Typical Meteorological Research Articles

Impact of future climate scenarios on thermal performance and resilience of building façades: Canadian climate case study

Buildings in urban areas are responsible for a significant share of GHG emissions that directly contribute to climate change. Nevertheless, the built environment is vulnerable to the changing climate. Particularly, the unpredictable weather threatens the performance of building components, durability of building materials, and indoor environmental comfort. In this study, the impact of future climate on thermal performance of building façades in the Canadian climate is investigated using simulation analysis. To account for different climate conditions, three future weather scenarios pertaining to global temperature rise of 0.5°C, 1.5°C, and 2.5°C were compared with historical weather data. Both hourly and Typical Meteorological Year (TMY) weather data were studied. The results, including thermal transmittance, heat flux, moisture content, and façade temperature were compared. This comparison could show the applicability of using averaged TMY data compared to the large hourly dataset. The results show a pattern of change in the façade's thermal and hygrothermal performance as temperature, relative humidity and solar radiation norms change in both seasons. The comparison between the TMY and the Yearly data showed an underestimation of heat transfer within the façade when the TMY data is used. The historical TMY data results showed the inadequacy of this weather file for climate impact assessments of facades in both summer and winter. The approach used in this study can be repeated for different climate conditions, acting as a tool to design façades and predict their performance in face of a changing climate.

A typical meteorological day database of solar terms for simplified simulation of outdoor thermal environment on a long-term

The simulation of the outdoor thermal environment long-term, represented by the annual conditions, can describe outdoor thermal environment characteristics more completely. To simplify the simulation, Typical Meteorological Year (TMY) or Principal Component Analysis (PCA) methods are often used to generate a Typical Meteorological Day (TMD) as input conditions for a simplified simulation of the outdoor thermal environment in the long term. However, the results of these typical days are accidental, the time interval between the typical days is heterogeneous, and these typical days cannot be linked together to represent a data sequence used to describe meteorological features in the long term. By combining knowledge of Solar Terms in the traditional Chinese calendar with the CSWD (the Chinese standard year) method, a meteorological element data sequence of a typical weather day (Solar Terms Typical Meteorological Day, STTMD) can be generated. There are 24 typical days obtained by the STTMD method. In the traditional Chinese calendar, they have a definite time order, and the time interval between the typical days is about 15 days. Connecting these typical days can form a hypothetical meteorological year, which is used to simplify the description of the annual meteorological characteristics with the maximum probability of occurrence. Based on meteorological observation data from 1981 to 2010, the STTMD data series of 61 stations in China were generated. These data were used to form a simple national climate division, and then the meteorological representation of different divisions was analyzed. Meteorological representativeness analysis for these subtions indicated the good accuracy of STTMD data. The method can simplify the numerical simulation process for those studied on long-term and can be extended to applications such as outdoor comfort evaluation and urban building energy consumption.

Performance assessment of the curved-type multi-channel PV roof for space-heating and electricity generation in winter

The curved-type PV roof integrated with flexible PV tiles is a novel attempt to implement the building-integrated photovoltaic/thermal technology into diverse genres of architectural design. To explore its performance in the function of electricity generation and space-heating, different ventilation configurations of the air channels are adopted in the design of the multi-air-channels of the system; two working modes (fresh-air-heating - FAH and recirculated-air-heating - RAH) are applied in the system's operation. The case study is based on a residential building linked with the system in Hefei, China. The performance assessment is conducted on a typical sunny winter day and through the winter of the Typical Meteorological Year. The investigation results indicate that RAH mode has the potential to raise the indoor air temperature to a greater extent. However, the electrical power output shows a disadvantage due to the relatively poor PV cooling effect; increasing the connection air channels could enhance the indoor air heating but add the fan power consumption. The prediction throughout winter manifests that the all-in-parallel connection produces 1827.63 kWh (FAH) and 1574.55 kWh (RAH) of total energy, whereas the 6-in-series connection produces 2085.91 kWh (FAH) and 1618.99 kWh (RAH).

Multi-factor thermal analysis of phase change material integrated roofing elements

In the present study, the PCM layer is integrated into the roof, analyzed, and compared for its thermal behaviour in terms of Indoor Air Temperature Reduction (IATR), Thermal Load Levelling (TLL), and Lag Time (LT) to determine the ideal position, thickness, and type of PCM. The observations from the experimental analysis were validated using COMSOL Multiphysics v6.0 with available literature. The results showed that PCM-integrated roofs reduced the interior temperature by around 30%. Furthermore, the fluctuations in the interior temperatures were reduced to 6.1% with PCM integration within the roof. A lag time of 5–8.25 h was observed for the PCM roofs. The study also found that the material properties impact the overall thermal behaviour of the PCM-integrated roofs. These properties were further explored using continuous cyclic thermal loading and variation of latent heat over a period of four days for three climatic zones in India. Abbreviations: PCM: Phase Change Material; CC: Conventional Concrete; AAC: Autoclaved Aerated Cement; TMY: Typical Meteorological Years; epw file: Energy Plus Weather File; mPCM: Microencapsulated Phase Change Material; PTC: Positive Temperature Coefficient; DHT: Digital Temperature And Humidity; IATR: Indoor Air Temperature Reduction (%); TLL: Thermal Load Levelling (%); LT: Lag Time (hours)

A Case Study of Air Infiltration for Highly Airtight Buildings under the Typical Meteorological Conditions of China

A Case Study of Air Infiltration for Highly Airtight Buildings under the Typical Meteorological Conditions of China

Comparative Numerical Energy and Performance Analysis of Solar Photovoltaic and Solar Thermal Power Generation

This study analyses fixed and tracking solar photovoltaics and, solar thermal power. Performance in different locations was analysed for each configuration and technology using simulations based on a Typical Meteorological Year data. The study analysed a 25MWp solar photovoltaic and solar thermal power plants in each of the eleven selected locations in Zimbabwe. The performance of the solar photovoltaic and solar thermal power plants under different meteorological variables were assessed for all the selected locations. It was shown that different configurations together with different technologies have different conversion efficiencies. A high solar thermal conversion efficiency was found to be 18.718% in Gweru while it was 15.502% for solar photovoltaics in Mutare. The study also showed that highest insolation and clearness index values were found in Gweru. The average energy generated by the fixed photovoltaic collectors, tracking photovoltaic collectors and the Concentrating Solar Power plant were respectively 47.38GWh, 68.18GWh and 192.86GWh. There was a maximum percentage difference in the LCoE generated of 32.03% between the fixed PV collectors and the Concentrating Solar Power plant and a difference of 4.74% was realised between the tracking photovoltaics and Concentration Solar Power.

Typical solar extinction year at Plataforma Solar de Almería (Spain). Application to thermoelectric solar tower plants

The atmospheric extinction of solar radiation reflected by heliostats are recognized as an important factor of radiative losses in Concentrating Solar Power technologies in general and especially in thermoelectric solar tower plants. These types of plants are getting larger (≥100 MWe), and consequently the distances between the heliostats and the receiver very often exceed 1 km and radiative losses due to solar extinction on this path can represent a high percentage. The aerosols and water vapor along this route scatter and absorb solar radiation, preventing a percentage of it from reaching the solar receiver. For this reason, in the process of choosing a location for the design and construction of these plants, the radiative losses due to extinction in that place should be known in advance. Until now, Typical Meteorological Years have been available for the location chosen in the plant design stage, mainly considering Direct Normal Irradiance but not solar extinction. Unfortunately, ground-based measurement of solar extinction has never been properly considered because it was not known how to measure or estimate it adequately. The Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT, Spain) has developed a reliable solar extinction measurement system which has been recording accurate horizontal extinction values at Plataforma Solar de Almería (PSA) since 2017. Based on this unique in the world solar extinction database of more than five years, a Typical Solar Extinction Year has been obtained for the first time to rigorously validate extinction models that then allow knowing the extinction in any area of interest in the world for solar tower power plants and to choose the most convenient one. It has been found that the annual average extinction at PSA for the measurement distance (742 m) is 6 %, with a standard deviation of 2 % and a median of 6 %. This average annual extinction value corresponds to a horizontal extinction coefficient of (0.083 ± 0.029) km−1 and a Visual Range of 47 km (-12 km, +23 km). Frequent haze events have been observed at PSA mainly caused by Saharan dust, which can be considered one more symptom of the current increased desertification and climate change.

Gridded Assessment of Mainland China’s Solar Energy Resources Using the Typical Meteorological Year Method and China Meteorological Forcing Dataset

The National Standard of China has recommended the typical meteorological year (TMY) method for assessing solar energy resources. Compared with the widely adopted multi-year averaging (MYA) methods, the TMY method can consider the year-to-year variations of weather conditions and characterize solar radiation under climatological weather conditions. However, there are very few TMY-based solar energy assessments on the scale of China. On the national scale, the difference between the TMY and MYA methods, the requirement of the data record length, and the impacts of the selection of meteorological variables on the TMY-based assessment are still unclear. This study aims to fill these gaps by assessing mainland China’s solar energy resources using the TMY method and China Meteorological Forcing Dataset. The results show that the data record length could significantly influence annual total solar radiation estimation when the record length is shorter than 30 years. Whereas, the estimation becomes stable when the length is greater or equal to 30 years, suggesting a thirty-year data record is preferred. The difference between the MYA and TMY methods is exhibited primarily in places with modest or low abundance of solar radiation. The difference is nearly independent of the examined data record lengths, hinting at the role of regional-specific weather characteristics. The TMY and MYA methods differ more pronounced when assessing the seasonal stability grade. A total of 7.4% of the area of China experiences a downgrade from the TMY relative to the MYA methods, while a 3.15% area experiences an upgrade. The selection of the meteorological variables has a notable impact on the TMY-based assessment. Among the three meteorological variables examined, wind speed has the most considerable impact on both the annual total and seasonal stability, dew point has the second most significant impact, and air temperature has the least. The results are useful for guiding future research on solar energy assessment in China and could be helpful for solar energy development planning.

Energy Performance and Thermal Comfort Assessment of an Educational Building in Northern Italy: The Importance of Climatic Files in Energy Simulations

Energy efficiency and thermal and acoustic comfort play crucial roles in the design and management of educational buildings. The literature underscores their influence on students’ and teachers’ well-being and productivity. Additionally, numerous works emphasise the growing environmental awareness among children attending sustainably designed or adapted buildings. In light of these considerations, the present study aims to evaluate the dynamic energy performance of a school located in Northern Italy, conceived as a Nearly Zero Energy Building (NZEB). The school integrates various technologies, intelligent systems, and renewable sources to reduce energy requirements. A second aspect under evaluation involves using both real and virtual anemometers to generate crucial weather data for energy simulations. Dynamic analyses often rely on traditional meteorological year (Typical Meteorological Year) datasets derived from outdated sources, leading to inaccuracies in representing current climatic conditions, especially in the case of results related to a specific period.

Numerical performance investigation of High Vacuum Flat Plate Hybrid Photovoltaic-Thermal devices.

We propose an innovative flat plate hybrid Photovoltaic-Thermal system under high vacuum (HV PV-T) optimized for solar-to-thermal energy conversion. It consists of a glass cover, metallic vessel, and the actual PV-T device, which englobes a low-emissive Transparent Conductive Oxide (TCO), a perovskite-based PV cell, a Solar Absorber, and a copper substrate. We investigate, through a 1-D model developed in MATLAB, the performances of the proposed PV-T system, still mined by radiative losses, varying the operating temperature (Top) and the emittance of the TCO (εTCO ) in the ranges of (25÷175) °C and (0.05÷0.45) respectively. The annual thermal and electrical productions are evaluated considering the Typical Meteorological Year of Naples, Italy. Specific annual costs and emission savings are evaluated and compared with the ones assured by commercial High Vacuum Flat Plate Solar-Thermal (HVFP ST) and PV collectors. Results indicate that the proposed HV PV-T increases the annual cost savings by 34% and 11% when compared to HVFP ST and PV collectors, respectively. Moreover, the presented HV PV-T increases the annual CO2 emissions savings by 7% and 48% when compared to HVFP ST and PV collectors, respectively.

Climate data for moisture simulations: producing a Danish moisture reference year and comparison with previously used reference year locations

Buildings comprise of complex systems, and various materials in combination. Different materials have different expected service life, and degradation processes start as soon as a building is put into operation. Degradation processes are often accelerated with the presence of moisture. To build robust and moisture safe buildings, hygrothermal simulations are used to predict hygrothermal conditions in constructions, and especially areas of risk. Simulations are useful tools for prediction of moisture accumulation, and results can further help predict the risk of moisture damage, i.e., frost damage or mould growth. The external climate can therefore have a significant impact on the service life of constructions. Due to the lack of a sufficient Danish climate reference year, including precipitation, hygrothermal simulations of Danish constructions are currently performed with either Danish climate data without precipitation (primarily for energy and indoor climate conditions), or with climate data from closest locations in either Sweden or Germany which include rain. Therefore, a complete Danish reference year, including precipitation, will inevitably enhance the value of hygrothermal simulations of buildings in Denmark. This paper presents the method used to develop full climate datasets based on Danish conditions, including precipitation. To integrate the climate datasets in the hygrothermal simulation programs, the climate datasets contain the following parameters: temperature, relative humidity, wind direction and speed, solar radiation, rain, as well as estimations for cloud cover and longwave radiation based on the other climate parameters. The climate datasets generated and presented in this paper, represent a Typical Meteorological Year (TMY), without extreme events, generated according to EN ISO 15927-4:2006. The climate datasets are based on Danish climate data from the period 2001-2019, provided by the Danish Meteorological Institute, DMI. By prioritizing climate parameters, and through statistical analysis, representative 1-year reference climate datasets were generated based on Danish climate conditions. The climate parameters of the 1-year reference climate datasets are compared with the traditionally used Swedish and German datasets, and a Danish Design Reference Year (DRY) from 1995.

Quantification of HVAC energy savings through occupancy presence sensors in an apartment setting: Field testing and inverse modeling approach

While many simulation-based studies have demonstrated the energy-saving potential of occupancy-driven smart thermostats in residential buildings, field testing of these devices remains relatively limited. The objective of this study is to propose a new field-testing approach to evaluate the heating, ventilation and air-conditioning (HVAC) energy-saving potential of occupancy-driven thermostats and occupancy-centric controls (OCC) for HVAC in a residential apartment in Texas, U.S. A proprietary prototype occupancy sensing system was chosen for this study, coupled with a Raspberry Pi-based hardware framework to facilitate OCC implementation by linking occupancy sensors to the existing HVAC system. Energy consumption of the HVAC system during both non-OCC baseline and OCC modes was measured using various sensors installed at the test site. Furthermore, XGBoost models were developed, used onsite measurements, to consider the effects of various factors like occupancy and outdoor weather conditions on HVAC energy consumption to facilitate a fair comparison. The scope of the analysis was further expanded to cover the period from 2018 to 2022 and Typical Meteorological Year 3 (TMY3) by incorporating a statistical occupancy schedule generator. The results suggest that approximately 15.1% cooling energy consumption could be saved during the testing period, equivalent to around 109 kWh in electricity savings. Moreover, OCCs have the potential to achieve electricity savings ranging from 300 to 330 kWh in the months between April and September, depending on the weather in each year. This corresponds to a cooling energy saving ratio ranging from 19% to 24%. Despite energy savings, OCC had some minor adverse effects on their thermal comfort and perceptions of Indoor Environmental Quality (IEQ). Moreover, residents' ratings for indoor thermal comfort dropped from neutral to slightly dissatisfied after the implementation of OCCs.

Estimating omnidirectional urban vertical wind speed with direction-dependent building morphologies

A main external factor that limits the energy assessment of high-rise buildings in urban areas is the vertical wind conditions. Conventional buildings' energy assessment tools apply a universal vertical wind profile estimation equation in all directions and only consider four typical terrain types. However, the various shape and configurations of buildings in cities can induce the unique wind speed in each direction, and the vertical wind speeds in the same terrain type can be different even in the same city. To get more realistic vertical wind conditions, this study developed an omnidirectional urban vertical wind speed estimation method with direction-dependent building morphologies. A pie-shaped segment method is applied to calculate building morphological features in approaching wind direction. Machine learning methods are developed to calculate parameters of the modified direction-dependent power law urban wind function to convert the Typical Meteorological Year (TMY) wind speed to urban vertical wind speeds. To validate the proposed method, wind tunnel data in Hong Kong are collected for a case study. The results indicate that the proposed method with morphological inputs can effectively estimate the vertical wind speed at different wind directions and heights, and deep neural networks and support vector regression have better performance.

Building Performance under Untypical Weather Conditions: A 40-Year Study of Hong Kong

As a common engineering practice, the buildings are usually evaluated under the Typical Meteorological Year (TMY), which represents the common weather situation. The warm and cool conditions, however, can affect the building performance considerably, yet building performances under such conditions cannot fully be given by the conventional TMY. This paper gives approaches to constructing the weather data that represents several warm and cool conditions and compares their differences by studying the cumulative cooling demands of a typical building in a hot and humid climate. Apart from the Extreme Weather Year (EWY), the Near-Extreme Weather Year (NEWY) and Common warm/cool Years (CY) data are proposed according to the occurrence distributions of the weather over the long term. It was found that the cooling demands of NEWY and EWY differ by 4.8% from the cooling needs of TMY. The difference between the cooling demands of NEWY and CY for most calendar months can be 20% and 15%, respectively. For the hot months, the cooling demands under NEWY and CY take 7.4–11.6% and 2.3–5.6% differences from those under TMY. The uncertainties of building performance due to the ever-changing weather conditions can be essential to the robustness of building performance evaluations.

Development of Predicted Power Generation Nomogram of Photovoltaic System by Installation Conditions Using Typical Meteorological Year

Development of Predicted Power Generation Nomogram of Photovoltaic System by Installation Conditions Using Typical Meteorological Year

Design and implementation of a bioclimatic housing prototype for the Bolivian highlands

The Mechanical - Electromechanical Engineering research institute, made up of a multidisciplinary team, designs and implements a bioclimatic housing prototype for the Bolivian highlands, located near the Andes Mountains at an altitude of 3,000 to 4,000 meters above sea level. This project was developed and implemented with financing from the Swiss Cooperation for Bolivia, within the Climate Change Adaptation projects.
 The design process includes: the sociocultural study of the inhabitants of the region, where direct information is collected from the peasants on the conception of housing in their culture and ancestral construction characteristics of their houses. Subsequently, the climate of the region (temperatures, solar radiation, winds and relative humidity) is characterized with the help of a meteorological station, which provided historical records of 5 years. Those data were sufficient to be able to carry out a reliable characterization, with which the Typical Meteorological Year is determined. Likewise, local construction materials that may be useful in the design are studied, analyzing their thermal and mechanical properties in the laboratory.
 Finally, with all the information described, an architectural design is carried out taking into account bioclimatic aspects, ways of harnessing solar energy, distribution of the room and a viable construction cost for the economic reality of the region. The developed architectural model is optimized in an energy simulation program (Siter Vs. 1.2), achieving energy savings of 65%. The software used is owned property of the research institute
 The house has been monitored with temperature sensors for about a year and six months, to date it shows good results, where the correct functioning of the elements and constructive forms is evidenced, reflecting a temperature inside the house of around 10 °C above room temperature 24 hours a day.

Viability and accessibility of the Great Lakes microclimate data over current TMY weather data for accurate energy demand predictions

As climate change continues to evolve with alarming increases in extreme weather conditions, its impact on building cooling and heating energy needs, in order to maintain acceptable occupant comfort becomes increasingly beyond the design constraints. To reduce such mismatch, it is important to improve the accuracy of weather files used for building energy simulations (BES). The widely used simulation software, EnergyPlus (EP) provides Typical Meteorological Year (TMY) weather data, which are not designed to detect extreme conditions and have limited data locations. There is a greater number of Microclimate (MC) stations that can be incorporated into BES for higher resolution data. The challenge is the time-consuming data preparation process to match the EP format. To address this issue, the co-authors developed a program, Virtual Information Fabric Infrastructure (VIFI), which automatically prepares MC data for more accurate BES. To demonstrate the need for higher resolution data, this study analyzed locations in the Great Lakes region and found that July's cooling demand for Rogers City MI and Alpena MI, which are located 35 mi (56 km) apart, differed by 19%. The results emphasize the importance of considering MC data in BES and provide a solution for their efficient integration into BES.

An improved attention-based deep learning approach for robust cooling load prediction: Public building cases under diverse occupancy schedules

Space cooling in buildings is responsible for massive energy consumption and carbon emissions. Accurate cooling load prediction can facilitate the implementation of energy-efficiency cooling control strategies in practice. In this paper, an improved attention-based deep learning approach is proposed for robust ultra-short-term cooling load prediction. First, a novel time representation learning is introduced to extract the periodicity and non-periodicity of cooling loads efficiently. Then, long short-term memory with an attention mechanism extracts properly the time steps by identifying the relevant hidden states and learns high-level temporal dependency. The approach additionally incorporates extreme gradient boosting through the error reciprocal method, enhancing the elimination of prediction errors and improving robustness. The study takes Guangzhou as an example and generates cooling loads using diverse occupancy schedules of five building types based on the Chinese National Standard and Typical Meteorological Year data. The approach is evaluated on datasets comprising the cooling loads, meteorological data, and contextual information. Through results analysis, the approach outperforms other models in terms of prediction accuracy and robustness across all building types. Additionally, model interpretation is provided regarding feature importance and attention matrixes, which enhances the understanding and transparency of the final prediction from the proposed approach.

Correction of Precipitation Data in Weather Files for the Subtropical Climate in Southern Brazil

The thermal performance of buildings can be evaluated prior to its construction by modeling it using a specialized software. Climate boundary conditions must be represented by a weather file that is composed of a weather dataset organized hourly according to a defined year structure such as Test Reference Year (TRY) or Typical Meteorological Year (TMY). Before this study, there were a few weather files available for cities in southern Brazil, notably, Santa Maria, RS, classified as a humid subtropical climate. The most recent available weather file was built in 2014 and presents inconsistencies with respect to precipitation data. Therefore, the objetive of this study was to process and analyze the climate data of Santa Maria over an eighteen-year period (2002-2020), to generate a more reliable weather file. The applied method considered the following procedures: data collection and processing; TRY (TRY17) and TMY2 (TMY0220) definition; solar radiation data calculation; EPW files generation; and comparison between the new EPW files and the previous existing files. As a result, in a short period of time (2014-2020), significant differences among the weather files were observed. The importance of updating weather files in time intervals shorter than 30 years was emphasized. In relation to the comparative analysis, both weather files (TRY17 and TMY0220) presented dry bulb temperatures in consonance with the other files previously available. Although, the correction of precipitation data could originate building simulations closer to the reality.

Effect of typical meteorological year selection on integrated daylight modeling and building energy simulation

The complete description of outdoor luminous and thermal environment is the basis for daylight utilization design with simulation tools. Nevertheless, Typical Meteorological Year (TMY) and generation method specifically developed for the energy simulation of daylight-utilized buildings is still unavailable currently. Luminous environment parameters have not been taken into consideration in existing TMY generation methods. In this study, the feasibility of existing TMY generation process has been examined. A generic office model implementing sided window daylighting is established. Historical meteorological data of Hong Kong from 1979 to 2007 have been collected and three existing weighting schemes are applied during the Typical Meteorological Month (TMM) selection procedures. Three TMY files for Hong Kong are generated and used to conduct integrated Climate-Based Daylight Modeling and building energy simulation. The result demonstrates that, on annual basis, the energy consumption results obtained from the generated TMY files are in good agreements with the long-term mean annual value. The maximum deviation of annual energy consumptions for the generated TMY files is only 1.8%. However, further analysis on monthly basis shows that all the three generated TMY files fail to fully represent the long-term monthly mean level. The maximum deviation of monthly energy consumptions for the generated TMY files can reach up to 11%. As the energy performance daylight utilization is subject to weather change, analysis on daily and monthly energy level is important, especially during design stage. The deficiency of existing TMM selection process and TMY generation method indicates the necessity to develop a corresponding typical weather data input with finer resolution for the energy simulation of daylight-related buildings.