Solar Radiation Data Research Articles

An hourly solar radiation prediction model using eXtreme gradient boosting algorithm with the effect of fog-haze

Hourly global solar radiation data is an important factor for solar energy utilization. Due to the lack of solar radiation observation stations in many areas, some hourly solar radiation models are proposed to predict hourly solar radiation. However, the existing models perform poorly in heavy fog-haze areas because the weakening effect of fog-haze on solar radiation is not considered. Thus, in this paper, hourly global solar radiation prediction models are developed considering air quality index (AQI) using XGBoost algorithm. The results show a general improvement in the accuracy of models with AQI as an additional input (Model B1-B6) compared to models that do not consider AQI (Model A1-A6). Compared to Model A, Model B have an increase in R value from 0.927 to 0.948, a decrease in RMSE value from 0.300 to 0.282 and a decrease in MAPE value from 0.159 to 0.145. In addition, for hourly solar radiation prediction, the six most important inputs are the day of the year, air temperature difference, surface temperature difference, hour, AQI, and total cloud cover.

An ensemble machine learning-based solar power prediction of meteorological variability conditions to improve accuracy in forecasting

ABSTRACT The increasing energy demand has significantly improved solar photovoltaic (SPV) systems as a distributed energy source. Real-time control of SPV performance is vital for accurate solar power (SP) prediction. The article proposes an ensemble Machine Learning Approach (MLA) called Random Forest Algorithm-Based Regression Model (RFARM) for hourly forecasting of SP. The approach selectively analyzes meteorological and solar irradiance data (SI) to enhance short-term solar panel prediction. It focuses on employing a correlation-based approach using an RFA with regression to achieve improved SP prediction accuracy. The study compares the PV power generated at Thiagarajar College of Engineering (TCE), Madurai, using four prediction techniques: Artificial Neural Networks (ANN), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) along with a proposed RFARM for different meteorological weather conditions over a 24-hour time horizon. The proposed RFARM method achieves high prediction accuracy by selecting significant parameters, avoiding artificial filtering, and minimizing errors, particularly in predicting solar output during cloud shading. The RFARM model outperforms conventional methods in predicting the daily curve of solar power performance. It achieves an RMSE of 1.52, MAE of 14, and R-squared of 98%. Feature selection further improves accuracy, reducing RMSE by 12.5% and MAE by 17.2% respectively.

Assessment of some Bioclimatic Indices using RayMan Model for Baghdad-Iraq

Abstract The evaluation of bioclimatic conditions on a local scale is still an important topic. It might be a key component of the national plan for the sustainable development of various areas under changing climatic conditions. The present study aimed to examine the behaviour of four bioclimatic indices namely physiologically equivalent temperature (PET), Standard Effective Temperature (SET*), Universal thermal climate index(UTCI) and, Predictive mean vote(Pmv) derived from energy budget models for Baghdad city. The monthly means data of air temperature, relative humidity, wind speed, and solar radiation during the period (1981-2021) were used in this study. All calculations of this study were extracted using RayMan Model. The relationship between PET, Pmv, SET, and UTCI indices in addition to meteorological parameters was also conducted in this study. The results indicate that July and August had the highest values of Pmv (4), SET* (39.4), PET (43.9), and UTCI (39.4). For the majority of the studied indices, the comfortable thermal perception occurred only in March, April, and October. PET and UTCI revealed acceptable comparability in terms of temperature perception when compared to other indices for most months of the year. The maximum heat stress was reported for all indices between May and September, with the lowest stress occurring between January and December. April was the most comfortable month of the year, according to the data. Correlations between UTCI and meteorological parameters found a substantial association with air temperature (0.99) and relative humidity (0.98), respectively. The lowest correlation coefficient (R2=0.69) was found with wind speed. All other indicators indicate a substantial association with the UTCI.

Evaluation of Carbon Reduction Potential and Economic Performance of Photovoltaic Charging System for Campus Electric Bicycles

This study considers the roof characteristics of different types of buildings in the Yuntang campus of Changsha University of Science and Technology, and evaluates the overall potential for photovoltaic power generation and carbon reduction performance of the campus by simulating the roof solar radiation at multiple time scales. In addition, a photovoltaic charging system for electric bicycles suitable for the campus is proposed based on the usage of electric bicycle charging stations on campus. The Monte Carlo method is used to simulate the hourly probability distribution of the charging load, and the capacity of the photovoltaic charging system is optimized by integrating the simulated solar radiation, outdoor temperature and other data, in order to strengthen the relationship between electricity demand and energy supply of the campus.

Day-ahead seasonal solar radiation prediction, combining VMD and STACK algorithms

Abstract This article proposes a method for accurately predicting solar irradiance over a 24-hour horizon to forecast photovoltaic energy generation in a positive-energy building. In order to make this prediction, the input data are divided into seasons and preprocessed using the variational mode decomposition (seasonal-VMD) method. The VMD method is used for extracting high-bandwidth features from the input data, decomposing them into a finite number of smooth modes and focusing on specific frequency ranges. Hence, the accuracy of signal extraction using the VMD method can be improved by selecting particular parameters judiciously, which impacts the smoothing and frequency concentration of the extracted signal. In this regard, the salp swarm algorithm (SSA) is employed to identify the optimal VMD parameters that can be used to enhance extraction accuracy. In addition, the obtained residual between the observed solar irradiation data and their decomposed modes is treated to enhance the prediction process. A stacking algorithm (STACK) is used to predict the following 24-hour solar irradiance modes and the residual, which are finally summed to reconstruct the desired signal. The performances of the proposed prediction method are evaluated using two quantitative evaluation indices: the normalized root mean square percentage error (NRMSPE) and normalized mean absolute percentage error (NMAPE). The proposed model is trained on data collected for three years in Rabat (2019–22). The performance of the proposed model is evaluated by predicting the 24-hour solar irradiance for a different season. The proposed approach seasonal-VMD-STACK is compared with two other methods in the case of using VMD-based STACK without season partition and STACK method only. Moreover, the proposed method has exhibited stability and proven good results with an NRMSPE of 3.87% and an NMAPE of 1.58% for cloudy days during the test phase. The results demonstrate that residual preprocessing, seasonal input data partition and appropriate selection of VMD parameters improve the performance and accuracy of the prediction.

An Improved UAV-Based ATI Method Incorporating Solar Radiation for Farm-Scale Bare Soil Moisture Measurement

The use of UAV-based remote sensing for soil moisture has developed rapidly in recent decades, with advantages such as high spatial resolution, flexible work arrangement, and ease of operation. In bare and low-vegetation-covered soils, the apparent thermal inertia (ATI) method, which adopts thermal infrared data from UAV-based remote sensing, has been widely used for soil moisture estimation at the field scale. However, the ATI method may not perform well under inconsistent weather conditions due to inconsistency of the intensity of the soil surface energy input. In this study, an improvement of the ATI method (ATI-R), considering the variation in soil surface energy input, was developed by the incorporation of solar radiation measurements. The performances of the two methods were compared using field experiment data during multiple heating processes under various weather conditions. It showed that on consistently sunny days, both ATI-R and ATI methods obtained good correlations with the volumetric water contents (VWC) (R2ATI-R = 0.775, RMSEATI-R = 0.023 cm3·cm−3 and R2ATI = 0.778, RMSEATI = 0.018 cm3·cm−3) on cloudy or a combination of sunny and cloudy days as long as there were significant soil-heating processes despite the different energy input intensities; the ATI-R method could perform better than the ATI method (cloudy: R2ATI-R = 0.565, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.156, RMSEATI = 0.033 cm3·cm−3; combined: R2ATI-R = 0.673, RMSEATI-R = 0.028 cm3·cm−3 and R2ATI = 0.310, RMSEATI = 0.032 cm3·cm−3); and on overcast days, both the ATI-R and ATI methods could not perform satisfactorily (R2ATI-R = 0.027, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.027, RMSEATI = 0.031 cm3·cm−3). The results indicate that supplemental solar radiation data could effectively expand applications of the ATI method, especially for inconsistent weather conditions.

A Quantile Functions-Based Investigation on the Characteristics of Southern African Solar Irradiation Data

Exploration of solar irradiance can greatly assist in understanding how renewable energy can be better harnessed. It helps in establishing the solar irradiance climate in a particular region for effective and efficient harvesting of solar energy. Understanding the climate provides planners, designers and investors in the solar power generation sector with critical information. However, a detailed exploration of these climatic characteristics has not yet been studied for the Southern African data. Very little exploration is being done through the use of measures of centrality only. These descriptive statistics may be misleading. As a result, we overcome limitations in the currently used deterministic models through the application of distributional modelling through quantile functions. Deterministic and stochastic elements in the data were combined and analysed simultaneously when fitting quantile distributional function models. The fitted models were then used to find population means as explorative parameters that consist of both deterministic and stochastic properties of the data. The application of QFs has been shown to be a practical tool and gives more information than approaches that focus separately on either measures of central tendency or empirical distributions. Seasonal effects were detected in the data from the whole region and can be attributed to the cyclical behaviour exhibited. Daily maximum solar irradiation is taking place within two hours of midday and monthly accumulates in summer months. Windhoek is receiving the best daily total mean, while the maximum monthly accumulated total mean is taking place in Durban. Developing separate solar irradiation models for summer and winter is highly recommended. Though robust and rigorous, quantile distributional function modelling enables exploration and understanding of all components of the behaviour of the data being studied. Therefore, a starting base for understanding Southern Africa’s solar climate was developed in this study.

Investigation of Shadowing Effect and Electricity Generation in Seasonally Adjusted Solar Photo Voltaic Arrays in Indian Sub-Continent

Solar photovoltaic panels are very widely used these days across the country. Their usage is also being encouraged by the central government and there are many subsidies in place as well. The tilt angle of the solar photovoltaic panel with the horizontal along with module orientation determines the transformation of energy into electricity. The optimum tilt angle for solar photovoltaic panels is considered for incident annual solar energy, this angle is usually kept unchanged for the year-round cycle. In this research work, we examine the optimum tilt angles of panels for the incident energy over panels on an annual and seasonal basis in combination with the shading effect of these panels over rooftops of Delhi (latitude 28.7° N), Nagpur (latitude 21.15° N) and Ahmedabad (latitude 23.02° N). Solar radiation data for the investigation purpose has been classified under 3 seasons, i.e., summer (May, June, July, August), winter (November, December, January, February) and equinoxes (March, April, September, October). It is observed that when panels are arranged facing down south and adjusted seasonally for optimum tilt angle; on an annual basis, they generate higher electrical energy and, in the summer, cast a larger shadow; in the winter, cast a smaller shadow as compared to panels which have fixed tilt throughout the year. This arrangement helps in reducing the solar heat flux via the roof in summer and increasing it in winter, thus, favourably affecting air conditioning load levelling in the structure.

A regional solar forecasting approach using generative adversarial networks with solar irradiance maps

The intermittent and stochastic nature of solar resource hinders the integration of solar energy into modern power system. Solar forecasting has become an important tool for better photovoltaic (PV) power integration, effective market design, and reliable grid operation. Nevertheless, most existing solar forecasting methods are dedicated to improving forecasting accuracy at site-level (e.g. for individual PV power plants) regardless of the impacts caused by the accumulated penetration of distributed PV systems. To tackle with this issue, this article proposes a novel generative approach for regional solar forecasting considering an entire geographical region of a flexible spatial scale. Specifically, we create solar irradiance maps (SIMs) for solar forecasting for the first time by using spatial Kriging interpolation with satellite-derived solar irradiance data. The sequential SIMs provide a comprehensive view of how solar intensity varies over time and are further used as the inputs for a multi-scale generative adversarial network (GAN) to predict the next-step SIMs. The generated SIM frames can be further transformed into PV power output through a irradiance-to-power model. A case study is conducted in a 24 × 24 km area of Brisbane to validate the proposed method by predicting of both solar irradiance and the output of behind-the-meter (BTM) PV systems at unobserved locations. The approach demonstrates comparable accuracy in terms of solar irradiance forecasting and better predictions in PV power generation compared to the conventional forecasting models with a highest average forecasting skill of 10.93±2.35% for all BTM PV systems. Thus, it can be potentially used to assist solar energy assessment and power system control in a highly-penetrated region.

SIZING PHOTOVOLTAIC SYSTEM IN DUHOK PROVINCE, KURDISTAN REGION OF IRAQ

Using clean energy sources instead of traditional methods of energy production is important to tackle global warming and climate change. As in the rest cities of the Kurdistan Region and Iraq, the national electricity outage crisis continues in Duhok Governorate and the production of energy via different sources is still needed. This paper studies the simulation of two different types of solar energy systems on school roofs using the Homer Pro software. The first is a grid-connected system and the second, is an off-grid system for six different regions of Duhok Governorate, using the solar radiation data for each site. Our findings confirm the preference for on-grid PV over an off-grid PV system at all sites. As the cost of energy in USD per kWh in both grid-tied and stand-alone models varies from one region to another, which is as follows: Amedi (0.089, 0.339), Kani Masi (0.091, 0.0338), Bamarni (0.093, 0.338), Mangesh (0.095, 0.349), Semel (0.107, 0.341), and Akre (0.127, 0.347). The net present cost NPC in both grid-connected and grid-independent for 160 kW PV systems is as follows: Kani Masi ($217303, $260504), Amedi ($217621, $260710), Bamarni ($219007, $260587), Mangesh ($220167, $261239), Semel ($227566, $261222), and finally Akre ($238671, $262203).

Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model

Accurately forecasting solar radiation is of great significance to solar energy utilization. To forecast the spatial and temporal distributions of solar radiation simultaneously, a deep neural network model named MRE-UNet is proposed, the solar radiation data in Heilongjiang province is taken as an example to test the forecasting performance in different periods ahead forecasting cases. According to the evaluation results, an 16 h historical solar radiation data was determined to be the best choice for input, and the minimum of MSE can reach 6.47 × 10−4, 1.38 × 10−3 and 2.69 × 10−3 for 1 h, 3 h and 6 h ahead forecasting cases, respectively. The transferability of the MRE-UNet is tested by performing the solar radiation nowcasting in Hubei province, China using the pre-trained MRE-UNet trained by the solar radiation data in Heilongjiang province. The robustness of MRE-UNet is tested by monitoring the effects of adding different level of noise, and MSE keeps to be 6.27 × 10−4 even though the measuring noise increase to be 50%. For further demonstration on the effectiveness of MRE-UNet in spatiotemporal forecasting, the performance in total cloud cover forecasting is also tested, and satisfactory forecasting results are obtained. Finally, spatiotemporal correlation analysis on solar radiation and total cloud cover data is carried out, a potential reason for satisfying forecasting performance of MRE-UNet is given. From this work, MRE-UNet proposed can be provided as an efficient tool for dealing with further solar radiation spatiotemporal forecasting problem, and the spatiotemporal correlation characteristics can be employed as the basis for further developing effective solar radiation forecasting approach to a degree.

A critical overview of the (Im)practicability of solar radiation forecasting models

To size solar energy facilities, the availability of solar radiation data on a horizontal surface is inevitable. Based on the vast applications of energy from the sun in electricity generation, quality research has been dedicated to developing models capable of estimating solar radiation for various purposes. Although tremendous successes have been recorded while developing these models, some researchers have questioned the relevance of such models for practical applications. Aside from presenting an overview of existing solar radiation forecasting models, this study presents a perspective and critical discourse on the practicability or otherwise of solar irradiation prediction models available in the literature. The existing models are classified as satellite-driven models, regression models, statistical models, artificial intelligence-driven models, and hybrid solar irradiation forecast models. This study also presents research gaps that need to be examined in the future. Insights from the review reveal that although the generalization capabilities of solar radiation forecast models specifically for locations without measuring instruments have turned out to be an uphill task, some promising researches are being implemented and improved to bridge the gap of generalization of models. The subject discussed in this paper will be valuable to researchers, practitioners, funders, and policymakers interested in developing and utilizing these models to foster the growth of the solar energy industry.

Long-term performance of grid-integrated solar photovoltaic system on institutional buildings in tropical wet and dry climates of India - a practical study.

This study presents a long-term performance analysis of a one-mega-watt power grid-connected PV plant installed on the rooftop of GITAM University,Visakhapatnam,Andhra Pradesh, India for 3 years. The 1000 kWp PV plant design, performance analysis, economic feasibility, and greenhouse gas reduction are provided. For 3 consecutive years (2018-2019, 2019-2020 and 2020-2021), the energy yields, performance ratio (PR), capacity utilisation factor (CUF) and efficiency are evaluated. The plant was established in 2018 with a peak power of 1000 kWp. The system feeds the state grid with all of the electricity it produces. The system is continuously monitored and analysed using yield, efficiency and performance parameters for 36 months (September 2018 to August 2021). Solar irradiation data has been collected from the National Renewable Energy Laboratory (NREL) database for these years. The system comprises 3078 PV modules, 23 inverters rating 20 kW/50 kW, a data logger and a Net-metering unit. The annual energy generation of the PV plant recorded during the study period is 1376.29 MWh, 1265.541 MWh and 1115.73 MWh. The annual average performance ratio for the first three consecutive years is observed as 0.68, 0.62 and 0.58, whereas the yearly average capacity factors are 15.5%, 14.11% and 12.72%, respectively. The overall system efficiency of the PV system during the study period is 11.39%. Compared to the country's current conventional thermal power plants, we predict that rooftop PV systems may reduce CO2 emissions by 2145.406 tCO2eq for the base year, based on the electricity produced by this PV plant. The grid-integrated photovoltaic plant in this study is compared to other systems in the literature for performance assessment.

Comparison of bias correction methods for climate change projections in the lower Shivaliks of Punjab

Abstract The study evaluates the performance of bias correction techniques by dividing the observed climate data period into calibration and validation sets. For this purpose, the daily data of temperature, rainfall, and solar radiation from 2010–2095 for lower Shivaliks of Punjab (Ballowal Saunkhri) were downloaded from Marksim weather generators using outputs of CSIRO-Mk3-6-0 climate model under four RCP scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). The bias correction of model data (temperature, rainfall, and solar radiation) was done by developing correction functions (using a model and observed data from 2010 to 2015) from different bias correction methods (difference method, Leander and Buishand method, modified difference method, linear scaling, variance scaling, and quantile mapping). The corrected model data for the year 2016–2020 were validated against the observed data. The difference method was found to be best for bias correction due to low error and high efficiency. The corrected future model data (2021–2095) analysis on an annual and seasonal basis predicted a rise in maximum temperature and minimum temperature by 1.3–2.8 °C and 0.5–3.0 °C, respectively, under different scenarios. The study predicted more increase in rainfall and solar radiation under RCP 8.5 followed by RCP 6.0, RCP 4.5, and RCP 2.6 scenarios.

Estimation of one-minute direct normal irradiance using a deep neural network for five climate zones

Due to its high cost and significance for solar thermal technologies, the calculation of direct normal irradiance has dominated the modelling of solar radiation components. There are three methods described in the literature to calculate direct normal irradiance: regression analysis, probabilistic approach, and application of machine learning models. To demonstrate that machine learning models can achieve sufficient generality for solar radiation data with good statistical results, a new methodology is proposed with three main goals: that required only one measure data as input, trained a deep neural network model using only one station referred as the reference station, and evaluate the direct normal irradiance estimated by the model with the measured values from others stations of the same climate zone. The results then were compared against the most prominent separation models in the literature. The proposed methodology shows that the model learn the radiative characteristics of the reference station and is a feasible method to estimate the component of solar radiation in others location. Therefore, the proposed approach is a viable methodology that has the ability to reach the sought-after quasi-universal model by researchers, as demonstrated by the neural network’s similar or superior performance compared to the separation models.

A waste heat and liquefied natural gas cold energy recovery-based hybrid energy cycle: An effort to achieve superior thermodynamic and environmental performances

Cascading energy production cycles to make maximum utilization of waste heat can considerably enhance thermodynamic performance. In addition, the simultaneous recovery of waste heat of a power plant and liquefied natural gas (LNG) cold energy in the form of cascading power generation cycles can lead to superior thermodynamic and environmental results. In this article a comprehensive analysis of a solar-assisted hybrid energy cycle (HEC) for electricity and natural gas (NG) generation is presented. The developed HEC is comprised a tri-stage organic Rankine cycle (ORC) unit, a double-stage organic flash cycle (OFC) unit, and a solar unit based on parabolic trough collectors (PTCs). Exhaust gas, LNG, and solar energy are considered as main inputs. By employing the offered solar-assisted HEC, it is possible to take maximum advantage of the large temperature difference between the exhaust gas (CO2) and LNG. Besides, a percentage of the cycle's thermal duty is produced via the solar farm. A comprehensive analysis of thermodynamic, exergoenvironmental and exergoeconomic standpoints has been conducted. A tri-objective optimization algorithm and a sensitivity analysis considering all the above standpoints are also developed. According to the developed simulation, net output electricity, NG production rate, and exergy efficiency of the energy cycle were almost 8.63 MW, 22.62 kg/s, and 66.4%, respectively. Further, the values of product unit environmental impact and product unit cost were 0.262 mPts/kWh and 0.0923 USD/kWh, respectively. Moreover, elevating the compressor pressure ratio can greatly enhance the environmental impacts caused by the offered solar-assisted HEC. Also, raising the value of pressure ration of the compressor can greatly enhance the environmental impacts. Finally, the performance of the solar unit was done according to the solar radiation data of a certain city.

Optimal Tilt Angle of Photovoltaic Panels: A Case Study in the City of Rio de Janeiro

The optimal tilt angle of photovoltaic panels plays a crucial role in energy generation. However, the accumulation of dust on solar panels can significantly impact their performance and efficiency, leading to a reduction in energy production. Therefore, it is crucial to consider the effect of dust deposition on the optimal tilt angle of solar panels. Regarding panel installation, it is often observed that panels are positioned to follow the natural slope of the roofs, disregarding the optimal angle for maximizing solar radiation utilization. Numerous studies have investigated the impact of dust accumulation on the performance of photovoltaic panels and the optimal inclination angle for different regions and seasons. This study aims to analyze the optimal tilt angle of photovoltaic panels for maximum energy generation, considering undesired effects such as dust, dirt, water droplets, and other atmospheric factors. The authors have proposed an equation to calculate the optimal tilt angle of photovoltaic panels based on a case study conducted in different Rio de Janeiro City regions. The methodology employed in this study involves estimating solar incidence on the surface of the photovoltaic panels using the authors' proposed equation, which considers the latitude and longitude of the panel installation location. The results obtained were validated using software that generates hourly solar radiation data. The results indicate that an inclination of 30 degrees, calculated using the proposed equation, resulted in a 2% deviation from the optimal theoretical angle.

Hybrid intelligent deep learning model for solar radiation forecasting using optimal variational mode decomposition and evolutionary deep belief network - Online sequential extreme learning machine

Accurate prediction of solar radiation is of great significance to improve the utilization of solar energy for photovoltaic power generation on the roofs of urban buildings. Therefore, a hybrid model, namely the OVMD-PACF-ISSA-DBN-OSELM model, is being proposed in this study for solar radiation prediction. Firstly, the data is decomposed by optimal variational mode decomposition (OVMD) technology. Secondly, the partial autocorrelation function (PACF) technology selected features for components decomposed by OVMD technology. Thirdly, a deep belief network-online sequential extreme learning machine (DBN-OSELM) prediction model is constructed, and sparrow search algorithm (SSA) is utilized to optimize the model hyperparameters. Aiming at the shortcomings of SSA algorithm, Tent map and differential evolution strategy are introduced to improve the SSA algorithm. Finally, the model is used to predict each component, and the prediction results of each component are summarized to obtain the prediction results of solar radiation. To verify that the proposed method can obtain accurate prediction results, experiments are conducted using solar radiation data for January, May and October 2022, and compared with other models. Taking January as an example, compared with shallow model BP, the RMSE, MAE and R of the proposed model are increased by 66.83%, 58.65% and 5.33%, respectively. Moreover, the experiments of the proposed model in other months have excellent results. The current findings demonstrate that the suggested model is a strong contender in the arena of solar radiation prediction, and can consequently provide valuable support in enhancing the efficiency of photovoltaic equipment installed on urban building roofs.

A novel data gaps filling method for solar PV output forecasting

This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. The accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.

Installation of solar power plant in Adıyaman region and analysis of solar energy potential

Energy is one of the most necessary and fundamental needs for human beings to continue their lives. Solar energy, which is one of the most important renewable energy sources, has great energy potential in our country with its long radiation duration and radiation amount.
 The primary thing to do in solar power plant (SPP) installation is to analyze the climatic characteristics of the region. Therefore, having and analyzing the meteorological data of the region has important advantages for the solar power plant to be established. The importance of solar radiation data in the installation of a solar power plant in a region is directly proportional to the payback period of the plant under normal conditions. In this study, a 1 MW SPP feasibility study was conducted in Besni district of Adıyaman province. Depending on the energy potential of the regions, it is calculated that the payback period is between 2-5.7 years for Adıyaman province. Considering the state's electricity pricing, it is seen that the payback period in the industry is about 2 years, while it has a period of 5.7 years for house installation.
 SPP is analyzed in the light of solar radiation data in the Adıyaman region which will be a pioneer for future studies thanks to the depreciation period as well as analyses performed. In addition to these, the parameters to be considered during the SPP installation stages will be specified in detail.