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Alternative energy alternatives to traditional energy sources like coal and fossil fuels include solar PV and wind energy conversion systems. The solar and wind energy conversion system's maximum power may be obtained by activating the converters. There are several MPPT (Maximum Power Point Tracking) regulating methods for solar and wind energy conversion systems. For solar PV energy conversion systems, this study suggests two MPPT controlling techniques: Covid-19 MPPT and FLC-based MPPT. The two MPPT methods that are suggested are put into practise using MATLAB. The first Covid-19 approach that has been developed combines aspects of hill climbing and progressive conductance methods. Calculate the direction of the perturbation for the PV modules' operation using the incremental conductance approach. The method of ascending hills is straightforward and involves fewer variables. When dI/dV equals the incremental conductance, the Maximum Power Point (MPP) is attained using the incremental conductance approach. In the hill climbing approach, the MPP is determined by comparing the power in the present and the past. Both incremental conductance and change of power are taken into account in the proposed Covid-19 MPPT regulating approach to obtain the MPP. With this hybrid approach, solar PV generates the most electricity possible under all conditions of temperature and irradiance. As a result, the planned Covid-19 technique moves forward as intended and swiftly reaches the MPP.Copyright © 2022, Anka Publishers. All rights reserved.
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With the abrupt and significant drop of PM2.5 concentrations during the lockdown in 2020, hourly direct radiation (Rdir) at surface substantially increased in East China, such as Zhengzhou, Wuhan and Baoshan, with the maximum enhancement of 86% at Wuhan. Most of these stations had decreased diffuse radiation (Rdif) except Zhengzhou. Zhengzhou had both enhanced Rdir and Rdif, as well as reduced but still high PM2.5 concentrations, indicating atmospheric particles were more scattering in this region. At Beijing and Harbin in North and Northeast China, intensification of aerosol pollution led to hourly Rdir (Rdif) falling (rising) up to −28% (59%) and −23% (40%), respectively. By contrast, surface solar radiation (SSR) in West China was also greatly influenced by the elevated dust/smoke layers, revealed by aerosol layer vertical distribution and the reduction of SSR and PM2.5 concentrations. This study highlighted the importance of aerosol optical properties and vertical structures in aerosol–radiation interactions. © 2023. The Authors.
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Indoor air quality is a crucial factor when evaluating habitability, especially in developed countries, where people spend most of their time indoors. This paper presents a novel double skin façade (DSF) system that combines physical and photocatalytic filtering strategies. The air purification system is made up of fixed slats that are both solar protection and air purification system. The objective of this work is to determine the thermal behaviour of the proposed system, so that its suitability for use in various environments may be assessed. This was carried out using a physical 1:1 scale model and a computational fluid dynamics (CFD) model. The maximum temperature inside the scale model cavity was 17–20 °C higher than outdoor air. Additionally, it was discovered that the airflow through the DSF would require forced ventilation. To determine the emissivity values of the photocatalytic coating, additional experimental measurements were made. The CFD model was tested for summer and winter conditions in Barcelona, Chicago, and Vancouver. The average increase in the intake air temperature was around 14.5 °C in winter and 12 °C in summer, finding that the system has its main use potential in temperate or cold areas with many hours of solar radiation.
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Face masks are commonly used to protect an individual's respiratory system from inhaling fine particulate matter (PM2.5) in polluted air, as well as the airborne pathogens, especially during the ongoing coronavirus disease 2019 (COVID-19) pandemic. However, all conventional masks with anti-PM2.5 function suffer from insufficient facial thermal comfort, particularly in a hot and humid environment. Herein, we demonstrated a novel infrared-transmittance visible-opaque PM2.5 media for radiative cooling utilizing rutile titanium dioxide particle-embedded polyamide 6 (PA6-TiO2). The transmission of visible light and infrared and PM2.5 removal performance of composite media containing a variety of microstructures, such as TiO2 particles of varying sizes, shapes, and contents, were numerically examined to determine the optimal ranges. Then the PA6-TiO2 media was effectively electrospun by controlling the arrangement of fibers and the morphology of TiO2 particles. By transmitting more than 85% of the thermal radiation from the human body and selectively blocking solar irradiance, the developed PA6-TiO2(flower-shaped) media cooled the simulative skin by 10.3°C as compared with commercial masks under strong solar irradiance. Additionally, they demonstrated a high PM2.5 removal efficiency of 95.3%, a low air resistance of 22.5 Pa (at 5.3 cm/s), and a sound water vapor transmission rate of 0.0169 g cm−2 h−1. This study presents an effective strategy for making thermally comfortable anti-PM2.5 masks, which will significantly benefit the public health prevention and control. © The Author(s) 2022.
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This perspective article argues that anticipatory research into possible "emergency" response measures such as solar geoengineering will increase knowledge, and thus confidence, in any future decisions to either deploy or reject these technologies. Similarities between COVID and climate can reveal some perspective on the benefits of anticipatory vaccine research for anticipatory for solar geoengineering research. Although we deeply hope governments will aggressively reduce emissions and scale up adaptation efforts in time to avoid the worst climate impacts, we argue that the benefits of anticipatory solar geoengineer research currently outweigh the risks of not moving research forward.
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This study aim to improve the quality of Phyllanthus sp production by increasing the content of secondary metabolites as a bioactive compound. Phyllanthus sp contains various secondary metabolites that enhance immunity and treat diabetes, hypertension, antioxidants, anti-cancer, kidney disorders, and other illnesses. Since the Covid-19 pandemic, Phyllanthus sp widely used as a raw material for making herbal medicines. The trade value of Indonesian herbal medicines is estimated to increase in 2022, and the price will reach around the US $ 910 million, so it has very bright business prospects. These relatively limited phytopharmaceutical products constrain the supply of high-quality raw materials under the requirements of the herb medicine industry. Therefore, conducting a depth assessment related to efforts to improve the quality of Phyllanthus sp production by increasing the content of secondary metabolites is necessary. The efforts to enhance the quality of Phyllanthus sp as a source of herbal medicine raw materials can be made through plant breeding such as genetic mutations and in combination with the environmental arrangements of soil water content and solar radiation intensity. The efforts to increase the quality of herbal medicine raw materials are critical to support the development of the herbal medicine industry.
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Coronavirus disease 2019 (COVID-19) has been a pandemic disease reported in almost every country and causes life-threatening, severe respiratory symptoms. Recent studies showed that various environmental selection pressures challenge the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infectivity and, in response, the virus engenders new mutations, leading to the emergence of more virulent strains of WHO concern. Advance prediction of the forthcoming virulent SARS-CoV-2 strains in response to the principal environmental selection pressures like temperature and solar UV radiation is indispensable to overcome COVID-19. To discover the UV-solar radiation-driven genomic adaption of SARS-CoV-2, a curated dataset of 2,500 full-grade genomes from five different UVindex regions (25 countries) was subjected to in-depth downstream genome-wide analysis. The recurrent variants that best respond to UV-solar radiations were extracted and extensively annotated to determine their possible effects and impacts on gene functions. This study revealed 515 recurrent single nucleotide variants (rcntSNVs) as SARS-CoV-2 genomic responses to UV-solar radiation, of which 380 were found to be distinct. For all discovered rcntSNVs, 596 functional effects (rcntEffs) were detected, containing 290 missense, 194 synonymous, 81 regulatory, and 31 in the intergenic region. The highest counts of missense rcntSNVs in spike (27) and nucleocapsid (26) genes explain the SARS-CoV-2 genomic adjustment to escape immunity and prevent UV-induced DNA damage, respectively. Among all, the most commonly observed rcntEffs were four missenses (RdRp-Pro327Leu, N-Arg203Lys, N-Gly204Arg, and Spike-Asp614Gly) and one synonymous (ORF1ab-Phe924Phe) functional effects. The highest number of rcntSNVs found distinct and were uniquely attributed to the specific UVindex regions, proposing solar-UV radiation as one of the driving forces for SARS-CoV-2 differential genomic adaptation. The phylogenetic relationship indicated the high UVindex region populating SARS-CoV-2 as the recent progenitor of all included samples. Altogether, these results provide baseline genomic data that may need to be included for preparing UVindex region-specific future diagnostic and vaccine formulations.
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This study seeks to understand and quantify the changes in tropospheric ozone (O3) in lower troposphere (LT), middle troposphere (MT) and upper middle troposphere (UMT) over the Indo-Gangetic Plains (IGPs), India during the COVID-19 lockdown 2020 with that of pre-lockdown 2019. The gridded datasets of ozone from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis product, ERA5 in combination with statistical interpolated (IDWs) surface NO2 observations, present a consistent picture and indicate a significant tropospheric ozone enhancement over IGP during COVID-19 lockdown restrictions in May 2020. The Paper also examines the influencing role of meteorological parameters on increasing ozone concentration. Over LT, an increase in O3 concentration (23%) is observed and in MT to UMT an enhancement of about 9–18% in O3 concentration have been seen during May 2020 with respect to May 2019. An investigation on causes of increasing ozone concentration (35–85 ppbv) from MT to UMT during May 2020 reveals that there was significant rise (by 1–6%) in low cloud cover (LCC). Notably, higher LCC increases the backscattering of upward solar radiation from the top of the atmosphere. A positive difference of 5–25 W/m2 in upward solar radiation (USR) is observed across the entire study region. The result suggests that higher LCC significantly contributed to the enhanced USR. Thereby, resulting in higher photolysis rate that lead to an increase in mid tropospheric ozone concentration during May 2020. The results highlight the importance of LCC as an important pathway in ozone formation and aid in scientific understanding of it.
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We present a low‐altitude satellite survey of power line harmonic radiation (PLHR) at 50 Hz over Mainland China. We analyzed the month‐to‐month variation pattern in PLHR occurrence rate and further analyzed its correlation with some influencing factors (i.e., solar radiation, lightning flashes, and electricity consumption) using CSES satellite electric field data from 2019 to 2021. We also investigate the response of PLHR occurrence rate to COVID‐19. The statistical results show the dayside PLHR occurrence rate decreasing from winter to summer solstice and increasing from summer to winter solstice, which indicates it is controlled by the solar radiation. The nightside variation is more complex, which may be due to many sources that could influence the nightside lower ionosphere. The PLHR occurrence rate significantly decreased over Mainland China in February 2020, which is because of the significant decrease in electricity consumption due to the suspension of industrial production caused by COVID‐19.Alternate :Plain Language SummaryPower line harmonic radiation (PLHR) is the electromagnetic waves radiated by electric power systems at harmonic frequencies of 50 or 60 Hz, depending on the frequency of the system on the ground. Previous research mainly focuses on identification of individual PLHR events and their subsequent analysis. However, the number of base‐frequency PLHR signal events is the most abundant, which is suitable for the statistical study of PLHR occurrence rate and its variation pattern, and further study of the factors affecting its variation pattern. In this paper, we use 3 years of electric field data from the China Seismo‐Electromagnetic Satellite (CSES) which is an LEO satellite launched into orbit in February 2018 to investigate the month‐to‐month variation pattern of PLHR occurrence rate over Mainland China and its correlation with the influencing factors. The response of PLHR occurrence rate to COVID‐19 are also investigated.
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Objective: We sought to evaluate the impact of the coronavirus-19 (COVID-19) pandemic on sun-seeking and sun-safe behaviors. Methods: We conducted an online, cross-sectional, population-based survey. Results: In total, 1,001 respondents participated in the survey and reported being exposed to 12 or more hours of sunlight (i.e., direct and indirect ultraviolet light, and blue light) each day. Participants self-reported a net increase in all types of light exposure since the onset of the COVID-19 pandemic, especially to blue light (+38%). Notably, while the effects of direct sunlight were well known among survey respondents, they were less aware of the potential damaging impact of indirect sunlight and blue light. Limitations: As the survey was only conducted among residents of the United States, results might not be generalizable to all geographical regions. Conclusion: Social outreach strategies are required to improve sun-safe behaviors. Future behavioral interventions should encourage the implementation of broad-spectrum sun protection.
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The novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a public health emergency of international concern. Exposure to droplets produced in the coughs and sneezes of infected individuals has been perceived as the dominant transmission mode for COVID-19. However, increasingly more evidence supports claims of COVID-19 having airborne transmission. An in-depth understanding of the transmission pathways and influencing factors of SARS-CoV-2 is of great significance for formulating more effective intervention strategies. A large number of epidemiological investigations into the influence of atmospheric environmental conditions on virus transmission have been conducted. In this paper, we review current understandings of the association between COVID-19 and atmospheric environmental conditions. We first summarize the epidemiological investigations on the impact of atmospheric environmental factors (including solar radiation, temperature and humidity, wind speed, particulate matters, and gaseous pollutants) on the spread of COVID-19, and 164 epidemiological investigations are included, in which air temperature and relative humidity received the greatest attention. However, the impact of these factors on the prevalence of COVID-19 remains largely uncertain. 56% and 41% of investigations of temperature and humidity, respectively, show that cold and dry weather promotes COVID-19 transmission, while some studies come to the opposite conclusion, and still others do not show a significant relationship between them. Investigations of solar radiation are limited, but have come to the consistent conclusion that weak solar radiation is linked to increased severity of COVID-19 infection. Investigation of the impact of air pollution mainly focuses on particulate matters, and more than 70% of investigations indicate that PM2.5 likely contributes to the spread of COVID-19. Similarly, 62%, 51%, and 31% of investigations of NOx, O3, and SO2, respectively, indicate that the exposure to severe pollution can aggravate COVID-19 transmission. Therefore, the available findings reveal the complexity of the impact of atmospheric environmental conditions on the spread of COVID-19. We further discuss their mechanisms from three perspectives: (1) Atmospheric environmental conditions influence the generation of virus-laden aerosols and the occurrence of SARS-CoV-2 in the atmosphere. Relative humidity can affect the evaporation process of water on virus-laden aerosol, and thus affect its atmospheric life and probability of being inhaled by human body. (2) Atmospheric environmental conditions directly affect the stability of infection activity of SARS-CoV-2. Generally, high temperature, medium relative humidity, and intense solar radiation promote the inactivation of SARS-CoV-2. (3) Atmospheric environmental conditions indirectly affect the infection ability of SARS-CoV-2 by changing the defense ability of host cells. Air pollutants, especially PM2.5, can affect human susceptibility to the virus by increasing the expression of the SARS-CoV-2 receptor (angiotensin converting enzyme 2) in host cells. Meanwhile, meteorological conditions and air pollution can lead to respiratory system and other diseases in the human body, thus reducing human immunity and increasing the risk of virus infection, as well as the numbers of severely infected and fatal cases. All three mechanisms may contribute to the prevalence of COVID-19, but the dominant mechanism remains unclear. Finally, future directions of in-depth studies regarding the association between the epidemic and atmospheric conditions are proposed. © 2022 Chinese Academy of Sciences. All rights reserved.
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Climate science depends upon accurate measurements of air temperature and humidity, the majority of which are still derived from sensors exposed within passively ventilated louvred Stevenson-type thermometer screens. It is well-documented that, under certain circumstances, air temperatures measured within such screens can differ significantly from “true” air temperatures measured by other methods, such as aspirated sensors. Passively ventilated screens depend upon wind motion to provide ventilation within the screen and thus airflow over the sensors contained therein. Consequently, instances of anomalous temperatures occur most often during light winds when airflow through the screen is weakest, particularly when in combination with strong or low-angle incident solar radiation. Adequate ventilation is essential for reliable and consistent measurements of both air temperature and humidity, yet very few systematic comparisons to quantify relationships between external wind speed and airflow within a thermometer screen have been made. This paper addresses that gap by summarizing the results of a 3-month field experiment in which airflow within a UK-standard Stevenson screen was measured using a sensitive sonic anemometer and comparisons made with simultaneous wind speed and direction records from the same site. The mean in-screen ventilation rate was found to be 0.2 m s-1 (median 0.18 m s-1), well below the 1 m s-1 minimum assumed in meteorological and design standard references, and only about 7 % of the scalar mean wind speed at 10 m. The implications of low in-screen ventilation on the uncertainty of air temperature and humidity measurements from Stevenson-type thermometer screens are discussed, particularly those due to the differing response times of dry- and wet-bulb temperature sensors and ambiguity in the value of the psychrometric coefficient.
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As the coronavirus pandemic has brought about global economic recession and reduction in greenhouse gas emissions, energy efficient building retrofitting has become a comprehensive solution to increase the employment rate and reduce the energy consumption of buildings. This situation requires more energy-efficient integrated generation systems. In this study, an integrated generation system is proposedfor building integrated photovoltaic, thermoelectric generator, and phase change material as an enhanced generation system for buildings. In the proposed system, the phase change material absorbs solar radiation as latent heat within the melting temperature, increasing the photovoltaic conversion efficiency. Additionally, the thermoelectric generator harvests additional electricity as the temperature difference is maintained during the phase change. The total generated energy of the proposed system highly depends on the melting temperature and thickness of the phase change material. Therefore, the appropriate melting temperature and thickness design conditions of the phase change material were derived with the following simulations based on transient energy balance equations in 12 daily profiles. As a result, the optimal melting temperature increased by 5.4°F (3.6°C) and 1.9°F (1.04°C) with an insolation increase of 317 Btu/ft2 (1000 Wh/m2) and a 1.8°F (1°C) increase in ambient temperature, respectively. In addition, the optimal thickness increased by 0.04 in (2.5 mm) with an insolation increase of 317 Btu/ft2 (1000 Wh/m2).
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The COVID-19 epidemic-led lockdown (LD) from March 25 to May 31, 2020, had a different level of impact on air quality in the ecologically sensitive region of northeast India, even though the restriction on main anthropogenic activities was expected to reduce particulate matter concentration. The daily average black carbon concentration measured at 880 nm (BC880) was 1.5–15.6 μg m−3 (mean: 5.75±4.24 μg m−3) during the measurement period. It was 9.29±4.11 μg m−3 during pre-LD (February 12–March 21), 4.70±0.95 μg m−3 during LD1 (March 25–April 14), 3.41±0.56 μg m−3 during LD2 (April 15–May 3), 3.69±1.50 μg m−3 during LD3 (May 4–17), 2.94±0.93 μg m−3 during LD4 (May 18–31), and 6.56±5.35 μg m−3 during the Post-LD (June 6–July 3) of 2020. It decreased up to 68% during the lockdowns. The source apportionment based on an improved method showed a significant improvement in the contribution of BC880 sources. The radiation effect determined by Angstrom Absorption Exponent showed that brown carbon accounted for 25% of the aerosol light absorption at 370 nm during the lockdown period. Relative humidity correlates substantially with BC880, while rainfall, temperature, and solar radiation were negatively correlated. The bivariate analysis showed the dominance of local emissions in the BC880 concentrations.Research highlightsBlack carbon concentration decreased up to 68% during the different phases of lockdown.BC associated with fossil fuel was 51–78%, and biomass burning was 22–49%.The fraction of fossil fuel and biomass burning in whole BC fallen to 0.73 and 0.65 during the lockdowns.Air quality improved by about 47–58% on the 4th and 7th day of lockdown.Brown carbon and meteorological parameters significantly impacted aerosol light absorption in this region.
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BACKGROUND: We recently showed that seasonal patterns of COVID-19 incidence and Influenza-Like Illnesses incidence are highly similar, in a country in the temperate climate zone, such as the Netherlands. We hypothesize that in The Netherlands the same environmental factors and mobility trends that are associated with the seasonality of flu-like illnesses are predictors of COVID-19 seasonality as well. METHODS: We used meteorological, pollen/hay fever and mobility data from the Netherlands. For the reproduction number of COVID-19 (Rt), we used daily estimates from the Dutch State Institute for Public Health. For all datasets, we selected the overlapping period of COVID-19 and the first allergy season: from February 17, 2020 till September 21, 2020 (n = 218). Backward stepwise multiple linear regression was used to develop an environmental prediction model of the Rt of COVID-19. Next, we studied whether adding mobility trends to an environmental model improved the predictive power. RESULTS: Through stepwise backward multiple linear regression four highly significant (p < 0.01) predictive factors are selected in our combined model: temperature, solar radiation, hay fever incidence, and mobility to indoor recreation locations. Our combined model explains 87.5% of the variance of Rt of COVID-19 and has a good and highly significant fit: F(4, 213) = 374.2, p < 0.00001. This model had a better overall predictive performance than a solely environmental model, which explains 77.3% of the variance of Rt (F(4, 213) = 181.3, p < 0.00001). CONCLUSIONS: We conclude that the combined mobility and environmental model can adequately predict the seasonality of COVID-19 in a country with a temperate climate like the Netherlands. In this model higher solar radiation, higher temperature and hay fever are related to lower COVID-19 reproduction, and higher mobility to indoor recreation locations is related to an increased COVID-19 spread.
Subject(s)
COVID-19 , Rhinitis, Allergic, Seasonal , COVID-19/epidemiology , Humans , Netherlands/epidemiology , Pollen , Rhinitis, Allergic, Seasonal/epidemiology , SeasonsABSTRACT
The profile of urban microclimates is important in many engineering fields, such as occupant’s thermal comfort and health, and other building engineering. To predict the profile of urban microclimate, this study applies the artificial neural network and long short-term memory network predictive models, and an urban microclimate dataset was obtained with a long-term monitoring from year 2017 to 2019 with 5-min resolution including temperature, relative humidity, and solar radiation. Two predictive models were applied, and the first (Model 1) is to apply the predictive techniques to predict the urban microclimate in the real-time sequence, and then extract the characteristics of urban microclimate, while the second (Model 2) is to directly extract the characteristics of the microclimate, and then predict the characteristics of the microclimate. Backpropagation artificial neural network (BP-ANN) and long-short term memory (LSTM) techniques were applied in both models. The results show Model 1 with as the time-series prediction can reach the best (99.92%) of correlation coefficient and 98% of the mean average percentage error (MAPE), for temperature, while 99.66% and 98.18% for relative humidity, respectively, while accuracies in Model 2 decreased to 79% and 88.6% of MAPE for temperature and relative humidity, respectively. The prediction of solar radiation using ANN and LSTM are 51.1% and 57.8% of the correlation coefficient, respectively.
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Using statistical models, the average hourly ozone (O3) concentration was predicted from seven meteorological variables (Pearson correlation coefficient, R = 0.87–0.90), with solar radiation and temperature being the most important predictors. This can serve to predict O3 for cities with real time meteorological data but no pollutant sensing capability. Incorporating other pollutants (PM2.5, SO2, and CO) into the models did not significantly improve O3 prediction (R = 0.91–0.94). Predictions were also made for PM2.5, but results could not reflect its peaks and outliers resulting from local sources. Here we make a comparative analysis of three different statistical predictor models: (1) Multiple Linear Regression (MLR), (2) Support Vector Regression (SVR), and (3) Artificial Neuronal Networks (ANNs) to forecast hourly O3 and PM2.5 concentrations in a mid-sized Andean city (Manizales, Colombia). The study also analyzes the effect of using different sets of predictor variables: (1) Spearman coefficients higher than ± 0.3, (2) variables with loadings higher than ± 0.3 from a principal component analysis (PCA), (3) only meteorological variables, and (4) all available variables. In terms of the O3 forecast, the best model was obtained using ANNs with all the available variables as predictors. The methodology could serve other researchers for implementing statistical forecasting models in their regions with limited pollutant information.
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In this article, the legal and regulatory concepts for the use of renewable energy in the construction industry are reviewed, relying on some laws in a number of countries, which were taken into account as a model for its application in Iraqi cities. In this article, we have highlighted the problems and prospects for the application of renewable energy in general, including marine energy represented by wave, tidal energy, and its predictability. Also, in this article, we put a brief study on concentrated solar energy and the technology used to generate electricity using energy from solar radiation through the technology known as CSP. In summary, we reviewed the most important challenges that faced renewable energy projects as a result of the outbreak of the Corona virus, Covid-19, the health crisis that led to hundreds of victims among the cadres working in this field, and thus the energy sectors were directly affected as a result of the tightening of preventive measures and the budgets of many countries.
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Globally evolving COVID-19 pandemic has raised major questions which may have catastrophic implications like absence of universal facemask use, misunderstanding implications of SARS-CoV-2 test results, ventilator related mortality, cytokine reduction technology and anti-viral treatments being in their infancy still, failure to update advanced healthcare directives during pandemic, and overlooked home hospice options for COVID-19 patients when terminally ill. Moreover, there are inquisitive and interesting avenues worth exploring and innovating during COVID19 pandemic like "cold" viruses such as SARS-CoV-2 uniquely choosing airways which normally and naturally have temperatures much lower than core body temperatures, potential therapeutic role (if any) of facemask usage, potential role of natural disinfection by sunlight and its component ultraviolet-C which is used for artificial cleansing, potential bimodal immune response against SARS-CoV-2, and exploration into BCG vaccination based non-specific protection against intracellular pathogens with SARS-CoV-2 itself being an intracellular pathogen. Summarily, I am praying that the natural delays in establishing reproducible evidence during COVID-19 pandemic should not turn the humanity as we know today into a historical evidence.
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We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors.