Measured Weather Data Research Articles

Design-Optimization of Solar-Collector Tilt Angle for Annual-Maximum and Seasonal-Balanced Energy Received

Abstract A detailed research study was performed on the annual energy received by a fixed-tilt solar collector. A new clear-sky model of solar radiation was developed by combining existing clear-sky models. Then, this model, was used to perform optimizations with two different objectives. The first objective is to achieve the maximum-annual solar energy received by a solar panel, while the second is to balance its energy received so that it is more uniform over the course of a year. Each of these objectives have differing optimum tilt values, depending on the location and usage of a solar receiver system. The novelty of this investigation is the use of the aforementioned solar model with a unique algorithm for multiple optimizations for these two different objectives, which results in two different optimum tilt angles that are in turn different from the widely accepted rule-of-thumb that recommends an angle that is equal to the latitude. The solar radiation results obtained with the model used herein were experimentally confirmed through comparison with measured weather data for a number of locations with different sky clearness indexes as well as comparison with those of other studies found in the literature. A major contribution of this study are the plots and tables showing optimum tilt angles as estimated by the model as well as with empirical data. By selecting the appropriate tilt angle, a designer can now decide to either maximize annual electrical energy production or else to balance delivery of electricity over the year.

Comparative analysis of actual evapotranspiration values estimated by METRIC model using LOCAL data and EEFlux for an irrigated area in Northern Sinaloa, Mexico

Landsat images combined with the METRIC model have been used in applications such as EEFlux to estimate actual evapotranspiration (ETa) in irrigated areas, with uncertainty as to whether the results are sufficiently accurate at local scales. This work compares temporally and spatially ETa estimates using Landsat imagery and the METRIC model with LOCAL measured weather data and EEFlux for an irrigated area in northern Sinaloa, Mexico, from 1995 to 2018. A regression analysis and error metrics such as coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and slope were used to compare both models. The temporal analysis results confirm that ETa differences are closely related to crop growth and that the daily mean absolute error was 1.17 mm/day. Spatial analysis showed that when using only cropland pixels without non-cropland pixels in EEFlux, the R2 increases from 0.36 to 0.73, and the RMSE decreases from 2.52 to 1.98 mm/day. Therefore, EEFlux should be limited to cropland areas, and it is suggested to exclude Landsat images close to the planting and harvesting season to reduce the differences.

Effect of different generated weather climate data file on building energy consumption in different climates of Iran

Accurate estimation of building energy consumption is critical, and it can usually be calculated using simulation software like Energy Plus. The input weather data for software is typically a typical or reference year as a standard weather file, such as TMY2. To best estimate building energy consumption, TMY2 data must be generated intelligently. In the present study, first, several TMY2 data files have been generated from different periods (5–21 years) of available measured weather data (9 separate files). Available measured weather data is for 1998 to 2019. Also, a TMY2 file has been generated from the long-term average for the total period (1998–2019), and two TMY2 files have been generated using the CCWeatherGen software for 2020 and 2050. In the next step, the calculated building energy consumption from those files for an educational building will be compared using Energy Plus software. The primary purpose of this study is to investigate the impact of different generated TMY2 (from different weather data periods) on energy consumption and determine the best period to generate TMY2 files from the available period of 5 to 21 years for the capital of Iran (Tehran), which has a semi-arid climate, and Tabriz, which has a cold and mountainous climate. These cities have different climates. Results show that for Tehran, building heating energy consumption changes by about 10% from other generated TMY2 files (one file generated from 2009 to 2019 and another generated from the 2007 to 2019 period). The result shows that generating TMY2 files from different weather data periods affects building energy consumption. So, a suitable strategy for generating TMY2 files is necessary. As a new strategy, the present study proposes that TMY2 files should be generated based on building cooling and heating energy from recent years. Also, because climate change is an important parameter, a TMY2 file should be generated from recent years’ weather data to accurately predict building energy consumption.

Assessment of Maize Crop Evapotranspiration Using Eddy Covariance Flux Tower Weather Data

The study conducted at the Maize Research Centre, Agriculture Research Institute, Professor Jayashankar Telangana State Agricultural University (PJTSAU), Rajendranagar, Hyderabad, from November, 2022 to March, 2023 aimed to compute the crop evapotranspiration (ETc) of maize crop under two different irrigation treatments using the weather data generated from the Eddy Covariance (EC) flux tower. The two treatments comprise of scheduling irrigation at certain Depletion of Available Soil Moisture (DASM) viz., T1: 20% DASM and T2: 40% DASM. The maize crop was sown and cultivated as per the recommended practices. The bio-physical parameters like plant height and LAI were recorded at 15 days interval. These parameters were statistically analysed by two sample T-test with equal variance. The plant height increased from 35 to 198 cm in 20% DASM and 36 to 180 cm in 40% DASM during the crop growth period (30 to 100 DAS). Similarly, LAI increased from 0.72 to 3.9 and 0.77 to 3.2 in 20 and 40 % DASM treatments till 90 DAS, respectively and later on decreased till harvest. The findings emphasize a positive influence of optimum moisture availability in the root zone on plant growth parameters. The amount of irrigation given was measured to compute ETc using Soil Water Balance (SWB) method and the crop parameters like plant height, LAI, stomatal resistance values etc., were used for computing the Penman-Monteith equation using the weather parameters generated from the EC flux tower. Seasonal ETc estimated from the Soil Water Balance (SWB) method (340 & 280 mm) and FAO Penman-Monteith method (350 & 295 mm) in 20 and 40% DASM treatments showed a deviation of +10 and +15 mm, respectively. Furthermore, the study concludes that FAO Penman-Monteith method can accurately estimate ET by using EC flux tower measured weather data, with minor deviations from the SWB method.

Active disturbance rejection coordinated control for integrated solar combined cycle system considering system inertia difference

The integrated solar combined cycle system (ISCC) primarily consists of solar thermal power generation equipment and gas turbines. This system effectively mitigates the inherent randomness associated with power generation due to weather conditions while simultaneously providing electric and heat loads. However, due to the difference in response speed of various power generation equipment, it is difficult to respond quickly as the strength of illumination and electricity-heat load demand change, seriously affecting the system's stability operation. This paper proposes an active disturbance rejection control (ADRC) method for the ISCC system considering the different response speeds of generation equipment. Firstly, the mathematical model of the ISCC system is established, in which the system's response speed is improved through the inertial power compensation method. Then, the ADRC method is used to reduce the disturbance caused by uncertain factors, such as solar radiation intensity and load demand change, to ensure the balance between electric and thermal power supply and demand. Finally, based on the measured weather data from National Renewable Energy Laboratory (NREL), the proposed method's performance is evaluated, compared with the hybrid H2/H∞ control method and the auto-coupling proportional integral differential (ACPID) control method. Comparison results show that the ADRC method has better control effectiveness and dynamic response performance, powerfully shortens the system stability time, and reduces overshoot.

Prediction of electricity generation from res by machine learning methods

RELEVANCE. Today, the degree of integration of renewable energy sources into the energy system is an indicator of the technological and industrial development of the state. Renewable energy is a driver for the development of the economy, science and education. In Russia, the largest technical potential from renewable energy sources in the Sun (in million tons of standard fuel) is 2.3 * 103, the second place is occupied by wind energy - 2 * 103. However, the use of solar energy is associated with great difficulties in predicting the generation of electricity due to its dependence on meteorological conditions, and there is an acute issue of forecasting the generation. In this article, the authors propose a solution to the urgent problem of predicting energy generation from solar power plants using machine learning systems. TARGET. The purpose of this work is to study the performance of modern artificial intelligence methods to create a platform for predicting the power generated from a solar station to an existing network. Develop the architecture of the information and communication system of the distribution network and the model for predicting the photovoltaic power of the power plant based on machine learning methods. METHODS. One approach to solving this problem is to use machine learning algorithms. Such algorithms, with a correctly chosen training model, are capable of predicting the volume of electricity generation a day ahead with a high accuracy of up to 95%. RESULTS. The values of real generation and predicted generation were compared by five machine learning algorithms, such as neural networks, linear regression, decision tree, random forest, adaptive boosting. The random forest algorithm has the smallest mean square error on the test data. The problem of optimization of the radial topology of the network, which minimizes the total loss of active power, is solved. CONCLUSION. An analysis of the construction of a working machine learning model showed that in order to build an optimal model, only the history of the power generation of this plant, compared with the calculated and measured weather data, is needed. The stability of the model was tested by applying the cross-validation method under various training and testing conditions. The results obtained showed that the model works reliably, since the root-mean-square error of the most accurate model is in the region of 600 kWh (4%).

VALIDATION OF PHOTOVOLTAIC MODEL FOR APPLICATION IN A DISTRIBUTED ENERGY SOURCE USING WEATHER DATA

This work presents a photovoltaic model for a simulation of the electric energy production of a distributed energy system. The model will be validated through measured weather data and energy produced by an 88.04 kWp power plant installed on the Federal University of Espírito Santo (UFES) in Vitória. The validation consists of comparing the real power data acquired by the photovoltaic plant and the power data calculated from the model using weather data, specifications of the panels and inverters and the constructive characteristics of the system. The proposed model is based on the paper “Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data”, Applied Energy, v. 278, 2020, https://doi.org/10.1016/j.apenergy.2020.115581. In addition, the model has been improved with the inclusion of new intrinsic parameters of the material, the calculation of losses and the adjustments for different tilt angles and azimuth orientations. Results of this method reveal that the model was able to reproduce the behavior of the real photovoltaic plant, presenting a mean absolute percentage error of 8.8% between real and simulated data.

Quantitative analysis and enhancement on passive survivability of vernacular houses in the hot and humid region of China

Occupants' health is closely related to indoor environmental quality. The vast stock of vernacular houses, which were designed according to historical weather features, may be vulnerable to the effects of global warming. Assessing and improving the vernacular houses’ passive survivability under the up-to-date climate is of great significance to prevent occupants from the threat of heat-related illnesses. This study aims to assess the current indoor overheating risks of four typical vernacular houses in the hot and humid region of China and to identify the potential of passive strategies for improving the indoor thermal environment. Two metrics, indoor overheating degree (IOD) and indoor overheating hour (IOH), are used to describe the thermal environment of the vernacular houses. The consequences of indoor environments under uncertain weather conditions are studied using 10 years of continuous ground measurements of weather data. The impacts of design strategies on indoor overheating were analyzed through sensitivity tests. It is shown that under natural ventilation, the ranges of IOD and IOH in vernacular houses can be as much as 0.7–1.5 °C and 40–70%, respectively. The ability of vernacular houses to resist thermal stress varies with different building characteristics. Implementing design strategies such as increasing the thermal performance of glazing and the operable area of windows, can effectively reduce the risk of indoor overheating in the summer. This research is expected to provide implications for improving the passive survivability of vernacular houses in hot and humid regions.

Evaluation of MSWX gridded data for modeling of wheat performance across Iran

Cropping system models (CSMs) are useful tools for developing and optimizing crop management strategies under specific soil and climate conditions. This requires good quality weather data, which is often lacking in many agricultural areas of the world. Studies exposed the capability of gridded weather data to be a proper source in situations where measured weather data is not available. This study assessed the accuracy of a recently published gridded weather dataset called MSWX for modeling of wheat performance in Iran. MSWX has a 0.1°× 0.1° spatial and 3–hourly temporal resolution and covers 1979 to present, and it was initially generated using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 gridded data. For this study, the CSM-NWheat model within Decision Support System for Agrotechnology Transfer (DSSAT) was applied for modeling of wheat production of three irrigated and three rainfed cultivars at six sites in Iran during 2000–2010 with observed daily weather data. Results showed that MSWX maximum and minimum temperatures (Tmax and Tmin) had higher accuracy than AgERA5, while MSWX solar radiation had weaker performance than AgERA5. The bias of MSWX solar radiation was significantly greater than AgERA5 in some of sites. Comparing the NWheat model outputs based on measured and MSWX temperature (solar radiation) data, grain yield was estimated using MSWX with a MAE of 172 (133) kg/ha and a NRMSE of 11.4% (7.6%). The MSWX temperature (solar radiation) data had high accuracy for simulation of flowering and maturity dates with MAE of 3 and 5 (1 and 3) days and NRMSE of 5.4% and 7% (2.1% and 4.5%). The MSWX temperature data was found as the best replacement for measured temperature data in the study area. Also, it seems that MSWX solar radiation has the potential to be an adequate substitution for measured solar radiation data after further bias correction.

Datasets of a Multizone Office Building under Different HVAC System Operation Scenarios

This study provides an open-source dataset of the measured weather data, building indoor data, and system data under the different test settings. The test building is the two-story Flexible Research Platform building at the US Department of Energy’s Oak Ridge National Laboratory, in Oak Ridge, Tennessee. Four heating tests and three cooling tests were conducted. The 1-min interval of weather, building indoor data, and system data from each test setting are provided. Actual weather data were collected from a weather station installed on the roof. This paper describes information on the test building and installed sensors, data collection method, and data validation. The provided dataset can be employed to understand HVAC system conditions and building indoor conditions under different HVAC system operations and the performance of building envelope without HVAC system operation using free-floating test data. Additionally, it can be used for empirical validation of the building energy modelling engine.

Suitability of Representative Concentration Pathway Climate Change Scenarios to Project Weather Elements in the Agro-climatic Zones of Egypt

The objective of this study was to compare between measured weather data (2010-2019) and projected data for the same period obtained from three global climate models (HadGEM2-ES, CSIRO-Mk3-6-0, and MIROC5), with its four RCPs scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) developed for the five agro climatic zones of Egypt and determine the most suitable scenario to be used in each agro-climatic zone for projection of the effect of climate change in the future on the agro-climatic zones of Egypt. Our results revealed that the four climate change scenarios developed from the three models show high level of suitability in the projection of the three studied weather elements. Climate change scenario RCP6.0 and RCP8.5 developed by HadGEM2-ES and CSIRO-Mk3-6-0 attained the highest agreement between measured and projected values of the studied weather elements. Whereas, climate change scenario RCP2.6, RCP6.0 and RCP8.5 developed by MIROC5 attained the highest agreement between measured and projected values of the studied weather elements. Thus, we recommend the use these models in the projection of the effect of climate change in the future in the agro-climatic zones of Egypt.

Urban heat island reduces annual building energy consumption and temperature related mortality in severe cold region of China

The urban heat island (UHI) phenomenon has gained increasing attention as it not only affects the energy consumption of buildings but also threatens the health of urban dwellers. In this study, we calculated the UHI intensity in major China cities across different climates based on the measured weather data of year 2019. The influences of UHI intensity on the building energy consumption and temperature-related mortalities were then quantified. Results show that the impact of UHI on urban dwellers is not always negative in terms of building energy consumption and temperature-related mortality. In most severe-cold-region cities in China, UHI reduces the annual building energy consumption caused by the cooling and heating load, because the amount of heating load reduced in winter is larger than the cooling load increased in summer. The annual temperature-related mortality is also reduced by UHI in certain cities. These reductions indicate that UHI has favourable effects in certain regions. Our findings indicate that detailed evaluation should be performed on a case-by-case basis to quantify the UHI effects on urban dwellers.

Low-altitude atmospheric lenses in arid regions

The paper presents the results of theoretical and experimental studies of low-altitude atmospheric lenses in arid and semi-arid climatic conditions. The calculations of daily, monthly and annual evaporation and evaporability from the soil were made at four points in the period from January 1, 2021 to December 31, 2021 using weather data (temperature, air humidity, wind speed), obtained from weather stations. The paper also presents the results of measurements in full-scale conditions at 46.3 north latitude, 44.2 east longitude, carried out from July 1, 2021 to October 1, 2021, where measurements of weather data obtained using a professional wireless weather station WS-2800-IT were organized, and evaporation from the soil was studied by the method of water balance of an isolated soil monolith using a soil evaporator GGI-500-50. As a result of processing data from weather stations, low evaporation values with high evaporability were revealed. According to the results of full-scale tests, local increase in evaporation values was observed mainly in areas with an effusion type of soil water regime. Hereinafter, this phenomenon will be referred to as “low-altitude atmospheric lenses”.

A Variable-Weather-Parameter Optimization Strategy Based on an Irradiance and Temperature Estimation Method for PV System

The variable-weather-parameter (VWP) methods have the fastest maximum power point tracking (MPPT) speed because their control signals are directly calculated by the measured weather data (including irradiance and temperature (I&T) data). However, they are suffering from the high hardware cost of the I&T sensors. To solve this problem, an estimation method to estimate the real-time I&T values is proposed. In this method, an equation set and two empirical equations are established to match the changeless and varying weather conditions, respectively. Based on them, a VWP optimization strategy (VWPOS) is proposed. It is without using I&T sensors (or external I&T data) that the advantage of the MPPT rapidity is inherited from the VWP methods. Finally, some simulation experiments involving the VWPOS are conducted, and the results show that the estimation method is accurate and workable regardless of the changeless or varying weather conditions. Meanwhile, simulation results also show that the tracking speed of an existing MPPT method can be greatly optimized by the VWPOS even if the I&T sensors or external I&T data are not used. In addition, simulation results still show that, when the conventional P&O method is optimized by the VWPOS, the average error of its control signal at the MPP can be decreased from 0.025% to 0.005%, and the settling time of its output power can be decreased to at least one-third of the original value. With this work, for the existing VWP methods, the trouble arising from the hardware cost of the I&T sensors can be prevented, which is beneficial to their widespread use.

Dynamic Performance Comparison of the Different PV Modules with Real Data

Nowadays, the photovoltaic panel is an important source for electricity production and one of the preferable renewable resources. Many companies have started to produce for increasing PV panel demand. It is a fact that the performance of PV panels under real environmental conditions in the application area will affect the benefit to be obtained from the application and investment to be made. In this study, Istanbul, Turkey, for the five most widely sold in the market PV panels of MATLAB/Simulink models using performance comparison minutes and measured weather data created in the environment is made. With the related study, the effects of using dynamic analysis to be obtained from more detailed simulation studies have been revealed instead of feasibility calculations to be made using only the data specified in the technical document.

Wind-driven rain exposure on building envelopes taking into account frequency distribution and correlation with different wall orientations

Excessive moisture inside building envelopes makes them vulnerable to moisture damages such as mould, algae growth, rot, corrosion, weathering, fading, etc. One of the main moisture sources for building envelopes is wind-driven rain (WDR). This paper characterizes the WDR for twenty-two locations in South Korea based on hourly measured weather data for 20 years and an artificial typical year from Meteonorm®. This study determines a wind-driven rain reference year using a semi-empirical method, taking into account the frequency of occurrence of the WDR and the correlation between the different wall orientations. The annual amount of the WDR shows a significant range from 125.4 kg/(m2∙a) to 904.4 kg/(m2∙a) with the individual prevailing orientations by location in South Korea. The deviations of the WDRs determined by the wind-driven rain reference year and the annual mean wind-driven rain can be characterized in two groups in terms of the amount and the distribution of the WDR. The first group has a characteristic that only the amount is changed while keeping the distribution across the wall orientations. The second group shows that the amount and the distribution are changing together. The wind-driven reference year can characterize the WDR by selecting and combining the most representative periods from the long-term measurements. By contrast, the artificial typical years from Meteonorm® hardly show any similar characteristics of the WDR determined with the long-term measured weather data.

Microcontroller based Multifunctional Automated Weather Monitoring and Logging System

Abstract: This paper describes a microcontroller-based prototype Automated Weather Monitoring and Logging System that can col- lect meteorological data such as air temperature, relative humidity, atmospheric pressure, light intensity, and rain detection from any distant location. The Weather Monitoring and Logging System is entirely automated, and measured weather data is transferred to a public server while showing immediate data on a liquid crystal display (LCD) and stored to a Secure Digital (SD) card. For private viewers, Android-based smart phones may be interfaced with the weather station and operated via the android application. The weather station is supplied by a direct current (DC) source, with a backup rechargeable battery. In the event of an emergency power outage, the system will immediately switch to battery power. Two Atmega 328p and Two ESP 8266 microcontrollers are utilized as the core of the control and coordination of the relative multitude of exercises of the singular modules. All of the sensors in the systems have been calibrated, ensuring that the system’s accuracy seems to be exceptional. This system will benefit all users, and it will benefit the meteorological industry because it will allow them to work from a remote location. Keywords: Automated Weather Station; Microcontroller; Sensor; Meteorological Instrument

Model for estimating the astronomical seeing at Dome A, Antarctica

A model for estimating astronomical seeing at Kunlun Station (Dome A, Antarctica) is proposed. This model is based on the Tatarskii equation, using the wind shear and temperature gradient as inputs, and a seeing model depending directly on the weather data is provided. The seeing and near-ground weather data to build and validate the proposed seeing model were measured at Dome A during the summer of 2019. Two calculation methods were tested from the measured weather data relating the wind shear and temperature gradient to a combination of the two levels for the boundary layer. Both methods performed well, with correlation coefficients higher than 0.77. The model can capture the main seeing trends in which the seeing becomes small when weak wind speed and strong temperature inversion occur inside the boundary layer.

Assessing the performance of two gridded weather data for sugarcane crop simulations with a process-based model in Center-South Brazil

High-quality measured weather data (MWD) are essential for long-term and in-season crop model applications. When MWD is not available, one alternative for crop simulations is to employ gridded weather data (GWD), which needs to be evaluated a priori. Therefore, this study aimed to evaluate the impact of weather data from two GWD sources (NASA and XAVIER), in the way that they are available for end users, on simulating sugarcane crop performance within the APSIM-Sugar model at traditional sites where sugarcane is grown in Center-South Brazil, compared to simulations with MWD. Besides, this study also evaluated the impact of replacing GWD rainfall by the site-specific measured data on such simulations. A common sugarcane cropping system was repeatedly simulated between 1997 and 2015 for different combinations of climate input. Both NASA and XAVIER appear to be interesting for applications that only require temperature and solar radiation for predictions, such as crop phenology and potential yield. Nonetheless, GWD should be used with caution for crop model applications that rely on accurate estimation of crop water balance, canopy development, and biomass accumulation, at least with crop models that run at a daily time-step. The replacement of gridded rainfall with measured rainfall was pivotal for improving sugarcane simulations, as observed for cane yield, by increasing both agreement (NASA d index from 0.67 to 0.90; XAVIER d from 0.73 to 0.93) and R2 (NASA from 0.35 to 0.76; XAVIER from 0.43 to 0.79) and reducing root mean square errors (RMSE) from 32.8 to 16.3 t/ha when simulated with other variables of NASA data and from 27.9 to 12.7 t/ha when having XAVIER data as input. Therefore, while using both GWD sets without any correction, it is recommended to replace gridded rainfall by measured values, whenever possible, to improve sugarcane simulations in Center-South Brazil.

Characterisation of the climatic temperature variations in the design of rigid pavements

ABSTRACT The design of rigid pavements in Austria is currently based on a catalogue of standard structures, which can be chosen depending on traffic-related parameters. This approach does not allow the consideration of real material characteristics, detailed traffic load information, local climate conditions, and other boundary conditions. To overcome these limitations, a new mechanistic-empirical pavement design method for rigid pavements (MEPDR) has been developed. The focus of this paper is laid on the proper characterisation of climatic boundary conditions and their impact on the design results. To simulate the actual temperature distribution within a concrete slab, a temperature prediction model was proposed and validated with measured weather data. Furthermore, representative temperature gradients were established using the proposed model and surface temperature data from measuring stations distributed over the Austrian highway pavement network. Additionally, the effect of these temperature gradients on the design life of a typical pavement structure was demonstrated using the MEPDR.