Typical Meteorological Year Data Research Articles

Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach

Accurate performance assessment of energy systems heavily relies on Typical Meteorological Year (TMY) data. The Sandia method, commonly used for TMY generation, is limited by default weighting criteria for meteorological parameters, restricting its suitability for diverse energy system analyses. In response, this study presents a novel framework for generating TMY files customized to various energy systems. Utilizing three tree-based algorithms (CART, RF, XGBoost) and interpretable machine learning techniques, the framework quantifies and personalizes weighting schemes. Validation of the method's applicability is conducted using long-term historical weather data from Beijing and Lhasa, encompassing three distinct energy systems (a full air conditioning building system and two renewable energy systems). Results indicate that the new TMY generation method excels over the original Sandia method for building and photovoltaic systems but encounters limitations with wind power system. Additionally, incorporating meteorological parameters highly relevant to specific energy systems and comprehensively considering their seasonality will contribute to the development of more representative TMY data. The proposed method facilitates precise foundational climate data acquisition, enabling more accurate energy performance analysis and decision-making.

Generation and application of typical meteorological year data for PV system potential assessment: A case study in China

The potential assessment of photovoltaic(PV) systems is of great significance for installing and laying out PV. Short-term meteorological data cannot reflect the local climate characteristics, so it is not suitable for the potential assessment of PV. A typical meteorological year(TMY) can reflect a region's climatic characteristics, and therefore, it is appropriate for the potential assessment of PV. Although the generation methods of TMY for building energy consumption simulation are mature, there are still limited studies on TMY for PV. Given this, this study employs a method to generate TMY data suitable for the Chinese region, while redefining the influencing factors and their respective weights. A TMY dataset was built for 90 cities based on 38 years of measured data in China and applied to BAPV potential assessment. The study assesses annual generation, CO2 reduction, levelized cost of electricity (LCOE), and net returns(NR). The results reveal the highest annual electricity generation in the Northwest region, with Shiquanhe, Golmud, and Ejina sites reaching 215.65 MWh, 201.2 MWh, and 197.5 MWh, respectively. Conversely, southern and coastal regions, notably Chongqing, exhibit the lowest generation at 75.75 MWh. BAPV in Class I tariff areas, along with some Class II and III areas, can achieve demand-side parity (DSP) in full on-grid mode. Through operational adjustments, DSP can be implemented in 23 currently ineligible areas. This study provides dataset support for calculating PV system power generation and analyzes PV potential across diverse cities. The results contribute to policy formulation and future solar energy research, facilitating precise calculations for efficient green power planning in pursuit of carbon neutrality.

Comparison of typical meteorological year generation methods for building energy simulation in marine climate of China

The unique marine climate can affect the applicability of typical meteorological year (TMY) methods, which has not been adequately discussed in previous research. Based on meteorological data of five islands of Dongsha, Xisha, Nansha, Meiji, and Yongshu in China, this study selected their respective TMY using four methods including CTYW, Danish, Festa-Ratto and Sandia. Then, the suitability of various methods in marine climate, low-latitude sea area, was evaluated through the deviation between TMY data and measured data, as well as the precision of the building energy loads. The conclusions indicate that TMY chosen through CTYW method exhibits the lowest deviations in predicting the monthly mean value of meteorological elements with an average NRMSE value of 2.98%, and the hourly value with a cumulative Filkenstein-Schafer (FS) value of 20.8. It was followed by Sandia method with NRMSE of 3.44% and FS of 21.7. Compared with the mean building energy loads calculated using long-term measured data, the result calculated based on TMY chosen through CTYW exhibits the lowest error rate, followed by Sandia method, with the average NRMSE values of 2.06% and 2.48%, respectively. In summary, the TMY chosen by CTYW has a reliable representativeness in long-term climate characterization and energy loads simulation for low-latitude sea areas of China.

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.

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.

A new method of generating extreme building energy year and its application

In the early stage of building design, typical meteorological year (TMY) used for building energy consumption simulation which mainly representing average conditions, cannot reflect the building energy consumption under extreme weather conditions. Intensified climate change leads to global warming and frequent extreme events, therefore, generating meteorological years for simulating building energy consumption under extreme weather conditions is desperately needed. In this paper, a new method for generating extreme weather years—An extreme building energy year (EBEY) is proposed, which not only considers the influence of various meteorological parameters on building energy consumption, but also calculates the extreme intensity and occurrence time of multiple parameters using dynamic thresholds. The results show that the accuracy of using EBEY to simulate building energy consumption under extreme weather conditions is better than that of temperature-only extreme years, radiation-only extreme years, extreme meteorological years (EMY), typical cold years (TCY) and typical hot years (THY). In addition, this study verifies that combining TMY and EBEY data reduces the number of simulations without losing the accuracy of the simulation results.

Empirical Model for Estimating Measured Monthly Average Global Solar Radiation in Lawra using TMY Data

In this study, simple and multiple regression models were developed to estimate the monthly average daily global solar radiation in Lawra, Ghana using ground measurement of global horizontal irradiance (Nov 2020–May 2022) and typical meteorological year (TMY) data (Jan 2017–Dec 2019). Various predictor variables such as sunshine ratio, minimum relative humidity and maximum relative humidity ratio, minimum and maximum temperature ratio, etc. were correlated from the TMY data. Many model equations were developed with the variables ranging from one to eight. The best model from each category was chosen and compared using statistical indices to determine the overall best model. We used the JMP statistical software’s ‘All Possible Models’ functionality to select the best model from each category. The selected models where then compared using the adjusted R-squared, mean absolute percentage error, and the root mean square error statistical indices. The best model equation correlated with eight independent variables with adjusted R-squared of 0.99. The equation can be used to estimate monthly global solar radiation in Lawra and in locations with similar climatic conditions where ground measurement of radiation data is unavailable but have access to the National Solar Radiation Database’s (NSRDB) TMY data.

Re-modeling the solar diffuse fraction in Taiwan on basis of a typical-meteorological-year data

An objective approach to determine a dataset from which a correlation model of solar diffuse fraction is fitted and tested is proposed. A typical meteorological year (TMY) of global radiation data that is measured in Tainan, Taiwan over 10 years (2011–2020) is used to construct the training dataset and the remaining data (90% data base) is used as a test dataset. Two multiple-predictor and one single-predictor correlation models for the hourly diffuse fraction are developed by using more theoretically rigorous techniques for determining the breaking points for segmentation (excluding the modified Boland-Ridley-Lauret (BRL) model) and how many significant predictors required in each segmented interval for the regression model with piece-wise linear multiple-predictor correlation. The performance of each developed correlation model is superior to that of the existing same-type model using a part (one year or two years) of the same data base. The re-modeled piece-wise linear multiple-predictor correlation model has the best long-term performance of the three developed correlation models. The modified BRL model on basis of the TMY data is second. The re-modeled Liu-Jordan-type (single predictor) model allows real time prediction and has a simpler form than the other two models but prediction accuracy is inferior.

Development of future typical meteorological year (TMY) for major cities in Indonesia: Identification of suitable GCM

Today, with the rapid process of urbanization, the proportion of building energy consumption will continue to increase and speed up the emission of greenhouse gases which can intensify the process of global warming. Thus, building energy conservation has become one of the essential aspects of a sustainable development strategy. A typical meteorological year (TMY) is frequently used in building energy simulation to assess the expected heating and cooling costs in the design of the building. Therefore, by considering the future alternations in climate, it is important to develop future TMY data. To generate the TMY for future climate, the projected weather dataset obtained from GCMs from the IPCC coupled inter comparison project phase 6 (CMIP6) can be helpful. However, a key issue with the use of GCM data is the low resolution and bias of the data. Thus, it is important to identify best suitable GCM for a particular region. Therefore, present study aims to evaluate the performance of 6 global GCMs from the CMIP6 for simulating the surface air temperature over the 29 major cities in Indonesia during 1980-2014. Here, dataset (MERRA-2) was utilized to compare the simulations of GCMs. Further three statistical metrics viz. correlation coefficient, standard deviation and centered root mean square error were computed to check the performance of each GCM against the reanalysis data. For most cities, the correlation coefficient values between the results of GCMs, and the reanalysis dataset ranges from 0.3 to 0.7 whereas the value of standard deviation varies from 0.3 to 1. The result revelled that among all the GCMs MPI-HR is one of the most appropriate choices to simulate the surface air temperature over 8 different cities. However, Nor-MM shows the worse performance over the cities located in Indonesia. For the future period, the input dataset from the best identified GCMs will be downscaled for the generation of TMY for future climate.

Development of typical meteorological year for massive renewable energy deployment in Togo

ABSTRACT Renewable energy (RE) penetration assessment and the development typical meteorological year (TMY) for five cities are considered together in this study. Thus, an integrated method is utilised encompassing RE status assessment and the Sandia method to generate the typical meteorological months of TMY. TMY and long-term data (LT) are then compared as well as a PV system of 3kWc output using TMY and LT under statistical errors. Until 2020 only 11.27% out of 360.02 MW capacity in power demand was RE (hydro and solar). LT and TMY are close for all the towns with a better closeness for Sokode. The latter predicts PV system performance within 2% of the LT in all the sites. More investment has to be put in RE sector because of its potential: 5.27 kWh/m2/day of mean solar radiation, 1238.21 mm of average annual precipitation and 7 m/s of mean wind speed at 50 m above the ground.

Bottom-Up Estimates of the Cost of Supplying High-Temperature Industrial Process Heat from Intermittent Renewable Electricity and Thermal Energy Storage in Australia

We report the upper and lower bounds for the levelized cost of high-temperature industrial process heat, supplied from electricity generated with solar-photovoltaic (PV) and wind turbines in combination with either thermal or electric battery storage using hourly typical meteorological year (TMY) data, in systems sized to supply between 80% and 100% of continuous thermal demand at a site in the northern part of Western Australia. The system is chosen to supply high-temperature air as the heat transfer media at temperatures of 1000 °C, which is a typical temperature for an alumina or a lime calcination plant. A simplified model of the electrical energy plant has been developed using performance characteristics of real PV and wind systems and TMY data of renewable energy resources. This was used to simulate a large sample of possible system configurations and find the optimal combination of the renewable resources and storage systems, sized to provide renewable shares (RES) of between 80% and 100% of the yearly demand. This allowed the upper and lower bounds to be determined for the cost of heat based on two scenarios in which the excess energy is either dumped (upper bound) or exported to the electricity grid (lower bound) at the average generating cost. The lower bound of the levelized cost of energy (LCOEL), which occurs for the system employing thermal storage, was estimated to range from USD 10/GJ to USD 24/GJ for RES from 80 to 100%. The corresponding upper bound (LCOEU), also estimated for the system using thermal storage, are between USD 16/GJ and USD 31/GJ, for RES between 80% and 100%. The utilization of electric battery storage instead of thermal storage was found to increase the LCOE values by a factor of two to four depending on the share of renewable energy. Compared with current Australian natural gas cost, none of the systems assessed configurations is economical without either a cost for CO2 emissions or a premium for low-carbon products. The estimated cost for CO2 emission that is needed to reach parity with current natural gas prices in Australia is also presented.

Better understanding on impact of microclimate information on building energy modelling performance for urban resilience

Building Energy Modelling (BEM) plays a significant role in projecting future building energy demands and predicting urban climate resilience in the context of climate change and urbanization. Accurate weather data are important components in BEM. In this study, we investigate how the BEM performance is affected by weather datasets, including 1) the typical meteorological year (TMY) data, 2) data measured at the suburban ground, and 3) three microclimate datasets, i.e., data measured at a high-rise rooftop near the site, data measured at the near-ground open space close to the site, and developed microclimate data within the urban canopy layer at the site. The new microclimate data are developed by integrating near-ground measured data and microclimate modelling results using a practical GIS model. Compared with the actual energy usage, the predictions of BEM using the developed microclimate data show the least mean bias error of 6%, while the error is 12% when TMY data are used. We further utilize this method to develop microclimate datasets and predict residential energy consumptions under the short-term coronavirus pandemic and long-term climate change scenarios. The findings provide scientific support for the decision-making in future energy planning to improve urban climate resilience.

Generation of meteorological year for the assessment of photovoltaic systems performance in Togo, West Africa

In this paper, the Sandia method, based on the Finkelstein-Schafer (FS) statistical techniques was used to develop a Typical Meteorological Year (TMY) for five cities in Togo (Lomé, Atakpamé, Sokodé, Kara and Mango) with a view to assessing the performance of the 72-cell polycrystalline solar photovoltaic (PV) module in the study areas. Seven daily measurements of meteorological parameters (relative humidity, dew point temperature, minimum and maximum temperatures, mean temperature, wind velocity as well as global solar radiation intensity) obtained from the National Aeronautics and Space Administration (NASA) for 1987–2016 period were used. The TMY predictions were compared with the 30-year Long-Term (LT) average values of the dataset. Two statistical indicators, the Mean Percentage Error (MPE) and the Root Mean Square Error (RMSE), were adopted to evaluate the performance of the PV energy system. Results show close agreements between TMY and LT values for all the parameters considered. The TMY in most cases underestimates the LT mean and fluxes are relatively small. Although the PV energy simulations were found to be underestimated, the generated solar PV energy obtained with TMY compares fairly well with those obtained with LT data. The performance of the TMY data is found to vary over the country but considered ‘very good’ with MPE values ranging between 0.24% and 2.46% as well as RMSE ranging between 8.29 and 16.71 kWh. Slight but insignificant biases (positive in the southern cities of Togo but negative in the north) were found between the estimated PV energy with the TMY when higher weight for global solar radiation was used. This study has implications in building energy simulation for comparative energy efficiency studies.

Economic and environmental assessment of a CO2 solar-powered plant with packed-bed thermal energy storage

Investigating supercritical natural fluids for efficient and clean energy production has become a trending research topic due to their technical and environmental advantages. However, on account of the supercritical operational conditions, using specially-developed components increases manufacturing prices, especially when dealing with solar-powered plants assisted by thermal energy storage (TES) systems. This paper assesses the economic and environmental trends of an integrated supercritical carbon dioxide (s-CO2) solar-powered plant. The system is composed of a packed-bed TES system, a solar field, and a power block while considering conventional backup heating. Transient year-around numerical simulations explore several operational conditions relying on detailed cost and typical meteorological year (TMY) data. Also, the modeling accounts for the system’s environmental sustainability through a penalization cost regarding CO2 emissions due to auxiliary heating. With parametric analyses, the study assesses the compromise solutions minimizing the levelized cost of energy (LCOE). The results revealed the possible feasibility of the integrated system using such a TES technology for s-CO2 and evidenced several venues for further examination. In the end, a sensitivity analysis investigates the influence of the specific costs and TMY data on the LCOE.

Multi-objective building design optimization considering the effects of long-term climate change

Building performance is heavily influenced by weather conditions. Though the climate is changing vastly, few building performance optimizations (BPO) consider global warming over the life expectancy of the buildings. This paper develops a novel multi-objective BPO framework by the using simulation-based surrogate models under the future weather conditions that are determined by morphing the typical meteorological year (TMY) data. This framework is adopted to optimize a typical classroom in a hot and humid area under the future weather scenarios of representative concentration pathways (RCP) 4.5 and 8.5. The energy, thermal comfort and daylighting performances with and without considering the climate changes in the optimizations are compared and their notable differences are discussed. Under future climate, the optimization considering the future climate change improves the building performances significantly compared to the optimization without any climate change considerations (i.e., using the historical TMY). Especially, the winter discomfort hours in RCP4.5 and 8.5 decrease by 7.4% and 13.3%, respectively, when such future climate changes are considered in the BPOs, compared to the BPOs using the historical TMY data. The results show that BPO without considering climate change effects may cause non-negligible uncertainties. The proposed method can effectively improve the building performance in a changing climate.

Fast generation of microclimate weather data for building simulation under heat island using map capturing and clustering technique

In this research, Urban Weather Generator (UWG) is utilized for UHI modeling in a center city area of Shenzhen based on urban morphologic data collected by an information-collection framework. The framework uses map capturing technique and unsupervised clustering for building type classification to gather input parameters for UHI model. The developed building type clustering technique, which adopted principal component analysis and Gaussian Mixture Model, is shown to have potential application in district-scale UHI modeling. Together with a differential-evolution-based calibration procedure, the proposed framework is implemented to the UWG modeling for the selected urban tissue. After calibration, it is found that the weighted sum of coefficient of variance for temperature and relative humidity has been reduced from 7.45 % to around 5.5 % compared with the uncalibrated model. The simulation results shows that the annual mean UHI index and max UHI index in center city Shenzhen is found to be 0.50 °C and 5.9 °C. After applying UHI effect to the typical meteorological year (TMY) data, the cooling degree day and heating degree day change by +12.60 % and −11.92 % compared with the original TMY. The developed method is computationally efficient and the morphed TMY data is a more accurate reflection on local urban microclimate.

Multi-Year and Typical Year Data for Solar Energy System in South Korea

Typical meteorological year (TMY) data has significant importance for solar resource assessment, as well as for building performance analysis. The necessity of high-accuracy TMY data has been well known for many years for the financial viability of solar long-term planing project as it represents long-term weather features. However, the TMY does not mean the use of real-time data; it is therefore important to determine the accuracy of the TMY dataset. In this study, the solar energy performance of TMY was evaluated quantitatively by comparing it to multi-year average weather data (2008-2017) based on the statistical analysis method. The PV power output were produced by using 100kW photovoltaic system based on PVsyst software at six sites. The results showed that the difference between the TMY-PV power output and long-term PV power output (measured with percentage error) are smaller than 9%, which means there is close-fit agreement between TMY and long-term averages PV power output. These findings suggests that TMY can provide reliable estimation of PV power in the feasibility study of PV project.

Compare several methods of select typical meteorological year for building energy simulation in China

In the early architectural design and energy saving reconstruction of building, it is essential need the typical meteorological year（TMY）for building performance analysis. The accuracy of the typical meteorological year data is directly impact the energy simulation results. In order to obtain accurate TMY data for construction engineering applications, the paper does the following work: (1) Reviewed the progress of the typical meteorological year; (2) Several TMY generation methods were compared using the same raw weather data, discusses the applicability of these methods for the cities of typical climate zones in China. Based on the most recent and comprehensive first-hand ground observation data, this paper adopts three methods to generate TMY for the typical city covering the climate zoning of China. Result shows different TMY generation method has applicability in different region, and through the simulation of building energy consumption, the comprehensive method has obvious advantages. It shows that in a region with diverse regional climate characteristics such as China, one certain method for selecting TMY is not enough, and comprehensive methods should be proposed to meet the needs of different building types and energy consumption simulation.

Differential evolution - based system design optimization for net zero energy buildings under climate change

A proper system design is crucial for a net-zero energy building (NZEB) to achieve the desired performance during its lifecycle. Most conventional design methods utilize TMY (typical meteorological year) data or multi-year historical data for NZEB system sizing. Due to the climate change, future weather data may differ considerably from these utilized data. Consequently, these designs may not guarantee NZEBs to achieve the expected performance during their lifecycle. Therefore, this study proposes a differential evolution – based system design for NZEBs under climate change. Using the predicted weather data of Hong Kong (including temperature and solar radiation), the proposed system design can optimize building system sizes for minimizing its lifecycle cost with user-defined performance constraints satisfied. Three performance constraints were considered and they were thermal comfort, energy balance and grid interaction. Using the actual weather data, the proposed design has been validated by comparing with two conventional designs (i.e., TMY data-based design and multi-year historical data-based design) in an office building. The results indicated that the proposed design can achieve better performance in terms of lifecycle cost and constraints satisfaction. With improved performance, the proposed design can be used in practice for NZEB system sizing especially as climate change considered.