concept of frosting degree hours, which is used to specifically characterize the impact of climatic conditions on air source heat pumps frost. In order to make the analysis and calculation of the frosting degree hours more accurate, this study carried out the collection of the actual operation data of the air source heat pumps in winter in many cities in China. Through the selection and analysis of the actual operating data, the data fitting method was used to analyze the different variables and the characteristic temperature difference between the evaporation temperature and the outdoor dry bulb temperature of systems with different power types was obtained. Finally, according to the analysis of the frosting degree hours under different characteristic temperature differences, combined with the frost characteristics of air source heat pumps of different power types in 275 cities in China, the climatic division was performed. This can provide a reference for the climate-adaptive design of air source heat pumps and the development of defrost control strategies.

Application of the bin weather data for building energy analysis in the tropics

The present study presents the bin weather data for four cities in the tropical region and its significance in the building energy analysis. The bin weather data for four cities in Malaysia, which are Bayan Lepas, Kuala Terengganu, Senai and Kuching, were generated. The bin weather data are in the range from 18 to 38 °C with 2 °C intervals and tabulated based on six 4-h shifts. Since there is a lack of updated bin weather data in Malaysia, the present paper provides valuable data for engineers and designers to conduct an energy analysis using the bin method in this region. This data may also serve as an important reference for all tropical regions with similar weather characteristics. Energy analysis is an important tool for estimating the energy usage of a building in order to minimize energy wastage and maximize energy efficiency. The significance of applying bin weather data in energy analysis is also investigated using a case simulation. When the mean outdoor temperature of occupied period is used in simulation, the results deviate less than 1.6% from the results with the bin weather data, but the deviation rises up to 16.68% with the use of the annual mean outdoor dry bulb temperature.

Impact of location and deadband on energy performance of nano aerogel glazing for office building in Saudi Arabia

ABSTRACT The present study has focused on the impact of location and deadband (range of thermostat setpoints for cooling and heating) on the energy performance and economics of nano granular silica aerogel (nanogel) glazing in a multi-storey office building, by considering 26 climatic locations of Saudi Arabia. The selected cities were classified according to the cooling degree days (CDD) and outdoor dry bulb temperature (DBT), as warm, moderately hot, hot, and very hot, and the results were generalized with respect to these climatic zones. The building model was built in Autodesk Revit (2015), and the energy simulation was performed by Ecotect (2011) in two cases: (i) insulated wall and roof, and DG window and (ii) insulated wall and roof, and nanogel glazing. The results show that the location and deadband have crucial impact on the energy performance and economic viability of nanogel glazing. The energy-saving potential and payback period of replacing DG window by nanogel glazing are attractive for cities with higher DBTs and CDD, compared to those with lower DBT and CDD. For all the climatic zones studied, the benefit of replacing DG window by nanogel glazing could be achieved for a specific deadband of 20–24°C.

Outdoor temperature sensitivity of electricity consumption for space heating and cooling: An application to the city of Milan, North of Italy

With the aim of investigating the effect of climate on the electricity use for space heating and cooling, the correlation between the daily electricity consumption of the city of Milan (Italy) in the five-year period 2013–2017 and the daily average outdoor dry bulb temperature of the same location was analyzed by means of statistical tools. To filter out the effect of the variables different from weather the analysis was limited to workdays. Electricity use and outdoor temperature were correlated by using a parametric model, within a parameter estimation approach, in order to highlight the relevant physical phenomena and to identify the value of the building stock characteristic parameters. A modified five-parameter model (M5PM) was proposed, based on the second principle of thermodynamics, which accounts for the effect of the past temperature by adopting an effective temperature approach.The comparison between the actual data for the workdays of the whole 5-year period and the electricity consumption obtained by means of the M5PM revealed a good agreement between the two distributions, as confirmed by the value of the coefficient of determination (R2 = 0.93), by the value of the normalized root mean square error (NRMSE = 2.3%) and by the value of the mean absolute percentage error (MAPE = 1.3%). The regression model was then applied to the electricity use analysis of the individual days of the week.By increasing the number of data set and their disaggregation, the approach based on regressing the energy consumption historical data series could be successfully adopted for applications that are currently approached by direct simulation or by measurement. In this perspective big data analytics in combination with the parameter estimation approach remains a promising tool to facilitate the interpretability of the energy use model.

Estimation and Analysis of Cooling Load for Indian Subcontinent by CLD/SCL/CLF method at part load conditions

A total equivalent temperature difference (TETD) and cooling load temperature difference (CLTD) methods are developed in the excel spread sheets using Visual Basic Programming for calculating the cooling load for a building. The spread sheets are developed to eradicate the human error and time consuming task of manual calculations. TETD and CLTD methods programmed in spread sheets are easiest and fastest way to estimate the cooling loads as compared to that of the commercially available software’s. The TETD method estimates the peak value of the cooling load and CLTD is used to estimate the cooling load on an hourly basis. Cooling loads estimated using TETD method and CLTD method are validated against CARRIER software HAPv4.9. However, there is a marginal difference in the obtained cooling load values of the spread sheets and HAPv4.9. For selecting the air conditioning equipment for a building, the accurate cooling load calculations are required. For peak cooling load, the solar peak time at a given location is required. The present work deals with the analysis of Solar Peak Time for Indian Sub-Continent, by varying the solar time and geographical directions. The variation patterns as a result of heat gains due to occupants, equipment and lighting in the building are analysed on hourly basis using the CLTD method. Similarly, the variation patterns of cooling load due to variation in outdoor dry bulb temperature and relative humidity are obtained for different cities across India by considering the same room psychometric conditions, internal loads and maximum solar peak time. The TETD method always over predicts the estimated cooling load.

A novel solar photovoltaic/thermal assisted gas engine driven energy storage heat pump system (SESGEHPs) and its performance analysis

In order to study the most economical way of heating the buildings, considering the effect of the bilateral load demands on the way of heating the buildings, a novel solar photovoltaic/thermal assisted gas engine driven energy storage heat pump system (SESGEHPs) is presented which can meet the demands of the real-time building heating load and lower temperature heat source of evaporator to ensure the PV/T system works in the optimum temperature range. A test bench of SESGEHPs is set up and the experiments are carried out to test the system heating performance, including the characteristics of the PV/T system, ESGEHPs, building heating load and environmental parameters. The experimental results show that the average COP and PER of the SESGEHPs is nearly 24.5% and 20.4% higher than the ESGEHPs, respectively. The fuel-consumption and the average compressor power consumption of SESGEHPs is nearly 7.5% and 24.0% lower than the ESGEHPs, respectively. Finally, the experimental data are processed using the generalized regression neural network (GRNN) to get the relationship between the independent variable (solar radiation, outdoor dry bulb temperature and wind speed) and dependent variable (the building heating load, heating capacity of the SESGEHPs, heat-obtained quantity and power generation of the PV/T system). It is found that the operating characteristics of the SESGEHPs are greatly affected by the solar radiation and the outdoor temperature, and less affected by the wind speed.

Support vector regression for predicting building energy consumption in southern China

It is increasingly significant to predict building energy consumption in energy-saving decision making. This paper presents the method of support vector regress (SVR) to forecast building energy consumption in southern China. To improve the reliability of SVR in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVR with respect to two parameters, c and g, is explored using the k-fold cross validation with grid searching method based on radial-basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 4 provinces of southern China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r2). With the analysis based on statistical error tests, the results show that the SVR method can estimate the building energy consumption with good accuracy with MSE less than 0.001 and r2 more than 0.99.

Applying support vector machines to predict building energy consumption in China

It is increasingly significant to predict building energy consumption (BEC) in energy-saving decision making. This paper presents the method of support vector machines (SVM) to forecast building energy consumption in China. To improve the reliability of SVM in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVM with respect to two parameters, c and g, is explored using the k-fold cross validation with grid searching method based on radial-basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 30 provinces (including autonomous regions and municipalities) of China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r2). With the analysis based on statistical error tests, the results show that the SVM method can estimate the building energy consumption with good accuracy with MSE less than 1E-3 and r2 more than 0.991.