Meteorological Year Research Articles

Assessment of Typical Meteorological Year Data in Photovoltaic Geographical Information System 5.2, Based on Reanalysis and Ground Station Data from 147 European Weather Stations

The Photovoltaic Geographical Information System (PVGIS) is a web application that provides free access to solar radiation and temperature data, typical meteorological year (TMY) data, and to photovoltaic performance assessment tools for any place in most parts of the world. The PVGIS was originally developed over 20 years ago, and since then, it has been under continuous development. At present, there are two versions of the PVGIS online—the older version 5.1 and the newest version 5.2. PVGIS 5.2 includes substantial improvements compared to the previous version, e.g., the update of the underlying data sets both in terms of quality, resolution, and geographical coverage and the extension of the time period used. This paper focuses on comparing the TMYs (and more specifically the TMY time series of air temperature), coming from both PVGIS 5.1 and 5.2, with the TMY produced on the basis of ground station meteorological data and with the ground station data itself. The results show that whereas overall the errors and biases for most locations are within the expected range (mean stationRMSE 4.27), these differences increase in places with complicated topography, e.g., in the Alps (maximum stationRMSE 9.50).

Bioclimatic design recommendations for novel cluster analysis-based mapping for humid climates with altitudinal gradient variations

Proper planning of urban environments in construction projects requires an accurate understanding of the prevailing climates in an area. However, the need for sufficient climate records in the Global South countries can make this characterization difficult. This study proposes using satellite information and cluster analysis to define the climatic behavior in Colombia, specifically in areas with intertropical climates with altitudinal gradient impact. The methodology used the statistical analysis of hourly temperature and relative humidity data for recent typical meteorological year (TMY) files. These data were obtained from the National Renewable Energy Laboratory (NREL). Subsequently, cluster analysis was applied to group regions with similar climatic characteristics. Standardization of the climatic variables ensured that all contributed equally to the research. The study results present an atlas with nine different climatic strategy zones, turning Colombia's climate into eight main clusters, each with specific characteristics and recommendations for the passive design of buildings. Identifying these zones made it possible to define the distribution of bioclimatic strategies in the different thermal floors of the region studied. This approach provides a framework for urban planners and city officials to develop climate-responsive building design guidelines adapted to the specific climatic conditions of each zone.

Comparative Analysis of Estimated and Actual Power Self-Sufficiency Rates in Energy-Sharing Communities with Solar Power Systems

Amid the ongoing climate crisis, the international community is enacting policies to promote low-carbon energy-sharing communities. The primary objective of such communities is to enhance community-level energy self-sufficiency. Accurate energy self-sufficiency assessments are paramount in planning energy-efficient architectural designs, urban landscapes, and communal environments. In this study, the energy self-sufficiency rate of an energy-sharing community was estimated at the design stage and compared with the actual energy self-sufficiency rate calculated based on data collected over the following year (April 2022 to March 2023). The outcomes reveal that the estimated energy self-sufficiency rate is 171%, whereas the realized rate is 133%, underscoring the disparity between the projections and outcomes. An analysis of the seasonal variations in these discrepancies elucidated a correlation between the differences in the insolation levels between standard typical meteorological year (TMY) data that are conventionally used for energy generation projections and the actual meteorological conditions. Moreover, a notable incongruity surface exists between the monthly average electricity consumption of a standard four-person household, as stipulated by the Korean Electric Power Corporation (KEPCO) at 273 kWh, and the empirical power consumption at 430 kWh, resulting in a variance of approximately 157 kWh. This study illuminates the complex relationship between variables affecting energy self-sufficiency in energy-sharing communities. It serves as a crucial step towards informed decision making and precision in sustainable urban energy solutions.

Experimental and numerical study of adaptive ventilation and sunlight regulation building envelope combining variable transparency shape-stabilized phase change material

The development of adaptive building envelopes with advanced PCM to reduce building energy consumption is a promising topic. This study proposed an adaptive ventilation and sunlight regulation wall (AVSRW) by combining the variable transparency shape-stabilized PCM (VTSS-PCM), reflective film, and ventilation cavity. The combination of VTSS-PCM and reflective film could passively regulate the solar absorptivity (SA) of AVSRW. The ventilation cavity with airflow control enhanced the heat storage and release efficiency of VTSS-PCM. A dynamic photo-thermal coupling model of the AVSRW was presented and validated by a full-scale experiment in summer and winter conditions. Then, based on the weather data of the typical meteorological year of Changsha, China, the performances of AVSRW on days, months, and a year were evaluated. The impact of three influential factors and their combined effects on the performance of AVSRW were analyzed. Ultimately, a global optimization was conducted to improve the performance of AVSRW, and compared with the massive wall. The results showed that the AVSRW passively decreased its SA on summer days and increased its SA on winter days. The total annual undesired heat exchange (TAHE) of AVSRW first decreased and then increased with the increase of melting temperature (Tm) and thickness (L) of VTSS-PCM. The reflective film had an opposite effect on the annual heat gain (AHG) and annual heat loss (AHL) of AVSRW, resulting in the TAHE remaining unchanged with the increase of reflectivity (R) of the reflective film. The greater the L, the smaller effect of the Tm and R on the AHG and AHL, and the greater the R, the greater effect of the Tm and L on the AHG and AHL. After optimization, the values of the Tm, L, and R corresponding to the optimal solution were 24.60 °C, 0.17 m, and 0.63, respectively. Compared with the reference wall, the AVSRW with the best performance had lower AHG and AHL, and the annual energy-saving rate of the AVSRW reached 43.49%. This study guides the application and parameter optimization of AVSRW in hot summer and cold winter regions.

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.

Defining generation parameters with an adaptable data-driven approach to construct typical meteorological year weather files

Weather files play a critical role in building energy simulation by providing crucial information on the environmental condition. Typical meteorological year (TMY) weather files are often used in building energy simulation to approximate the long-term performance. These files are generated using long-term weather data and are meant to reflect the typical weather conditions within the dataset. Common generation methods used to construct TMY weather files typically rely on expert-defined universal generation parameters. However, the weather parameters and weighting factors used to generate these files, termed generation parameters, often overlook variations in climate and applications. This study introduces a data-driven approach to define the generation parameters used in the Sandia method to produce TMY weather files. The study aims to enhance the generation method by providing a framework to define the generation parameters which considers data availability, climate and application.The proposed methodology employs machine learning and feature importance to define the generation parameters used to construct TMY weather files. The generated TMY weather files are evaluated for a Montreal case study using building energy demand. The results show an improvement in the representativeness of the long-term average building energy demand for the weather file generated using the model-defined generation parameters.

Development of typical meteorological years based on quality control of datasets in Indonesia

This study aims to demonstrate the comprehensive development of typical meteorological years (TMYs) under relatively limited observational data. The distribution of missing hourly observational data of the 2011–2020 period at all sites was examined. This paper proposes a quality control method for filling the gaps in the missing hourly observational data using bias-corrected ERA5 reanalysis data in the process of developing TMYs. Initially, the temperature bias distribution from −4.5 °C to 2.7 °C was reduced to a range of −0.014 °C to 0.005 °C. The relative humidity bias distribution was −6 % to 10 %, and was reduced to −0.32 % to 0.07 %. The bias distribution of wind speeds ranging from −4 m/s to 2 m/s was reduced to −0.02 m/s to 0.35 m/s. The Sandia method with a modified weighting of Finkelstein-Shaffer (FS) statistics was applied to eight climate elements, namely, global horizontal irradiance, direct normal irradiance, diffuse horizontal irradiance, temperature, precipitation, wind speed, relative humidity, and dew point temperature to generate TMYs at 106 sites across eight climate zones in Indonesia. The verification results showed that the average correlation and RMSE between TMYs and their long-term averages were 0.96 and 75 w/m2 for global horizontal radiation, respectively, while those for temperature were 0.86 and 1.3 °C, respectively.

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.

Design and techno-economic analysis of an off-grid integrated PV-biogas system with a constant temperature digester for a cost-effective rural application

This study addresses the issue of fluctuating output in cow dung-based biogas (BG) production due to temperature dependence. A thermal model is proposed and implemented in an area with abandoned BG resources, utilizing typical meteorological year (TMY) data. The model aims for constant BG output by maintaining digester temperature at 39 °C throughout the year. It is found that integrating heating with a 3100 CUM cow dung-based biogas digester (BD) increase the energy production capacity from 2.25 GWh/year to 3 GWh/year. Subsequently, three different cases are studied: standalone PV, PV-BG system, and PV-BG system with the heating arrangement, and they are scheduled using the area's TMY data, according to a strategy that can fulfil highly dynamic load curves with 690 kW peak and 264 kW average demand, as reported by the Government of India, with zero loss of power supply probability (LPSP). In the proposed case, the integration of heating reduced the PV and energy storage system (ESS) sizes by 65 % and 52 %, respectively. The current system imposes a COE of INR 2.1/kWh, which is 70 % cheap than grid electricity in the surrounding areas, with the help of proper resource estimation utilizing TMY data, integration of heating, and compressed biogas (CBG) production with energy.

Research on the generation method of missing hourly solar radiation data based on multiple neural network algorithm

Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of the data directly affects the analysis results. This paper investigates the estimation of hourly solar radiation based on the generation of the typical meteorological year(TMY) using various real meteorological parameters and limited solar radiation data. The focus of this paper is to use two types of neural network algorithms to improve the estimation accuracy and applicability, and to solve the problem of hourly solar radiation data acquisition in non-radiation areas. First, select two city station data and use three methods to generate TMY. Then, two neural network models, BP Neural Network (BP)，Convolutional Neural Network (CNN) are used to estimate the hourly solar radiation data and verify the results. Finally, by constructing a photovoltaic-integrated office building model, the accuracy of the hourly solar radiation estimation model is verified using energy consumption simulation and photovoltaic (PV) power generation simulation. The results show that this paper can well solve the problem of limited radiation data, which provides a new idea for the study of building energy efficiency in areas where radiation data is missing.

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.

A multi-objective optimization framework for building performance under climate change

Most building energy simulations utilize typical meteorological year data, overlooking the impacts of climate change. This can lead to inaccurate predictions and suboptimal designs. This study develops an optimization framework to identify robust building designs under varying climate scenarios. A case study is presented for residential buildings in Huangshan, China under 2020, 2050 and 2020–2050 conditions. A general circulation model provides future weather data. Multi-objective optimization maximizes energy efficiency and minimizes life cycle costs. Machine learning models predict heating, cooling and cost as functions of envelope parameters. Optimization is performed using a genetic algorithm. Results reveal the optimum insulation thickness, wall properties and window specifications. Compared to 2020 data, the ideal roof insulation increases from 40 mm to 90 mm by 2050. The optimal heating transmittance decreases from 0.38 W/m2K to 0.19 W/m2K. The approach highlights the significant influence of evolving climate on building design. It provides architects with accurate projections to make informed design decisions for energy efficiency and cost savings over the building lifespan.

Impacts of high-albedo urban surfaces on outdoor thermal environment across morphological contexts: A case of Tianjin, China

The urban heat island (UHI) effect can exacerbate various environmental challenges related to high temperatures in urban areas. Increasing urban surface albedo is an effective strategy to mitigate UHI. However, the efficacy of high-albedo urban surfaces across varying urban contexts remains poorly understood. In this study, we leverage Urban Weather Generator to systematically simulate the effects of high-albedo roads, walls, and roofs on urban microclimate across Tianjin. Our simulation covers a typical meteorological year, representing typical weather conditions from January to December. Our results reveal that increasing road albedo is more effective in mitigating UHI in fringe areas, whereas increasing wall and roof albedo is more effective in mitigating UHI in central areas. Local climate zones with an urban aspect ratio of about 0.5 can obtain a maximum reduction of road surface temperature (-6 °C) and wall surface temperature (-3 °C). The temperature changes induced by albedo changes show evident seasonal characteristics: the road temperature decreases significantly in summer, while the wall temperature decreases significantly in spring and autumn. Our results could help guide UHI mitigation policies and urban planning in cities hoping to enhance urban surface albedo to balance urban growth and climate resilience.

Development of a hybridized small modular reactor and solar-based energy system for useful commodities required for sustainable cities

The current study develops a hybridized small modular nuclear reactor and solar-based system designed specifically for sustainable communities in metropolitan areas to meet their power, heat, clean fuel, fresh water and food requirements. Both floating-type and bifacial-type photovoltaic arrays are integrated with a high-temperature gas-cooled small modular reactor. In the integrated system, a Rankine cycle subsystem for power generation, a solid oxide electrolyzer for hydrogen production, a pressure swing adsorption unit with ammonia reactor for nitrogen and then ammonia production, a multi-effect desalination unit for freshwater generation, and heat recovery units in order to provide heating to residential area, food drying facility, greenhouse, and fish farm facilities. For both floating and bifacial PV options, the 120 MWp PV modules are considered and integrated with two small modular reactor units at 250 MWth capacities each, 500 MWth in total. A thermodynamic analysis, which is based on energy and exergy approaches, is carried out. The time-dependent analyses are carried out for floating-type PV and bifacial-type PV options for the cities of Istanbul in Turkey; Barcelona in Spain; Los Angeles (LA) in the United States; Tokyo in Japan; and Toronto in Canada by using their hourly meteorological data, source and load capacities in a typical meteorological year. Among ten cases, between 112835 and 146180 tonnes of ammonia are generated in a typical year while meeting the heating and power requirements of sustainable communities in metropolitan areas. Among ten cases, the average overall energy and exergy efficiencies are found as 41.04% and 46.88%, respectively. The hybridization of such nuclear and solar systems achieves unique advantages by preventing intermittencies compared to the renewable-alone systems, reducing carbon emissions compared to fossil-based systems, and lowering the levelized costs of energy compared to the nuclear energy-alone systems.

Assessing the impact of morphed CMIP6 climate model outputs on building energy performance simulations

In this paper we utilise the latest generation of climate change models from Climate Model Intercomparison Project 6 (CMIP6) to morph typical meteorological year (TMY) weather files. These morphed files are used in conjunction with a standard set of reference models for the climate zones in the United States. Building energy demand, comfort, and photovoltaic (PV) yield are all simulated and evaluated for how they can be expected to change under each climate scenario. We find that the shifts in various preference metrics may vary seasonally in both magnitude and direction. Additionally, the overall distribution of the change can be highly variable across the US climate zones. The results indicate that buildings, as designed now, may not be able to provide comfortable conditions in the future. Furthermore the impact of increased cooling demand will change how building-based PV electricity yield is utilised.

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 and performance evaluation of a minimum input model calibration methodology for residential buildings

Energy simulations of existing dwellings are often impeded by the complexity of assigning appropriate model inputs. While data-driven calibration is an effective method to reduce variance between measured and simulated datasets, significant effort is required for monitoring and auditing. A new minimum input calibration method is proposed, where the number of inputs is greatly reduced through a three-step sensitivity analysis creating an input set with the most influential parameters on internal temperatures. The reduction in input parameters simplifies calibration and reduces the likelihood of unrealistic solutions. The proposed method is verified on two dwellings where conventional calibration techniques were compared to the minimum input calibration method using sub-hourly internal temperatures. Compared to baseline models, the variance of minimum input models reduced from 9.9% and 9.7% to 3.3% and 3.8% (CVRMSE (%)). Results indicate that minimum input model calibration can sufficiently predict thermal performance and could be applied to retrofit optimization. Acronyms: BoM: Bureau of Meteorology; BPS: building performance simulation; CD: cooling dominant; CDH: cooling-degree hours; CVRMSE: coefficient of variation of the root mean square error; D: discrete; ECM: energy conservation measure; HD: heating dominant; HDH: heating-degree hours; MAE: mean absolute error; MBE: mean bias error; MIM: minimum input model; MIS: minimum input set; MOAT: manual one at a time; N: normal distribution; NRMSE: normalized root mean square error; RMSE: root mean square error; RMY: reference meteorological year; SHGC: solar heat gain coefficient; U: uniform distribution

A Methodology to Assess the Suitability of Typical Meteorological Year Weather Data for Simulating the Performance of Buildings Conditioned Entirely by Ambient Energy

Abstract This study evaluates for the first time the suitability of typical meteorological year (TMY) weather data for simulating the performance of buildings that are entirely conditioned by ambient energy. A home in Durango, CO, was simulated with TMY data, with real data for 1998–2020 and with extreme meteorological year (XMY) data. For this climate, the indoor temperature in the house designed with TMY data drops below the range of comfortable indoor temperature (20–25 °C) for 16 of 23 years, including as low as 13 °C during 2008. With the thermal time constant of the house adjusted for each data set to maintain comfort, the required time constants for the real data ranged from 1.178 to 7.56 days with a mean of 3.14 and a median of 2.38, while the TMY value was 1.862 for a percentile rank of 0.318. XMY data did not produce significantly better results. The correlation of the time constant to weather parameters showed that the maximum interval during which the 24-h average solar load ratio remains below 1 is a promising index for identifying the most challenging year. Until more representative TMY and XMY weightings are developed for ambient-conditioned buildings across other climates, it is advisable that current TMY data are used only for preliminary design and that multi-year simulations are conducted for final design.

Improvement of methods of obtaining urban TMY and application for building energy consumption simulation

In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs.

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