Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.

Proteomic Alterations in Human Dermal Fibroblasts under Photo-Induced Pollution Caused by Excessive Solar Irradiations such as Infra-Red, Blue Light, UVA and UVB

Background: The skin serves as the first line of defense for the human body. Direct sunlight contains damaging radiations that can speed up the ageing process of the skin, resulting in wrinkles, leathery skin, dark patches, and solar elastosis. Objectives: To evaluate the effect of multiple solar irradiation related factors at the protein level in human dermal fibroblast (HDF). The overall effect of individual solar irradiations such as Infrared A (IRA), blue light (BL), UVA, and UVB on HDF cells and the extent of molecular level aberrations to be assessed and compared against each. Methods: Label-free quantitative proteomics (MS/MS) approach has been adopted in this study to observe the protein level changes induced in the HDF cells through various exposures of full light sources. Following that, downstream insilico analysis has been carried out. Results: In this study, it is demonstrated all the four different solar irradiations significantly contribute to the molecular degeneration of skin cells through various mechanisms. This study confirms that BL down-regulates DNA repair proteins and the skin cells-HDF stimulate the histone proteins as a response mechanism to maintain the chromosomal integrity. Conclusions: The proteomics experiment carried out in the current study intends to support the future sun care products based on full light protection technology that can be custom designed to provide complete protection from the solar radiation. Similar technology could enhance the further investigations for deeper understanding of induction, mode of action, and prevention of skin damage from extensive solar irradiation.

Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm

Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and also make some forecasts based on the given data. Classical machine learning has its quantum part, which is known as quantum machine learning (QML). QML, which is a field of quantum computing, uses some of the quantum mechanical principles and concepts which include superposition, entanglement and quantum adiabatic theorem to assess the data and make some forecasts based on the data. At the present moment, research in QML has taken two main approaches. The first approach involves implementing the computationally expensive subroutines of classical machine learning algorithms on a quantum computer. The second approach concerns using classical machine learning algorithms on a quantum information, to speed up performance of the algorithms. The work presented in this manuscript proposes a quantum support vector algorithm that can be used to forecast solar irradiation. The novelty of this work is in using quantum mechanical principles for application in machine learning. Python programming language was used to simulate the performance of the proposed algorithm on a classical computer. Simulation results that were obtained show the usefulness of this algorithm for predicting solar irradiation.

On the Development of Spatial/Temporal Solar UV Irradiation Maps: A Case Study in Pernambuco State (Northeast of Brazil)

This work summarizes recently published information on the solar UV broadband irradiation of Pernambuco, Northeast of Brazil. We describe the spatial and temporal distribution of solar UV radiation and its relationship with climatic and geographical conditions. Statistical experimental correlation between solar total irradiation and UV broadband obtained for 03 locations was generalized by the use of Koppen-Geiger Climatic criterium, which was used for mapping the spatial/temporal distribution of broadband UV. The climatological solar radiations used in the correlations were obtained by modeling through satellite and previously verified with terrestrial data. We present one map with the location of the recording stations where the statistical correlations were measured, one annual and 12 monthly contour maps describing monthly daily solar UV radiation levels throughout the territory of Pernambuco. The solar UV irradiation (“broadband”) annual-average daily value in the State of Pernambuco varied from 226 to 268 Wh/m2. Seasonal variation of solar UV irradiation in the State of Pernambuco follows, in general and as expected, the climate, relief and seasons of the year. The highest value of monthly solar UV irradiation was observed in the central south region of the state, more precisely in Belém do São Francisco, Floresta, Ibimirim and Buíque in the month of December (summer), with 311.8 Wh/m2. The lower value was found in the south Agreste region, in Garanhuns and Caruaru, in the month of June (winter), with 162.2 Wh/m2.

The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

The Benefits of Solar Energy on the Provision of Sustainable Affordable Housing in Nigeria

Solar energy is the most abundant form of energy on Earth. Solar energy brings impactful benefits and products that are expected to make homes more reliable, sustainable, and affordable. Thanks to technological advancements like the solar cell, we can gather this energy and turn it into electricity. The construction industry has an exceptional chance of benefiting from this sustainable energy. Many recognised benefits have been spelled forth in the construction industry, such as providing homes with clean energy with no trace of ozone depleting material emission. There are many people in Nigeria who are not linked to the public electric grid, and the energy sector produces and generates less than 58% of the entire amount of energy required. As stated in the Nigeria’s National Energy General Plan, the Sustainable Energy programme aims to enhance the country’s use of solar electricity. This paper focuses on the role of solar energy in the provision of sustainable affordable housing in Nigeria. It considers the description, method, and utilisation of solar energy with a focus on residential and commercial buildings.

Potentiality of Solar Photovoltaic Energy in Paraiba, Brazil, and the Relation with Sustainable Development

The use of non-renewable energy has been a major environmental concern and, therefore, there is a need to look for other renewable energy sources, especially photovoltaic’s. In view of this, an attempt was made to quantify the potential of solar irradiance in the State of Paraiba, as an alternative source for conversion and use in electrical energy, these determinations being the main objectives. Global solar irradiance and solar photovoltaic data were extracted from scientific publications and/or made available on the websites of the National Institute of Meteorology (INMET), the Ministry of Mines and Energy and the National Electric Energy Agency, among others. For the case study, semi-structured questionnaires were applied in different business establishments in Campina Grande, with questions related to socioeconomic aspects and photovoltaic technology. Data were analyzed using descriptive statistics criteria and using an Excel spreadsheet. The main results indicated that the Brazilian energy matrix is predominantly from renewable sources. The Northeast is the second region with the highest production of photovoltaic solar energy and the State of Paraiba occupies its fourth position in the generation of this type of energy. The option of photovoltaic technology is a promising alternative, especially for rural areas, where there is not always a conventional electricity grid. The high availability of solar energy in northeastern Brazil, in almost all months of the year, especially in the state of Paraiba, demonstrates the existence of a high potential to generate electricity from photovoltaic systems. This technology contributes to local sustainable development, as it is an activity that generates employment and income, without degrading the environment.

Analysis for Effects of Temperature Rise of PV Modules upon Driving Distance of Vehicle Integrated Photovoltaic Electric Vehicles

The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO2 emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the environment.Solar Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation systems.Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time.In this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and precipitation.Therefore,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting data.In order to perform long-term collection of weather data,Hadoop MapReduce tool is employed.The proposed solar irradiance prediction model operates on different stages.Primarily,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data normalization.Besides,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive analysis.It is divided into input layer,hidden layer,loadbearing layer,and output layer.To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is applied.In order to validate the performance of the proposed model,a series of experiments was conducted.The experimental values infer that the proposed model outperformed other methods used for comparison.

Performance of Water in Glass Evacuated Tube Solar Water Heater under Kenya Climatic Condition

Solar water heaters which provide a cost-effective and environmental friendly approach to hot water generation are in widespread application. Evacuated tube solar water heaters perform better than flat plate solar water heaters as a result of their greater surface area exposed for sunlight absorption. Water-in-glass evacuated tube solar water heaters are widely used as compared to heat-pipe solar water heaters due to their short payback periods. In this study, the performance of water-in-glass evacuated tube solar water heater is investigated through experiments under the climatic conditions in Kenya. The results revealed a daily efficiency range of 0.58 - 0.65 and a daily final outlet temperature greater than 55°C given an initial temperature of 25°C.

On the Development of Spatial/Temporal Solar Radiation Maps:A Minas Gerais(Brazilian)Case Study

Appropriate information on solar resources is very important for a variety of technological areas, such as agriculture, meteorology, forestry engineering, and water resources, particularly for an innovative technology such as solar energy. In the market entry process of an innovative technology such as solar energy, the increased and sustained deployment of this energy technology strongly depends on the economy and reliability of the solar systems installed. The economy and reliability of a system are the consequences of a well-prepared project, resulting from an accurate knowledge of the solar resource available. Therefore, knowing the potential of the solar resource accurately is not only a need but also an imperative for the larger diffusion and use of the solar energy. The existing sources of the information on radiation in MG are quite varied, both at the institutional level and in different types of publications. The publications containing this information are project reports, internal reports of institutions, and several magazines. Thus, the quality of the data varies considerably, the information presents spatial and temporal discontinuity;moreover, the instruments and the measurement units are not standardised. The general objective of this paper is to recover, to qualify, to standardize, and to make available the best information from the current existing solar resource in MG, Brazil, either in the form of isoline charts of solar radiation or a numerical database. In this paper, the procedure to elaborate the maps of daily solar radiation and insolation, along with the monthly and annual averages, is described. We present one map with the localisation of the recording stations in addition to one annual and 12 monthly contour maps. The map of the daily global solar irradiation and the annual average determined in this project show that solar radiation on the state of Minas Gerais ranges from 4.5 to 6.5 kWh/m2. The maximum values occur in the northern region of Minas Gerais, and the minimum values occur in the southeast region, where there are areas of higher altitude (Mantiqueira and Serra do Caparaó) and the rainfall is more intense, with total annual rainfall greater than 1400 mm. The North and Northeast Regions of MG, where the largest annual irradiation of 6.5 kWh/m2 occurs, are characterised by a semiarid climate with annual rainfall between 600 - 800 mm and altitudes between 400 - 600 m. The maritime tropical air mass that acts between Brazil and Africa is the main climatic factor that inhibits cloud formation and, consequently, the rainfall in this region. Finally, the map of annual insolation reveals a consistent correlation between the daily rainfall and the solar radiation annual average.

Anomalous pattern of ocean heat content during different phases of the solar cycle in the tropical Pacific

Solar radiation is a forcing of the climate system with a quasi-11-year period.As a quasi-period forcing,the influence of the phase of the solar cycle on the ocean system is an interesting topic of study.In this paper,the authors investigate a particular feature,the ocean heat content（OHC）anomaly,in different phases of the total solar irradiance（TSI） cycle.The results show that almost opposite spatial patterns appear in the tropical Pacific during the ascending and declining phases of the TSI cycle.Further analysis reveals the presence of the quasi-decadal（11-year） solar signal in the SST,OHC and surface zonal wind anomaly field over the tropical Pacific with a high level of statistical confidence（95%）.It is noted that the maximum centers of the ocean temperature anomaly are trapped in the upper ocean above the main pycnocline,in which the variations of OHC are related closely with zonal wind and ocean currents.

Impact Analysis of Solar Irradiance Change on Precision Orbit Determination of Navigation Satellites

Solar radiation pressure is the main driving force and error source for precision orbit determination of navigation satellites.It is proportional to the solar irradiance,which is the"sun constant".In regular calculation,the"solar constant"is regard as a constant.However,due to the existence of sunspots,flares,etc.,the solar constant is not fixed,the change in the year is about 1%.To investigate the variation of solar irradiance,we use interpolation and average segment modeling of total solar irradiance data of SORCE,establishing variance solar radiation pressure(VARSRP)model and average solar radiation pressure(AVESRP)model based on the built solar pressure model(SRPM)(constant model).According to observation data of global positioning system(GPS)and Beidou system(BDS)in 2015 and comparing the solar pressure acceleration of VARSRP,AVESRP and SRPM,the magnitude of change can reach 10-10 m/s^2.In addition,according to the satellite precise orbit determination,for GPS satellites,the results of VARSRP and AVESRP are slightly smaller than those of the SRPM model,and the improvement is between 0.1 to 0.5 mm.For geosynchronous orbit(GEO)satellites of BDS,The AVESRP and VARSRP have an improvement of 3.5 mm and 4.0 mm,respectively,based on overlapping arc,and SLR check results show the AVESRP model and the VARSRP model is improved by 2.3 mm and 3.5 mm,respectively.Moreover,the change of inclined geosynchronous orbit(IGSO)satellites and medium earth orbit(MEO)satellites is relatively small,and the improvement is smaller than 0.5 mm.

Fengyun-4 Geostationary Satellite-Based Solar Energy Nowcasting System and Its Application in North China

Surface solar irradiance(SSI)nowcasting(0-3 h)is an effective way to overcome the intermittency of solar energy and to ensure the safe operation of grid-connected solar power plants.In this study,an SSI estimate and nowcasting system was established using the near-infrared channel of Fengyun-4A(FY-4A)geostationary satellite.The system is composed of two key components:The first is a hybrid SSI estimation method combining a physical clear-sky model and an empirical cloudy-sky model.The second component is the SSI nowcasting model,the core of which is the derivation of the cloud motion vector(CMV)using the block-matching method.The goal of simultaneous estimation and nowcasting of global horizontal irradiance(GHI)and direct normal irradiance(DNI)is fulfilled.The system was evaluated under different sky conditions using SSI measurements at Xianghe,a radiation station in the North China Plain.The results show that the accuracy of GHI estimation is higher than that of DNI estimation,with a normalized root-mean-square error(nRMSE)of 22.4%relative to 45.4%.The nRMSE of forecasting GHI and DNI at 30-180 min ahead varied within 25.1%-30.8%and 48.1%-53.4%,respectively.The discrepancy of SSI estimation depends on cloud occurrence frequency and shows a seasonal pattern,being lower in spring-summer and higher in autumn-winter.The FY-4A has great potential in supporting SSI nowcasting,which promotes the development of photovoltaic energy and the reduction of carbon emissions in China.The system can be improved further if calibration of the empirical method is improved.

Response of photoelectron peaks in the Martian ionosphere to solar EUV/X-ray irradiance

An important population of the dayside Martian ionosphere are photoelectrons that are produced by solar Extreme Ultraviolet and X-ray ionization of atmospheric neutrals.A typical photoelectron energy spectrum is characterized by a distinctive peak near 27 eV related to the strong solar HeⅡ emission line at 30.4 nm,and an additional peak near 500 eV related to O Auger ionization.In this study,the extensive measurements made by the Solar Wind Electron Analyzer on board the recent Mars Atmosphere and Volatile Evolution spacecraft are analyzed and found to verify the scenario that Martian ionosphere photoelectrons are driven by solar radiation.We report that the photoelectron intensities at the centers of both peaks increase steadily with increasing solar ionizing flux below 90 nm and that the observed solar cycle variation is substantially more prominent near the O Auger peak than near the HeⅡ peak.The latter observation is clearly driven by a larger variability in solar irradiance at shorter wavelengths.When the solar ionizing flux increases from 1 mW·m^-2 to 2.5 mW·m^-2,the photoelectron intensity increases by a factor of 3.2 at the HeⅡ peak and by a much larger factor of 10.5 at the O Auger peak,both within the optically thin regions of the Martian atmosphere.

Development of Solar Spectroradiometer for Meteorological Observation

A newgeneration of solar spectroradiometer has been developed by CUST/JRSI to improve solarirradiance observation data under hyperspectral resolution. It is based on the grating spectroradiometer with a back-thinned CCD linear image sensor and is operated in a hermetically sealed enclosure. The solar spectroradiometer is designed to measure the solar spectral irradiance from300 nm to 1100 nm wavelength range with the spectral resolution of 2 nm( the full width at half maximum). The optical bench is optimized to minimize stray light. The Peltier device is used to stabilize the temperature of CCD sensor to 25℃,while the change of temperature of CCD sensor is controlled to ±1℃ by the dedicated Peltier driver and control circuit.

Periodic exposure to ambient solar irradiance benefits the growth of juvenile seedlings of Hizikia fusiformis

In our trials, from 2007 to 2008, of mass production of seedlings of Hizikiafusiformis using synchronization techniques, problems of a ＂dark thalli＂ phenomenon and epiphytes contamination severely threatened the health of juvenile seedlings. In this investigation, we optimized conditions for improving the growth of juvenile seedlings. Seven string collectors were seeded with zygotes and a series of experiments were conducted including direct exposure to solar irradiance, co-culture with Ulva spp. and treatment with sodium hypochlorite. It was found that direct exposure to solar irradiance （maximum： 1 740 μmol photons/（m2.s）） for 2 h per day could efficiently enhance the growth of young seedlings and simultaneously inhibit the growth of epiphytic algae. In this treatment, 50-day old seedlings could reach an average of 0.44 cm in length and possess up to nine leaflets. However, a single treatment with 18-mmol/L sodium hypochlorite for 10 rain severely harmed 15-day old seedlings. In comparison, weekly treatment with 2.2-mmol/L sodium hypochlorite for 10 rain brought no apparent harm to seedlings and eliminated epiphytic algae efficiently. However, this treatment significantly increased the detachment rate of seedlings, Inoculating Ulva spp. onto the collector caused a dramatic decrease in the number of seedlings. However, the growth of the remaining seedlings appeared unhampered. All collectors except the control were daily sprayed with a high pressure water jet from the 84 day post fertilization. From the first day to 50th day, no ＂dark thallus＂ was observed on any of the seven collectors. We believe that well-timed daily exposure to solar irradiance would favor H. fusiformis in its early growing stages.

Measuring solar irradiance profiles in the Arctic sea ice using fiber optic spectrometry via inclined holes

An irradiance profile measurement approach and profiling system were developed to measure the solar irradiance profile of the Arctic sea ice using fiber optic spectrometry.The approach involved using a miniature spectrometer to sense light signals collected and transmitted from a fiber probe.The fiber probe was small,and could thus move freely in inclined bore holes drilled in sea ice with its optical entrance pointing upward.The input-output relationship of the system was analyzed and built.Influence factors that determined the system output were analyzed.A correctional system output approach was proposed to correct the influence of these factors,and to obtain the solar irradiance profile based on the measurements outputted by this system.The overall performance of the system was examined in two ice floes in the Arctic during the 9th Chinese National Arctic Research Expedition.The measured solar irradiance profiles were in good agreement with those obtained using other commercially available oceanographic radiometers.The derived apparent optical properties of sea ice were comparable to those of similar sea ice measured by other optical instruments.

Observation of clouds and solar radiation over the Pacific Ocean as relation to global climate

During a research cruise over the Pacific Ocean in 1989, solar irradiance was measured with a broad-band pyranometer along the cruise track. Cloud cover was photographed with an all-sky time-lapse came ra. Cloud types were observed and recorded. The data show that both the types and the amounts of clouds affect radiation fluxes on the sea surface. For low-level and middle-level clouds, the correlations (r) between measured irradiance (in Percent of calculated maximum irradiance) and cloud amount (in fraction of sky) were significant: r=-0. 79 and - 0. 66, respectively. For high-level clouds, the correlation was not significant: r=-0. 21. The results indicate that cloud shortwave forcing is a major modifier of the earth's surface insolation and change of cloud amount may affect global climate.

Non-ionizing energy loss calculations for modeling electron-induced degradation of Cu(In,Ga)Se_2 thin-film solar cells

The lowest energies which make Cu,In,Ga,and Se atoms composing Cu（In,Ga）Se_2（CIGS） material displaced from their lattice sites are evaluated,respectively.The non-ionizing energy loss（NIEL） for electron in CIGS material is calculated analytically using the Mott differential cross section.The relation of the introduction rate（k） of the recombination centers to NIEL is modified,then the values of k at different electron energies are calculated.Degradation modeling of CIGS thin-film solar cells irradiated with various-energy electrons is performed according to the characterization of solar cells and the recombination centers.The validity of the modeling approach is verified by comparison with the experimental data.