Solar Data Research Articles

Solar Radiation Intensity Imputation in Pyranometer of Automatic Weather Station Based on Long Short Term Memory

Automatic Weather Station (AWS) experienced problems in the form of component damage and communication system failure, resulting in incomplete parameter data. Component damage also occurs in pyranometers. Decreased pyranometer performance results in deviations, uncertainty in measuring solar radiation intensity, and data gaps. Data imputation is one solution to minimize measurement deviations and the occurrence of missing AWS pyranometer data. This research aims to design and analyze the accuracy performance of the multisite AWS pyranometer solar radiation intensity data imputation model when a data gap occurs. This research attempts to utilize the spatio-temporal relationship of multisite AWS solar radiation intensity in the imputation model. Long-Short Term Memory (LSTM) algorithm is used as an estimator in the multisite AWS pyranometer network. Data imputation modeling stage includes data collection, data pre-processing, creating missing data scenarios, LSTM design and model testing. Overall, LSTM-based imputation model has ability of filling gap data on AWS Cikancung pyranometer with maximum missing sequence of 12 hours. Imputation model has MAPE 1.76% - 5.26% for missing duration 30 minutes-12 hours. It still it meet WMO requirement for solar radiation intensity measurement with MAPE<8%.

Enhanced Solar Tracking System Using Internet of Things Technology

Solar energy has grown in popularity in recent years. Conventional solar panels cannot provide enough energy to meet consumer demand. Therefore, more solar panels are needed to meet consumer demand. The Internet of Things (IoT) Solar Tracker maximises solar energy collecting by connecting the physical and digital worlds. This study integrates IoT to improve solar tracker performance. Blynk uses the NodeMCU ESP8266 control unit to monitor voltage, current, and power usage. This research uses the Arduino IDE, a photoresistor sensor, and a current sensor, INA219. This work advances solar energy efficiency. By comparing solar panel electrical qualities in different environments, this research succeeded. To assess solar panel efficiency, 5 V and 12 V fans were used as loads. The results demonstrated that load configuration deliberately influences power and energy production. Solar panels produced greater power and energy under 12 V fan loads than 5 V loads. This observation assists solar energy system analysis and design. Second, the IoT solar tracker was tested for electricity harvesting. The solar tracker harvested more solar energy than a fixed orientation. After tracking the sun, it enhances voltage, current, power, and energy by 30%. Web dashboards and smartphone apps allow real-time IoT device monitoring and control. The interactive web dashboard displayed solar panel data. Track electrical properties and sunshine intensity. IoT devices influence solar tracker decisions and promote sustainable energy.Keywords: IoT solar tracker, Blynk application, NodeMCU ESP8266

Seasonal Analysis and Capacity Planning of Solar Energy Demand-to-Supply Management: Case Study of a Logistics Distribution Center

In recent years, global warming and environmental problems have become more serious due to greenhouse gas (GHG) emissions. Harvesting solar energy for production and logistic activities in supply chains, including factories and distribution centers, has been promoted as an effective means to reduce GHG emissions. However, it is difficult to balance the supply and demand of solar energy, owing to its intermittent nature, i.e., the output depends on the daylight and season. Moreover, the use of large-capacity solar power generation systems and batteries incurs higher installation costs. In order to maintain low costs, demand-to-supply management of solar energy, based on appropriate seasonal analysis of power generation and consumption and the capacity planning for power generation and the storage battery, is necessary. In this study, the on-demand cumulative control method is applied to actual power consumption data and solar power generation data estimated at a distribution center. Moreover, the monthly, seasonal, and temporal characteristics of power generation and consumption at the distribution center are analyzed. Additionally, the total amount of power purchased is investigated for solar energy demand-to-supply management.

Off-design performance evaluation of thermally integrated pumped thermal electricity storage systems with solar energy

Pumped thermal electricity storage that integrates with low-grade heat source (thermally integrated PTES, TI-PTES) have gained widespread attention due to the superior performance compared to traditional PTES systems. In this work, a TI-PTES system integrated with solar energy is constructed. R1233zd(E) is employed as the working fluid in the system. Select solar data from four typical days (Vernal Equinox, Summer Solstice, Autumn Equinox, Winter Solstice) throughout the year in Chuxiong City, Yunnan Province, which has abundant solar resources. Design conditions for the system are determined by a multi-objective optimization approach, and based on this, the off-design performance of both the charge and discharge processes are studied. The off-design models for the main components of the system are established. In the charge process, the weather conditions from 10:00 to 15:00 on a typical day are selected for a 6-hour charge period, and a parameter analysis of the system is conducted under different weather conditions. In the discharge process, the off-design performance of the system under different load levels is analyzed for two operating modes: constant pressure mode and variable pressure mode. The results indicate that in the charge process, the values of the compressor power under off-design conditions (Wcomp,off), the mass flow rates of the heat pump system (mRHP), and thermal energy storage system (mts) of Winter Solstice are the highest from 11:00 to 15:00 compared to the other 3 days. In the discharge process, the power-to-power efficiency (ηPTP) and Levelized Cost of Storage (LCOS) of the Vernal Equinox and Autumn Equinox reach their maximum (74.47 %) and minimum (0.247 $·kWh−1) values at an 80 % load level under variable pressure mode. Under constant pressure mode, the power-to-power efficiency and LCOS of the Vernal Equinox and Autumn Equinox reach their maximum (74.34 %) and minimum (0.232 $·kWh−1) values at 70 % and 40 % load levels. Comparing the off-design performance between the variable pressure mode and constant pressure mode in the discharge process, the variable pressure operation mode is recommended for load levels between 78 % and 85 %, while the constant pressure operation mode is recommended for load levels between 40 % and 78 %, as well as between 85 % and 120 %. The obtained results can serve as a reference for the off-design performance analysis of TI-PTES.

Using IoT in Double Skin Facades toward Thermal Comfort: A Review

Integrating the Internet of Things (IoT) into building design and operation has ushered in a new era of sustainable architecture and enhanced occupant comfort. This paper presents comprehensive case studies focused on the application of IoT technology in the context of double-skin facades, explicitly focusing on achieving and optimizing thermal comfort. These case studies, located in an urban setting, represent a paradigm shift in architectural design principles. Double skin facades, renowned for their potential to enhance energy efficiency and occupant well-being, are further empowered through the intelligent deployment of IoT devices. This synergy between architectural innovation and digital technology is instrumental in addressing the pressing challenges of energy consumption and occupant comfort in urbanized areas. It delves into the sensors, actuators, and data analytics systems employed to monitor and control the indoor environment. Special attention is given to the dynamic adaptation of the facade system in response to real-time environmental conditions, occupancy patterns, and user preferences. One of the case studies—PH01-BRK, projects the practical application of these principles in South Dakota’s first custom passive and net-zero home, completed in 2016, using advanced thermal analysis tools, software for heating and cooling requirements, and solar data assessment. Furthermore, the paper investigates the impact of IoT-enabled double-skin facades on thermal comfort, energy efficiency, and indoor air quality within the Brookings Passive House. Quantitative data and occupant feedback are analyzed to assess the effectiveness of the IoT-driven system in maintaining optimal thermal conditions while minimizing energy consumption. The findings of this case study underscore the potential of IoT technology in revolutionizing the design and operation of sustainable buildings. It demonstrates that integrating IoT into double-skin facades can significantly enhance thermal comfort, reduce energy consumption, and create healthier indoor environments.

Investigation of the Influence of Inner Geometries of the Receiver Tube of a Parabolic Trough Collector System Used for Water Heating

Solar collectors are devices that help to harvest solar thermal radiation and convert into other forms of energy. Because of its low fabrication cost and simple design, the Parabolic Trough Collector (PTC) is one of the finest alternatives for medium-temperature (i.e., in 80°𝐶𝐶-250°𝐶𝐶 range) needs among all types of concentrated solar collectors. The absorber tube or the receiver tube is one of the main critical components of a PTC. Thermal radiation of the sun falling onto a parabolic reflector is reflected to its focal line where the receiver tube is placed. Therefore, the performance of a PTC system significantly depends on Receiver Geometries (RGs). A locally developed PTC system with a new and accurate tracking system was designed and fabricated in the current study and steam generation inside the receiver was considered in the performance analysis. The experimental analysis was performed using seven different RGs. The experiments were carried out by measuring generated steam every 10 minutes for all the RGs, and simultaneously, solar insolation data was taken by a domestic solar Photovoltaic (PV) system with a capacity of 5 kW. The steam generation data was taken only when the power output of the reference solar PV system was ≥ 4kW. Having tested seven different RGs, the researchers were able to improve the steam generation to a maximum of 10.0% compared to the reference geometry using direct experimental data. This was 8.1% in accordance with the statistical approach via multiple linear regression model. A three-dimensional mathematical model for two-phase flow was formulated for a closed domain. Restricting this model into twodimensional geometry, the steam generation process was simulated as a TPF problem using ANSYS Fluent software. The volume fraction profiles clearly show the phase transition, which leads to evaporation and condensation inside the tube.

Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product

Gridded global horizontal irradiance (GHI) databases are fundamental for analysing solar energy applications' technical and economic aspects, particularly photovoltaic applications. Today, there exist numerous gridded GHI databases whose quality has been thoroughly validated against ground-based irradiance measurements. Nonetheless, databases that generate data at latitudes above 65˚ are few, and those available gridded irradiance products, which are either reanalysis or based on polar orbiters, such as ERA5, COSMO-REA6, or CM SAF CLARA-A2, generally have lower quality or a coarser time resolution than those gridded irradiance products based on geostationary satellites. Amongst the high-latitude gridded GHI databases, the STRÅNG model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is likely the most accurate one, providing data across Sweden. To further enhance the product quality, the calibration technique called "site adaptation" is herein used to improve the STRÅNG dataset, which seeks to adjust a long period of low-quality gridded irradiance estimates based on a short period of high-quality irradiance measurements. This study introduces a novel approach for site adaptation of solar irradiance based on machine learning techniques, which differs from the conventional statistical methods used in previous studies. Seven machine-learning algorithms have been analysed and compared with conventional statistical approaches to identify Sweden's most accurate algorithms for site adaptation. Solar irradiance data gathered from three weather stations of SMHI is used for training and validation. The results show that machine learning can substantially improve the STRÅNG model's accuracy. However, due to the spatiotemporal heterogeneity in model performance, no universal machine learning model can be identified, which suggests that site adaptation is a location-dependant procedure.

Simulation modeling of a power supply system taking into account the stochastic nature of energy generation and consumption processes

This paper presents the calculation of parameters and simulation modeling of the power supply system based on solar panels, taking into account the stochastic nature of the processes of energy generation and consumption. Through simulation in MATLAB® Simulink, the performance of the power supply system under the change of solar insolation and load power was evaluated. The method of controlling a bidirectional converter based on the calculation of the difference in Shannon entropy of energy generation and consumption flows is described. This method allows for increasing the efficiency of energy use in the system by reducing the difference between the entropy values at the input and output of the system. It also allows for reducing the duration of time intervals when the energy storage in the system is uncontrollable while being fully charged or fully discharged. Modeling of electrical processes in the power supply system was carried out with the implementing the control either considering the entropy difference or without it. To evaluate the impact of the accuracy of determining the entropy values on the modeling results, the entropy for different interval durations was calculated. The results of modeling the power supply system were analyzed using solar insolation and load power data with a 1-minute discreteness.

Influence of atmospheric conditions and solar activity on the underground karst system of the Ponor Mountain

This study focuses on the meteorological parameters of the near atmosphere, the surface of the Earth and karst areas, such as temperature of water and air, relative humidity, and wind speed. These parameters are significantly influenced by solar activity, which in turn affects the temperature distribution in karst underground cavities, specifically in the “Kolkina Dupka” cave located within the Ponor Mountain of the Western Balkan Mountains range in Bulgaria. This is the deepest and longest cave in Bulgaria with a length of more than 20 km and a calculated depth of 800 m. Meteorological data within the cave was collected using data loggers, while surface weather data was sourced from National Institute of Meteorology and Hydrology of Bulgaria. Solar activity information was obtained from the website of the Royal Observatory of Belgium. The impact of solar activity on karst regions is substantial and affects temperature, precipitation, and atmospheric circulation. Changes in solar radiation can alter heat absorption on the surface of the Earth, leading to shifts in temperature and precipitation patterns. We performed statistical analysis and modeling to understand the complex interactions between Earth's near atmosphere, the karst system of the Kolkina Dupka cave, and the role of air and water flows in regulating cave temperatures. Results showed a significant negative correlation between air temperatures in the deep underground airflows (at a depth of 130 m below the surface) and temperatures at the cave entrance (at a depth of 40 m during the winter months. Conversely, during spring and summer, an intriguing reversal occurs where higher external temperatures are linked to increased air temperatures at the cave entrance, accompanied by lower temperatures in the deeper zone. Furthermore, by employing “lagged correlations” in result analysis, investigating correlations between internal temperatures and external temperatures over the preceding and subsequent seven days, cyclic variations in heat exchange between the near-surface atmospheric layer and the underground temperatures within the karst system were observed. Specifically, a consistent temperature elevation was noted at the cave entrance three days prior to an increase in external temperatures during the spring-summer season. This temporal relationship was also observed in the solar activity data, where an increase in temperature at a depth of -40 m was registered three days prior to the escalation of solar activity within the measured range of 2800 MHz. The obtained results formed the basis for the development of new theories in solar-terrestrial physics. In summary, there is a connection between solar activity and Earth's climate, but it is not a direct and simple correlation, and it is just one piece of the larger puzzle that shapes temperature variations of the Earth. Climate science involves studying these interactions over extended periods to gain a more comprehensive understanding of the climate system of the Earth.

DSPM: Dual sequence prediction model for efficient energy management in micro-grid

Power generation and consumption predictions are fundamental for smart grid operations, addressing challenges posed by renewable energy variability and irregular consumer demand. This study introduces the Dual Sequence Predictive Model (DSPM) based on Spatiotemporal CNN (STCNN) architecture, offering a holistic solution for forecasting Electricity Generation (EG) and Electricity Consumption (EC) collectively. Leveraging STCNN, the DSPM efficiently extracts spatial and temporal information, improving prediction accuracy and processing speed. A 1D spatial attention module is added to capture crucial spatial dependencies in historical data. Incorporating shared historical weather information streamlines learning process and reduces model complexity. Extensive benchmark evaluations demonstrate the DSPM superior performance, achieving the lowest error rates compared to baseline models. The DSPM also employs the Kullback–Leibler Divergence (KLD) algorithm for power generation and consumption matching, ensuring efficient power distribution within smart and microgrids. Furthermore, the DSPM is evaluated alongside Single Sequence Prediction (SSP) models, providing a comprehensive analysis of its capabilities and comparisons with state-of-the-art models. The SSP model achieved an average RMSE reduction of 17.3% for solar EG data and 15.86% for IHEPC residential EC data, while DSPM reduced RMSE by 7.57% over PowerGrid dataset compared to baseline models.

Atomic masses with machine learning for the astrophysical r process

The astrophysical r process plays a vital role in the production of heavy elements. Modeling of the r process is sensitive to masses and further requires knowledge of masses beyond current experimental reach. Therefore, simulations of the r process offer a unique test bed for predicting mass extrapolations. We take a Machine-Learning (ML) approach to model the masses across the entire chart of nuclides. For the first time, we simulate r-process nucleosynthesis with a physics-based ML mass model. We compare simulated abundances to solar data in order to evaluate the model's performance far from stability. The resulting r-process abundances up to thorium and uranium qualitatively match those of the observed solar system abundance pattern, with the characteristic peaks well positioned. We propagate the mass uncertainties obtained from the ML model to r-process abundance yields to estimate an uncertainty band associated with our approach. The size of the uncertainty band is approximately one order of magnitude which aligns with the uncertainty reported using alternative techniques.

Wavelets in the analysis of local time series of the Earth's surface air

The practical application of local smoothing and wavelet analysis methods for studying the spectral composition and coherent relationships of local average annual surface air temperatures with solar activity and the displacement of the Earth's North Pole is presented. A preliminary analysis of local time series of surface temperatures revealed the presence of emissions and their localization. It is shown that to eliminate the influence of outliers (short-term events) on the reliability of identifying a long-term nonlinear trend, the wavelet decomposition method, which filters high frequencies, is most suitable. Functional approximation models are constructed and compared at different levels of wavelet decomposition of the data. Time or scale smoothing is used to improve the reliability of the wavelet spectrum. Based on data on average annual surface air temperatures in Yalta (44.48⁰, 34.17⁰, = 72.0 m) for the time interval from 1869 to 2022, functional models of long-term trends were built and used to obtain short-term forecasts. Information about the linear relationship of events in the compared time series is obtained and discussed in the analysis of wavelet cross-correlation, wavelet coherence and phase coherence. Local similarities were discovered between data on surface air temperature and solar activity data (Wolf numbers) for a period of ∼(30–70) years, as well as oscillations with period of 11 years, manifested in the constancy of the phase difference and an increase in the modulus of wavelet coherence power over time. Localized similarities were also found in data on surface air temperature in Yalta and in data on displacements of the Earth's mean pole relative to the conventional beginning of EOP (IERS) CO1 in the interval of periods ∼ (30–70) years.

Staring at the Sun with the Keck Planet Finder: An Autonomous Solar Calibrator for High Signal-to-noise Sun-as-a-star Spectra

Extreme precision radial velocity (EPRV) measurements contend with internal noise (instrumental systematics) and external noise (intrinsic stellar variability) on the road to 10 cm s−1 “exo-Earth” sensitivity. Both of these noise sources are well-probed using “Sun-as-a-star” RVs and cross-instrument comparisons. We built the Solar Calibrator (SoCal), an autonomous system that feeds stable, disk-integrated sunlight to the recently commissioned Keck Planet Finder (KPF) at the W. M. Keck Observatory. With SoCal, KPF acquires signal-to-noise ratio (S/N) ∼ 1200, R = 98,000 optical (445–870 nm) spectra of the Sun in 5 s exposures at unprecedented cadence for an EPRV facility using KPF’s fast readout mode (<16 s between exposures). Daily autonomous operation is achieved by defining an operations loop using state machine logic. Data affected by clouds are automatically flagged using a reliable quality control metric derived from simultaneous irradiance measurements. Comparing solar data across the growing global network of EPRV spectrographs with solar feeds will allow EPRV teams to disentangle internal and external noise sources and benchmark spectrograph performance. To facilitate this, all SoCal data products are immediately available to the public on the Keck Observatory Archive. We compared SoCal RVs to contemporaneous RVs from NEID, the only other immediately public EPRV solar data set. We find agreement at the 30–40 cm s−1 level on timescales of several hours, which is comparable to the combined photon-limited precision. Data from SoCal were also used to assess a detector problem and wavelength calibration inaccuracies associated with KPF during early operations. Long-term SoCal operations will collect upwards of 1000 solar spectra per six-hour day using KPF’s fast readout mode, enabling stellar activity studies at high S/N on our nearest solar-type star.

Detecting active latitudes of Sun-like stars using asteroseismic a-coefficients

Aims. We introduce a framework to measure the asphericity of Sun-like stars using a1, a2, and a4 coefficients and constrain their latitudes of magnetic activity. Methods. We evaluated systematic errors on the inferred coefficients in function of key physical and seismic parameters (inclination of rotation axis, average rotation, height-to-noise ratio of peaks in power spectrum). The measured a-coefficients account for rotational oblateness and the effect of surface magnetic activity. We used a simple model that assumes a single latitudinal band of activity. Results. Using solar SOHO, VIRGO, and SPM data, we demonstrate the capability of the method to detect the mean active latitude and its intensity changes between 1999 and 2002 (maximum of activity) and 2006 and 2009 (minimum of activity). We further applied the method to study the solar-analogue stars 16 Cyg A and B using Kepler observations. In 16 Cyg A, we detected an equatorial band of activity exhibiting an intensity that could be comparable to that of the Sun. However, 16 Cyg B exhibits a bimodality in a4 that is challenging to explain. We suggest that this could be a manifestation of the transition between a quiet and an active phase of activity. Validating or invalidating this hypothesis may require new observations.

Validation of decomposition models for solar irradiance at high latitudes: A preliminary study

The accurate estimation of buildings’ solar potential contributes to boost the exploitation of solar energy at high latitudes. The decomposition of global irradiation into the direct and diffuse fractions is a fundamental step of the solar irradiance model chain. Diffuse and direct irradiation are, in fact, rarely measured. Previous works recommended Yang4 as the decomposition model with the best overall performance. However, in geographically limited applications, quasi-universal decomposition models such as Yang4 and Engerer4 can be outperformed by local models (i.e., models parametrized with climate-specific data) such as Skartveit3 and Starke3. This makes necessary to perform local validation studies to verify the findings from worldwide validation studies. In this study, the four decomposition models are implemented in Python and experimentally validated against one-minute solar irradiance data (i.e., direct and diffuse irradiance) of Trondheim (Lat. 63°26’ N, Norway). Two months representative of clear sky (August) and overcast (October) conditions are considered. The study confirms that the Yang4 model performed the best for high-latitude application: the nMBE ranged from -0.54% (August) to 0.65% (October), the nRMSE from 17.18% (August) to 22.29% (October), and the R2 from 0.96 (August) to 0.97 (October). However, Skartveit3 combines a level of performance close to Yang4 with the lower number of input parameters.

Solar Radio Burst Detection Based on the MobileViT-SSDLite Lightweight Model

Real-time detection of solar radio bursts is crucial in solar physics research and space weather forecasting. However, current research on the automatic detection of solar radio bursts is limited to identifying the presence or absence of solar radio bursts or recognizing only a single type of burst, such as type II or III. Furthermore, existing methods cannot learn spectral and temporal features and often suffer from the drawbacks of large network models, resulting in slow speeds. This paper proposes an automatic recognition and localization method based on a lightweight object detection model for solar radio burst events. We collected observation data from e-CALLISTO and established a data set containing type II, III, IV, and V solar radio bursts. To address the real-time requirements of practical applications and consider the temporal and frequency domain information of spectrogram images, we improved a vision transformer with a self-attention mechanism and adopted a lightweight model for detection. The experimental results demonstrate that our proposed method achieves an average precision at a 50% intersection-over-union threshold of 78.2% and a recall rate of 92% on the established solar radio burst data set. Additionally, the model operates at a detection speed of 54.8 frames s–1, where a frame refers to a spectral image with a duration of 15 minutes, enabling efficient automated detection and localization of type II, III, IV, and V solar radio bursts.

Integration of a physics-based direct normal irradiance (DNI) model to enhance the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

Time Series Analysis and Forecasting of Solar Generation in Spain Using eXtreme Gradient Boosting: A Machine Learning Approach

The rapid expansion of solar photovoltaic (PV) generation has established its pivotal role in the shift toward sustainable energy systems. This study conducts an in-depth analysis of solar generation data from 2015 to 2018 in Spain, with a specific emphasis on temporal patterns, excluding weather data. Employing the powerful eXtreme gradient boosting (XGBoost) algorithm for modeling and forecasting, our research underscores its exceptional efficacy in capturing solar generation trends, as evidenced by a remarkable root mean squared error (RMSE) of 11.042, a mean absolute error (MAE) of 5.621, an R-squared (R²) of 0.999, and a minimal mean absolute percentage error (MAPE) of 0.046. These insights hold substantial implications for grid management, energy planning, and policy development, reaffirming solar energy’s promise as a dependable and sustainable contributor to the electrical power system’s evolution. This research contributes to the growing body of knowledge aimed at optimizing renewable energy integration and enhancing energy sustainability for future generations.

Modeling the Formation and Evolution of Solar Wind Microstreams: From Coronal Plumes to Propagating Alfvénic Velocity Spikes

We investigate the origin of mesoscale structures in the solar wind called microstreams, defined as enhancements in the solar wind speed and temperature that last several hours. They were first clearly detected in Helios and Ulysses solar wind data and are now omnipresent in the “young” solar wind measured by the Parker Solar Probe and Solar Orbiter. These recent data reveal that microstreams transport a profusion of Alfvénic perturbations in the form of velocity spikes and magnetic switchbacks. In this study, we use a very-high-resolution 2.5D MHD model of the corona and the solar wind to simulate the emergence of magnetic bipoles interacting with the preexisting ambient corona and the creation of jets that become microstreams propagating in the solar wind. Our high-resolution simulations reach sufficiently high Lundquist numbers that capture the tearing mode instability that develops in the reconnection region and produces plasmoids released with the jet into the solar wind. Our domain runs from the lower corona to 20 R ⊙, which allows us to track the formation process of plasmoids and their evolution into Alfvénic velocity spikes. We obtain perturbed solar wind flows lasting several hours with velocity spikes occurring at characteristic periodicities of about 19 minutes. We retrieve several properties of the microstreams measured in the pristine solar wind by the Parker Solar Probe, namely an increase in wind velocity of about 100 km s−1 during a stream's passage together with superposed velocity spikes of also about 100 km s−1 released into the solar wind.

Optimization of membrane thickness for proton exchange membrane electrolyzer considering hydrogen production efficiency and hydrogen permeation phenomenon

Reducing the membrane thickness of Proton exchange membrane (PEM) electrolyzer was found to efficiently promote the hydrogen production rate. However, it can also aggravate the phenomenon of hydrogen permeation, leading to an increased hydrogen content on the anode side and posing a risk of explosions. Thus, optimal design of the membrane thickness of PEM electrolyzer systems is crucial to ensure both safe and efficient hydrogen production. In this paper, we propose two optimization problems for the membrane thicknesses, aiming at achieving a balance between the hydrogen content on the anode side and the hydrogen production rate under constant and varying power input conditions. First, the theoretical model of a PEM electrolyzer is established, and a photovoltaic-PEM electrolyzer system is considered to provide varying power input conditions. Then, the two optimization problems are formulated and resolved using the sequential quadratic programming (SQP) and particle swarm optimization (PSO) algorithms, respectively. Our results reveal that the optimal membrane thickness decreases as the constant input power increases. This suggests that high/low power inputs require thin/thick membranes. For varying power input, we collected one year of solar radiation intensity data from four different regions in China. The findings demonstrate that the selection of the optimal membrane thickness depends not only on the average solar radiation intensity but also on its seasonal variations. By applying these strategies, we effectively optimize the membrane thickness in PEM electrolyzer systems, thereby enhancing the efficiency and safety of hydrogen production. The outcomes provide valuable insights for selecting the appropriate membrane thickness in regions with varying solar radiation intensities and accounting for seasonal variations in solar radiation.