Horizontal Global Solar Radiation Research Articles

Medium-term forecasting of global horizontal solar radiation in Brazil using machine learning-based methods

The generation of electric energy through renewable sources, such as solar photovoltaic (PV) systems, has emerged as one solution to the climate change crisis. To avoid fluctuations in energy generation on the electricity grid, reliable and accurate forecasts are required. Statistical downscaling methodologies often rely on data from General Circulation Model (GCM) to provide information on the local level. In contrast to traditional downscaling approaches, the present study focuses on a data-driven approach using the Global Data Assimilation System (GDAS) database in three different ways to feed the machine learning (ML) models, based on boosting and bagging, to provide medium-term Global Horizontal Irradiance (GHI) forecasts, i.e. up to seven days ahead. In addition, feature selection and feature augmentation techniques are also applied. The results showed that Extra Trees method outperformed the other tested in the present study, with a RMSE of 99.6 and 137.6 W/m2, and correlation 0.31 and 0.52 for June and October, respectively. Thus far, this work marks the initial suggestion of incorporating GDAS analysis data into a data-driven forecasting approach employing bagging and boosting models. This innovative approach demonstrates the potential for integrating global-scale data assimilation systems with local measurements using ML techniques. The outcomes of this research contribute to advancing the field of traditional downscaling methods and provides more robust and specialized forecasting.

DWT-BILSTM-based models for day-ahead hourly global horizontal solar irradiance forecasting

AbstractAccurate forecasting of electricity generation from renewable energy sources is crucial for the operation, planning and management of smart grids. For reliable planning and operation of photovoltaic (PV) systems in grid-connected or islanded utilities, an hourly day-ahead forecast of PV output is critical. The forecast of PV power can be done indirectly by estimating solar irradiance. For forecasting day-ahead hourly global horizontal irradiance (GHI), two forecasting models with different multivariate inputs are proposed in this paper, and the results are compared. These models use a hybrid algorithm of discrete wavelet decomposition and bidirectional long short-term memory (BILSTM). The inputs of the first model contain GHI and weather type data. The other model allows for observation of the effect of meteorological values including GHI, temperature, humidity, wind speed, and weather type data. The forecasting performance of deep learning algorithms which contain recurrent neural network (RNN), long short-term memory (LSTM), and BILSTM algorithms for day ahead hourly solar irradiance forecasting problems are also compared. To evaluate the performance of proposed models, two datasets are used for Model 1 and one dataset is used for Model 2. An experiment is also done to demonstrate that the proposed Model 1 is applicable in datasets collected in the vicinity of the city of Trabzon. On the other hand, BILSTM algorithm outperforms RNN and LSTM algorithms. It is seen that the test successes of both proposed models are better than the results given in the literature.

GEOSPATIAL ANALYSIS OF THE POTENTIAL OF THE TERRITORIES OF UKRAINE FOR THE LOCATION OF SOLAR POWER PLANTS BASED ON SATELLITE DATA

Climate change necessitates the relevance of renewable energy utilization worldwide. The Green Deal defines the energy development policy in Europe until 2030. This challenge holds particular significance for Ukraine in the context of post-war energy infrastructure recovery. Therefore, an important issue is the analysis of the suitability of Ukrainian territories for the installation of large-scale solar power plants (solar farms) and the optimization of their placement. This research aims to determine the suitability of Ukrainian territories for solar power plant placement using satellite data on climate and terrain characteristics. Among the factors determining the suitability of the territory for solar farms, the greatest impact lies in climatic indicators, including data on the total global horizontal solar irradiation (GHI), accumulated temperature above 25 °C at a height of 2 meters, average annual wind speed, and map of accumulated annual precipitation from the ERA5-Land dataset. In this study, terrain maps containing information on elevations, slopes, and terrain shading from the Shuttle Radar Topography Mission (SRTM) project were also used to determine the suitability of the territories. The suitability of the territories is determined through geospatial analysis using weighted sums. Based on the research results, a suitability map was constructed, depicting the distribution of zones with different suitability coefficients (ranging from 0 to 1). It was found that a significant portion of Ukraine’s territory is favorable for the placement of solar power plants. Over 48 % of the country has moderate suitability values (0.3…0.4). The obtained suitability map was used to analyze the optimal placement of already constructed major solar power plants in Ukraine. Data from Wikimapia was utilized to determine the locations of these energy facilities. Overall, all the analyzed large-scale solar power plants in Ukraine were situated in optimal territories. The research also revealed that certain regions, such as Odesa, Poltava, Kharkiv, Zaporizhzhia, Dnipro, Donetsk, and Luhansk, have good suitability values (0.3…0.4) but are not fully utilized. These areas have significant potential for the future construction of powerful and productive solar power plants

Influences of Cloud Microphysics on the Components of Solar Irradiance in the WRF-Solar Model

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance.

Genç kadınlarda aylık ortalama küresel yatay güneş radyasyonu ve güneşlenme süresinin vitamin d düzeyine etkisi

Aim: The most important reason for vitamin D deficiency is the lack of synthesis in the skin. The synthesis of vitamin D can be affected by many variables such as geographical region, race, season, monthly average daily sun exposure (MADSD), monthly average daily global horizontal solar radiation (MADGHSR). In this study, we aimed to investigate possible association between vitamin D levels and MADSD and MADGHSR in young women. Methods: This is a retrospective study evaluating the levels of Vitamin D classified by age, seasons, months, MADGHSR and MADSD in women aged between 15-45 years admitted to a secondary state hospital. Results: All of the women involved were at reproductive age and approximately 94.6% of them had vitamin D levels below 30 ng/mL. The median (minimum-maximum) vitamin D level was 16.1 (3.6-49.4) ng/mL in summer and 14.3 (3.2-49.8) ng/mL in winter (p=0.001). The rate of vitamin D deficiency (<20 ng/mL) was 68.1% in summer and 75.1% in winter (p<0.001). The median MADGHSR was 4.6 (3.4-5.7) hours in winter and 6.9 (5.2-7.2) hours in summer and the median MADSD was 3.3 (1.6-3.5) watt/m²/day in winter and 15.2 (12.2-15.8) watt/m²/day in summer. Vitamin D level was weakly correlated with age (r=0.082, p=0.002), MADSD (r=0.075, p=0.001) and MADGHSR (r=0.062, p=0.006). Conclusion: We found that MADGHSR and MADSD had an effect on vitamin D synthesis in addition to factors related with personal and environmental situations. We suggest that routine optimal dose vitamin D replacement is necessary in geographies similar to the population in our study.

Harnessing solar power: Predicting photovoltaic potential in fiche, oromia, ethiopia with artificial neural networks

In developing nations like Ethiopia, especially in rural and semi-urban regions, reliable access to electricity through grid-based systems is often limited or unavailable due to inadequate energy infrastructure. To tackle this issue, photovoltaic (PV) technology plays a crucial role in converting solar energy into electricity, especially in remote regions. This study focuses on assessing the daily and monthly energy exposure of PV arrays located near Fiche town, Ethiopia. The study utilizes four network types - Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg-Marquardt (LR) - with eight input variables and a training-testing split of 70%-30% to estimate global solar radiation (GSR). Hidden and output layers are employed with a sigmoid and linear function as activation functions, respectively. The results reveal that the expected mean daily and monthly horizontal global solar radiation (HGSR) ranges between 3.282 kWh/m2/day ≤ HGSR ≤ 6.967 kWh/m2/day and 4.628 kWh/m2 ≤ HGSR ≤ 6.613 kWh/m2, respectively. Furthermore, the study determines that the daily energy delivered to the PV system and available from the PV array to load falls within the range of 0.656 kWh/m2 ≤ Ed ≤ 1.394 kWh/m2 and 0.567 kWh/m2 ≤ El ≤ 1.205 kWh/m2, respectively. The average results for the study period from 2016 to 2021 were 1.175 kWh/m2 and 1.015 kWh/m2 for the Ed and El, respectively. These results demonstrate that PV arrays near Fiche town have abundant solar energy resources, making them highly attractive for electricity generation. Furthermore, utilizing solar energy from PV arrays can help mitigate indoor pollution in the rural communities surrounding Fiche town.

Learning lessons from Murzuq-Libya meteorological station

In this study, an examination was conducted on weather data gathered from the Murzuq weather station over a period of nine months, specifically focusing on 15-minute time series solar radiation data. The data was sourced from the Center for Solar Energy Research and Studies in Tajoura-Tripoli, through a collaborative agreement between the Faculty of Engineering at Wadi Alshatti University and the research center. The information collected encompassed various solar radiation components, such as global horizontal solar radiation, direct normal radiation, sky-diffuse solar radiation, and ground reflected solar radiation. The aim of this study is to verify calculated values of these components using mathematical models by comparing them with their measured values. The investigation revealed that the Earth's reflectance value for the region was estimated and determined to be around 0.4. It is important to note that this figure was different from the typically advised value of 0.2 that was given in previous literature.

Design and Development of Grid-Connected Solar PV Power Plant Using PVsyst

As we know, the biggest problem that threatens today's world is climate change and global warming. Alongside that, Afghanistan does not have a reliable source of power, and people who live in cities do not have full access to electricity. So, we need immediate access to electricity. Therefore, among all other renewable sources, the only one that is feasible in the target area and could solve this problem soon is investing in a solar PV power plant. This study aims to develop a standard procedure for the design of grid-connected solar PV systems using PVsyst software. The project began with a broad database of meteorological data, including global daily horizontal solar irradiance, and also a database of various renewable energy system components from different manufacturers. This paper will explain the grid solar power limited in the year 2023. The photovoltaic power plant has a solar radiation of 6.22 KWh/Sq./day, covering 162.66 acres of land. The operating module temperature varies from -40°C to 85°C, with a tilt angle of 32 degrees. The various power losses (PV losses due to irradiation level, temperature, soiling, inverter, wiring, power electronics, grid availability, and interconnection) amount to 2006 KWh/m2, with a total production of 60513314 KVAh/year, and the performance ratio is calculated from simulation, giving an annual PR of 75.16%.

On-Grid Mono-Si PV Solar System Performance Evaluation in Baghdad

Abstract The performance assessment of a 5 kWp On-Grid Mon-crystalline silicon photovoltaic( PV) solar system is the subject of the present paper. This PV system is located in Baghdad city, Al-Mansour company. The present PV solar system is observed during 2018. The current system’s yearly daily averages for the array yield final yield, and the reference yield are 4.65 kWh/kWp, 4.5 kWh/kWp, and 5.9 kWh/kWp, respectively. The final yield’s yearly energy is 1613.8 kWh/kWp. The present year’s electrical energy output from this system is 7891.57 kWh, and Baghdad’s annual global horizontal solar irradiation (GHI) is 1987.5 kWh/m2. The average yearly daily losses for the system and the array are 0.15 kWh/kWp and 1.33 kWh/kWp, respectively. The average yearly efficiencies for the system, the array and the inverter are 11.33%, 11.7%, and 96.75%, respectively. The annual daily averages for performance ratio and capacity factor are 74.7% and 17.75 %, respectively. Mono-crystalline silicon (Mono-Si) seems to operate well in the environment of Baghdad-Iraq.

A comprehensive methodological workflow to maximize solar energy in low-voltage grids: A case study of vertical bifacial panels in Nordic conditions

The large-scale deployment of solar photovoltaic (PV) panels in residential and commercial buildings affects the local distribution grid. Voltage rises during times when PV production exceeds consumption can limit the hosting capacity of the distribution grid. This study presents a comprehensive methodological workflow that moves from the solar analysis of an ideal district to the identification of the PV hosting capacity of a distribution grid. The workflow aims to be highly flexible: the input parameters (i.e., PV technology, PV orientation, global horizontal solar irradiation, and grid properties) can be varied to simulate different cases and compare the corresponding hosting capacities. This workflow enables the analysis of the influences of PV systems’ properties and orientation, electricity consumption, and grid characteristics on a grid’s hosting capacity. A case study was conducted in which the IEEE European Low Voltage Test Grid was used, and conventionally mounted monofacial photovoltaic (MPV) panels were progressively replaced with east–west oriented, vertically mounted bifacial photovoltaic (VBPV) panels that can provide a better match with the electricity load and a higher daily total to daily peak electricity production ratio. The benefits of VBPV are highlighted at high-latitude locations, such as Nordic countries. Therefore, Turku, Finland, which is at a representative Nordic latitude considering the locations of the most important population centers, was chosen as the location for the case study. The results showed that applying VBPV increased a grid’s PV hosting capacity significantly, up to 46% with the optimal system (30% MPV and 70% VBPV).

A New Hybrid Short Term Solar Irradiation Forecasting Method Based on CEEMDAN Decomposition Approach and BiLSTM Deep Learning Network with Grid Search Algorithm

An accurate and efficient forecasting of solar energy is necessary for managing the electricity generation and distribution in today’s electricity supply system. However, due to its random character in its time series, accurate forecasting of solar irradiation is a difficult task; but it is important for grid management, scheduling and its balancing. To fully utilize the solar energy in order to balance the generation and consumption, this paper proposed an ensemble approach using CEEMDAN-BiLSTM combination to forecast short term solar irradiation. In this, Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) extract the inherent characteristics of time series data by decomposing it into low and high frequency Intrinsic Mode Functions (IMF’s) and Bidirectional Long Short Term Memory (BiLSTM) used as a forecasting tool to forecast the solar Global Horizontal Irradiance (GHI). Furthermore, using extensive experimental analysis, the research minimizes the number of IMF’s by integrating the CEEMDAN decomposed component (IMF1–IMF14) in order to increase the prediction accuracy. Then, for each IMF subseries, the trained standalone BiLSTM network are assigned to carry out the forecasting. In last stage, the forecasted results of each BiLSTM network are aggregate to compile final results. Two year data (2012–13) of Delhi, India from National Solar Radiation Database (NSRDB) has been used for training while one year data (2014) used for testing purpose for the same location. The proposed model performance is measured in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Correlation coefficient (R22) and forecast skill (FS). For the comparative analysis of proposed model, several others models: persistence model, unidirectional deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), BiLSTM and two CEEMDAN based BiLSTM models are developed. The proposed model achieved lowest annual average RMSE (18.86 W/m22, 22.24 W/m22, 26.25 W/m22) and MAPE (2.19%, 4.81%, 6.77%) among the other developed models for 1-hr, 2-hr and 3-hr ahead solar GHI forecasting respectively. The maximum correlation coefficient (R22) obtained by the proposed model is 96.4 for 1-hr ahead respectively; on the other hand, forecast skill (%) of 89% with reference to benchmark model. Various test such as: Diebold Mariano Hypothesis test (DMH) and directional change in forecasting (DC) are used to analyze the sensitivity with reference to the difference in forecasted and observed value.

Agricultural Grid Connected Photovoltaic System Design and Simulation in Egypt by using PVSYST Software

Agricultural Photovoltaic Systems are a key technology to achieve sustainable development goals by reducing competition between land for food and electricity. In addition, Agricultural Photovoltaic Systems are at the heart of the link between power generation, crop production and irrigation water conservation. The main ecophysiological constraint on crop production under photovoltaics is the reduction of light. It is difficult to recommend shade tolerance for some plant varieties due to insufficient information on shading conditions for most plants. The use of shading panels (photovoltaic panels) requires more crop-specific research to determine the optimal percentage of panels and their placement that will not reduce agricultural yields. Crop yield variation versus field shading and availability to maximize the system require extensive research. This study aims to develop a standard procedure for designing an agricultural grid-connected photovoltaic power generation system for solar power generation in an agricultural area in Bahteem, Egypt. The technical and annual performance of the grid-connected PV system was simulated using PV Syst software. The paper started with a pre-feasibility study of a grid-connected photovoltaic system using PV Syst. Software with an extensive database of meteorological data, including global daily horizontal solar irradiance, and a database of various renewable energy system components from different manufacturers. In this work, a comprehensive literature review of agricultural solar photovoltaic systems is conducted, with a particular focus on grid-connected systems, followed by a design procedure for grid-connected solar photovoltaic systems. The planned photovoltaic system will generate a total of 400 KWp of electricity. This generated electricity can drive down electricity prices by exporting excess electricity to the national grid. In addition, solar power systems are fuel-efficient and have a low environmental impact.

Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in Northern Cyprus: A Case Study

In this paper, an in-depth analysis of small-scale PV in Northern Cyprus is conducted for the first time at 37 locations in Northern Cyprus. No previous study has investigated the viability of off-grid PV systems with various sun-tracking systems in Northern Cyprus. In order to achieve this, NASA POWER data were used for the evaluation of the solar resource in the selected locations. The results showed that the selected locations are suitable for the installation of various scales of PV systems due to the high global horizontal solar radiation. The mathematical modeling method was utilized for the design and analysis of PV systems with various sun-tracking systems and for the assessment of their economic viability and feasibility. Energy production, capacity factor, payback period, and cost of energy production were calculated. The results indicate that the proposed systems are very promising for all the selected locations. The PV projects with a 2-axis sun-tracking system produce a large amount of energy and have a low electricity cost. It was found that the electrical energy cost of the developed systems was within the range of 0.4851-0.6641TL/kWh. The payback periods varied from 4.57 years to 8.49 years, depending on the type of solar PV panel and sun-tracking system. This study provides some useful recommendations for decision-makers regarding the development and deployment of PV energy technology in the country in order to achieve sustainable development goals.

Techno-Economic Feasibility Assessment for the promotion of Grid-Connected Rooftop PV Systems in Botswana: A Case Study

The main aim of the present study is to investigate the solar energy potential and evaluate the economic viability of a 5kW grid-connected rooftop photovoltaic (PV) system as an electricity generation source in three selected regions (Gaborone, Maun, and Tshabong) in Botswana for the first time. In this study, NASA POWER data were used for evaluating the solar potential in the selected regions. The results showed that the selected locations are suitable for the installation of various scales of PV systems due to the high global horizontal solar radiation. RETScreen Expert software was used to assess the techno-economic feasibility of the proposed systems. The performance of the proposed systems with various PV technologies (mono-crystalline silicon and poly-crystalline silicon) is analyzed. Furthermore, economic and financial indicators such as net present value, annual life cycle savings, payback, benefit-cost ratio, and cost of energy production were calculated. The results indicate that the proposed system is very promising for all the selected locations. Additionally, it was found that the PV projects with poly-Si technology produced a large amount of energy and have a low electricity cost compared to mono-Si technology. The results suggest that grid-connected rooftop PV systems have a significant role in covering the electricity demand and in reducing carbon dioxide emissions, especially in high population density and rural regions. This study provides some useful recommendations for decision-makers regarding the development and deployment of PV energy technology in Botswana.

Hourly and sub-hourly ahead global horizontal solar irradiation forecasting via a novel deep learning approach: A case study

In this paper, a Long Short-Term Memory Neural Networks (LSTM) Deep Learning Approach is proposed to forecast Global horizontal solar Irradiation (GHI) in Erfoud Moroccan city. The main objective is to predict simultaneously hourly and sub-hourly GHI for time horizons from h + 1 to h + 6, and from 10 min + 10 to 10 min + 60, based only on endogenous historical data. For this purpose, two scenarios have been proposed, an annual performances scenario to consider all meteorological conditions of the year and seasonal performances scenario to distinguish the meteorological conditions characterizing each season of the year. In addition, LSTM performances are compared to those of the Multi-Layers Perceptron Artificial Neural Networks (MLP-ANN) and Random Forest (RF) ensemble method. The annual performances results show that LSTM models are the most efficient and predict precisely hourly and sub-hourly GHI for all time horizons against ANN and RF models with nRMSE varies from 11.63% for h + 1 to 17.35% for h + 6, and from 11.28% for 10 min + 10 to 14.88% for 10 min + 60. As well, the improvement in terms of nRMSE due to LSTM utilization compared to ANN and RF is more important with the forecast horizon increase. This study also reveals that sub-hourly GHI prediction performances are more accurate than for those of hourly GHI prediction, especially for a long time horizon (from 10 min + 40 to 10 min + 60). Moreover, the seasonal study shows that the global horizontal irradiation is more difficult to predict during autumn than other seasons due to the high meteorological variability observed in this period. However, the LSTM deep learning approach is the most robust even in the case of the high variability of GHI. While the ANN and RF are the most sensitive ones.

Incorporating theoretical and practical approaches to assess the amount of sunlight captured by a tilted surface in a tropical climate

Global solar radiation can theoretically be approximated in terms of tilt and azimuth of the surface regarding the impossibility of simultaneous measurement of solar radiation at various surface tilt and azimuth angles. Moreover, the random and anisotropic nature of diffuse radiation in a tropical climate makes it extremely difficult to estimate global solar radiation accurately as a function of surface tilt and azimuth angles. This study aims to develop a novel experimental and theoretical approach in the form of a computational network in order to determine a precise combined model integrated with global horizontal solar radiation to evaluate global tilted solar radiation in a tropical climate. Obtained results revealed that precisely estimation of the global tilted solar radiation was possible, by combining geometric factors for the tilted beam solar radiation, a combination of Gueymard and Louche models for the tilted diffuse solar radiation, and isotropic ground reflectance model for the ground reflected radiation, along with global horizontal solar radiation. It was observed that the accuracy of the model developed was higher for the partly sunny sky compared to the cloudy and rainy sky, estimates were more accurate on south-facing surfaces, and the model's accuracy declined with the increasing tilt angle of the surface. The statistical analysis exhibited excellent agreement between the measured data and simulation results, considering the value of normalized mean absolute error (nMAE %), normalized root mean squared error (nRMSE %), and mean absolute percentage error (MAPE %), which were in the ranges 0.22–0.94, 0.27–1.11, and 0.23–1.02, respectively for estimating global tilted solar radiation in various regions of Peninsular Malaysia, and they were respectively found in the range of 10.2–27.5%, 16.1–38.9%, and 6.0–17.8%, for evaluating the monthly optimum tilt angle towards the south, that leads to a loss of solar energy from 1.3 to 5.4 kWh/m2/year in Peninsular Malaysia. This search revealed that the experimental and theoretical approach employed in this study can be extended to more climatic regions.

SIZING AND IMPLEMENTATION OF A STANDALONE SOLAR PHOTOVOLTAIC SYSTEM FOR A THREE-PHASE SUBMERSIBLE PUMPING MACHINE CONSIDERING THE TECHNO-ECONOMICS PERSPECTIVES: A CASE OF SUPER FARM ILORIN

The solar photovoltaic system is an alternative power supply source that could aid the economic growth of a nation. However, improper sizing of the system leads to a high installation cost and a low level of penetration in the energy mix. This paper focused on the appropriate sizing and implementation of a standalone solar system for a three-phase pump machine in a farm in Ilorin, Nigeria. The pump rating was determined manually as 20HP, with a flow rate of 180,000 L/h. Consequently, the daily load and solar global horizontal irradiance data were acquired and used for sizing the PV system using HOMER Pro software. Fifty-six solar panels with a total area of 120.4 m2 were proposed and installed to achieve a total power of 22.4 kW required for the machine's operation. The levelised cost of energy is 0.4007 $/kWh as against the cost of energy when the farm was running on generators with an energy cost of 4.170 $/kWh. The economic analysis of the system for the farm shows the NPC and COE of the system configuration are $28,341.91 and 0.4007 $/kWh, respectively.

Agricultural Grid Connected Photovoltaic System Design and Simulation in Egypt by using PVSYST Software

Agricultural Photovoltaic Systems are a key technology to achieve sustainable development goals by reducing competition between land for food and electricity. In addition, Agricultural Photovoltaic Systems are at the heart of the link between power generation, crop production and irrigation water conservation. The main ecophysiological constraint on crop production under photovoltaics is the reduction of light. It is difficult to recommend shade tolerance for some plant varieties due to insufficient information on shading conditions for most plants. The use of shading panels (photovoltaic panels) requires more crop-specific research to determine the optimal percentage of panels and their placement that will not reduce agricultural yields. Crop yield variation versus field shading and availability to maximize the system require extensive research. This study aims to develop a standard procedure for designing an agricultural grid-connected photovoltaic power generation system for solar power generation in an agricultural area in Bahteem, Egypt. The technical and annual performance of the grid-connected PV system was simulated using PV Syst software. The paper started with a pre-feasibility study of a grid-connected photovoltaic system using PV Syst. Software with an extensive database of meteorological data, including global daily horizontal solar irradiance, and a database of various renewable energy system components from different manufacturers. In this work, a comprehensive literature review of agricultural solar photovoltaic systems is conducted, with a particular focus on grid-connected systems, followed by a design procedure for grid-connected solar photovoltaic systems. The planned photovoltaic system will generate a total of 400 KWp of electricity. This generated electricity can drive down electricity prices by exporting excess electricity to the national grid. In addition, solar power systems are fuel-efficient and have a low environmental impact.

Optimal Parameters for Spatial Distribution Modeling of Global Horizontal Solar Radiation in Iraq

Solar radiation plays an essential role in all interior physical processes and the radiative budget of the earth-atmosphere system. However, information about the spatial distribution of global solar radiation or its components is limited and exclusive at some scientific centers that specialized in solar radiation physics. Based on daily data of global solar radiation from (1984) to (2004) available at (16) locations in Iraq retrieved from NASA e-archive, this study aims to determine the optimal parameters of the experimental model to estimate the amount of global solar radiation on a horizontal surface as a function of latitude only. The results showed that the suggested model which is mathematically expressed by the sum of sine of latitude is highly appropriate for estimation of global solar radiation in clear sky conditions after adjusting the parameters according to geographical location. Through comparison between modeled output and an independent measured, globally used, and satellite available data and based on overall results besides some validity metrics: MBE, RMSE, pRMSD, NSE, R2, and r; It can be concluded the proposed model is achievable with high accurate practically performance to calculate and estimate the horizontal global solar radiation (GH) in Iraq region.

An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance

ABSTRACT An accurate forecasting of solar global horizontal irradiance (GHI) provides the estimation of future output from photovoltaic (PV) cells, resulting in guaranteed sustainable functioning of solar integrated smart grids. Traditional forecasting algorithms find it relatively hard to obtain the intrinsic non-linearity of solar GHI, leading to poor forecasting accuracy. This work offers a deep learning (DL)-based hybrid model called RLMD BILSTM that combines robust local mean decomposition (RLMD) algorithm with bidirectional long short-term memory (BILSTM) predictor. The proposed RLMD BILSTM model has been developed and evaluated for two separate Indian locations: Hisar and Jaipur, by comparing with benchmark models: persistence, long short-term memory (LSTM), gated recurrent unit (GRU), BILSTM, and their RLMD-based hybrid variations (RLMD LSTM and RLMD GRU). The performance of the proposed model has been verified by the statistical and visual results of short-term annual and seasonal forecast of solar GHI. From the results, it has been observed that the proposed model achieved lower root mean square error (RMSE) (16.34 W/m2-35.07 W/m2) compared to contrast mforecastsodels. The higher annual R2 yielding a value of 0.977–0.995 for the proposed model compared to the contrast model proved its better fitness. Moreover, the study showed that the RLMD with BILSTM improved the RMSE (59.16%–88.88%) and mean absolute error (MAE) (66.45%–92.22%) over various contrast models. The study also conducted the sensitivity analysis using the forecasting efficacy and forecast skill to prove the significance of the proposed model.