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Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets.

In the field of renewable energies, the logistical intricacies of production, as well as the use and storage of photovoltaic energy, have become critical issues. In addition to sheer quantity, the stability of this type of energy is a crucial factor in ensuring the reliability and consistency of power generation. This paper defines Solar Irradiance Stability Factors (SISFs) as indicators complementing electricity production. When planning solar energy production in each geographical area, both the quantity and stability of solar irradiance are necessary for exploitation and determining the quality of solar irradiance. While the average production of solar energy per unit area in each time interval is a widely used parameter in daily practice, the observation of the amplitude of solar irradiance and its influence on energy production in the observed time interval is currently still rare. The SISFs defined in this article are new metrics that mainly depend on the meteorological variability in an area, and the observed time intervals should be in the range of seconds, minutes, or even hours. Larger time intervals are not helpful for the stability of solar irradiance in energy production and logistics from the source to the destination. They provide a complementary and more accurate measure of how suitable a particular geographical area is for producing solar energy.

Under the action of waves, a small unmanned surface vehicle (USV) will experience continuous oscillation, significantly impacting its photovoltaic power generation system. This paper proposes a USV photovoltaic power generation simulation model, and the efficiency of photovoltaic MPPT control under wave action is studied. A simulation model for solar irradiance on solar panels of USV under wave action is established based on CFD and solar irradiation models. The dynamic changes in irradiance of USV solar panels under typical wave conditions are analyzed. The MPPT efficiency of USV photovoltaic power generation devices under continuously changing irradiance conditions is studied on this basis. The simulation research results indicate that waves and solar altitude angles significantly impact the instantaneous irradiation energy of USV photovoltaic devices. However, the impact of waves on the average irradiance is relatively tiny. The sustained oscillation of irradiance poses certain requirements for the Maximum Power Point Tracking (MPPT) control frequency of USV photovoltaic systems; a disturbance control frequency of no less than 50 Hz is proposed.

Solar irradiance data provided by the Copernicus program are crucial for several scientific, environmental, and energy management applications, but their validation by means of ground-based measurements may be necessary, especially if daily and hourly data resolutions are required. The validation process not only ensures that reliable information is available for solar energy resource planning, power plant performance assessment, and grid integration, but also contributes to the improvement of the Copernicus system itself. Ground-based stations offer site-specific data, allowing for comprehensive assessments of the system’s performance. This work presents a comparative statistical analysis of solar irradiance data provided by the Copernicus system and ground-based measurements on a seasonal basis at three specific Italian reference sites, showing a maximum average relative error of less than 7% for hourly horizontal global irradiance in the irradiance range defined by the IEC 61724-2.

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle. The measurements were conducted during the summer season, and two models were suggested to calculate the overall efficiency of the PV/T system. The variance between the results obtained from the two models was minimal, below 5%. For the examined panel type, the following average values were derived: electrical efficiency = 12.01%, thermal efficiency = 47.21%, and overall efficiency = 59.23%.

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

In the quest to scientifically develop power systems increasingly reliant on renewable energy sources, the potential and temporal complementarity of wind and solar power in China’s northwestern provinces necessitated a systematic assessment. Using ERA5 reanalysis data for wind speed and solar irradiance, an evaluation was carried out to determine the potential and spatial distribution of wind and solar power across these provinces. Land use types and terrestrial surface slopes were considered in gauging this potential. Theoretical wind and solar power outputs were then compared to understand their complementarity on annual, monthly, and hourly temporal scales. This exploration utilized methodologies including rank correlation coefficients, crossover frequency analysis, and standard deviation complementarity rates. Areas such as the Tarim Basin, Jungar Basin, and the northeastern part of Xinjiang, northwestern Qinghai, and northern Gansu were identified as having significant wind and solar power potential, with wind power densities reaching as high as 600 W/m2 and solar irradiance surpassing 2000 kWh/m2. In these energy-rich areas, the distinct complementarity between theoretical wind and solar outputs was discerned. On an annual scale, the complementarity appeared weakest, with only 7.48% of the combined provinces’ area showing medium-level complementarity. On a monthly scale, conversely, a pronounced complementarity was displayed, especially during the March–May and October–November periods. When evaluated on an hourly basis, an impressive 63.63% of the total output duration exhibited complementary characteristics.

Solar resource forecasting is an essential step towards smart management of power grids. This study aims to increase the performance of intra-hour forecasts. For this, a novel ensemble model, combining statistical extrapolation of time-series measurements with models based on machine learning and all-sky imagery, is proposed. This study is conducted with high-quality data and high-resolution sky images recorded on the Solar Platform of the West University of Timisoara, Romania. Atmospheric factors that contribute to improving or reducing the quality of forecasts are discussed. Generally, the statistical models gain a small skill score across all forecast horizons (5 to 30 min). The machine-learning-based methods perform best at smaller forecast horizons (less than 15 min), while the all-sky-imagery-based model performs best at larger forecast horizons. Overall, for forecast horizons between 10 and 30 min, the weighted forecast ensemble with frozen coefficients achieves a skill score between 15 and 20%.

Energy nowcasting is a valuable asset in managing energy loads and having real-time information on solar irradiation availability. In this study, we evaluate the spectrally integrated outputs of the SENSE system for solar irradiance nowcasting for the period of the ASPIRE (atmospheric parameters affecting spectral solar irradiance and solar energy) campaign (December 2020–December 2021) held in Athens, Greece. For the needs of the campaign, several ground-based instruments were operating, including two pyranometers, a pyrheliometer, a cloud camera, a CIMEL sunphotometer, and a precision spectral radiometer (PSR). Global horizontal irradiance (GHI) estimations were more accurate than direct normal irradiance (DNI). SENSE estimations are provided every 15 min, but when comparing bigger time intervals (hours-days), the statistics improved. A dedicated assessment of the SENSE’s inputs is performed in respect to ground-based retrievals, considering cloud conditions (from a sky imager), AOD, and precipitable water vapor from AERONET. The factor that established the larger errors was the visibility of the solar disc, which cannot be defined by the available sources of model inputs. Additionally, there were discrepancies between the satellite estimation of the clouds and the ground picture, which caused deviations in results. AOD differences affected more the DNI.

Accurately predicting the power produced during solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for its balanced operation and optimized dispatch and reduces operating costs. Solar PV power generation depends on the weather conditions, such as temperature, relative humidity, rainfall (precipitation), global solar radiation, wind speed, etc., and it is prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility, and intermittency. Recently, the demand for further investigation into the uncertainty of short-term solar PV power generation prediction and its effective use in many applications in renewable energy sources has increased. In order to improve the predictive accuracy of the output power of solar PV power generation and develop a precise predictive model, the authors used predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is an important aspect of optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods identified in the reviewed literature are classified according to the input data source, and the case studies and examples proposed are analyzed in detail. The contributions, advantages, and disadvantages of the predictive probabilistic methods are compared. Finally, future studies on short-term solar PV power forecasting are proposed.

The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany’s National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations.

Building-integrated photovoltaics (BIPVs) and vehicle-integrated photovoltaics (VIPVs) receive solar irradiance through non-uniform shading objects. Standard scalar calculations cannot accurately determine the solar irradiance of BIPV and VIPV systems. This study proposes a matrix model using an aperture matrix to accurately calculate the horizontal and vertical planes affected by non-uniform shading objects. This can be extended to the solar irradiance on a VIPV by applying a local coordinate system. The 3D model is validated by a simultaneous measurement of five orientations (roof and four sides, front, left, tail, and right) of solar irradiance on a car body. An accumulated logistic function can approximate the shading probability. Furthermore, the combined use of the 3D solar irradiance model is effective in assessing the energy performance of solar electric vehicles in various zones, including buildings, residential areas, and open spaces. Unlike standard solar energy systems, the energy yield of a VIPV is affected by the shading environment. This, in turn, is affected mainly by the location of vehicle travel or parking in the city rather than by the climate zones of the city.

Electric vehicles (EVs) have the advantage of being resilient to natural disasters. However, users hesitate to donate electricity when they lose the chance to recharge at the utility. Solar electric vehicles (SEVs) save energy through vehicle-integrated photovoltaics (VIPV) and make it possible to voluntarily donate excess energy, thus maintaining facility resilience. Given that the supply of solar energy to VIPV systems is not continuous and is difficult to forecast, the contribution of VIPV to the resilience of the larger energy system has been called into question. This is the first study in which the potential of VIPV to maintain utility resilience is investigated in the context of physical factors, such as irradiance, and social factors. The actual energy yield of a VIPV car was determined using an advanced 3D solar irradiation model under a nonuniform shading distribution, with validation from actual measures of solar irradiance on five orthogonal sides of the car body. The Monte Carlo method was used to model the complex factors in VIPV energy storage and energy donations under different scenarios. Depending on the climate, population density, and shading environment, the voluntary contribution of stored electricity in SEV is sufficient to provide disaster relief support.

Challenges in utilising fossil fuels for generating energy call for the adoption of renewable energy sources. This study focuses on modelling and nowcasting optimal tilt angle(s) of solar energy harnessing using historical time series data collected from one of South Africa’s radiometric stations, USAid Venda station in Limpopo Province. In the study, we compared random forest (RF), K-nearest neighbours (KNN), and long short-term memory (LSTM) in nowcasting of optimum tilt angle. Gradient boosting (GB) is used as the benchmark model to compare the model’s predictive accuracy. The performance measures of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were used, and the results showed LSTM to have the best performance in nowcasting optimum tilt angle compared to other models, followed by the RF and GB, whereas KNN was the worst-performing model.

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management.

This paper analyzes the potential of solar thermal systems for being employed for process heating in cotton-based textile industries. The technological capability of a flat plate collector (FPC) to meet the solar industrial process heating (SIPH) requirement in yarn production is assessed. Moreover, the usability of a parabolic trough collector (PTC) in meeting the technological mandates of SIPH in fabric processing units is critically examined. Further, this paper reports the findings of a study on the potential cost advantage of solar process heating over the conventional process heating practices in cotton-based textile industries. The approach involves the selection of the locations of sample textile industries and the employable solar collector technologies, as well as assessment of financial reward, if any. Eight different cotton-based textile industries located in different geographical domains (in India) are selected. The selected textile industries are situated within the textile hubs existing in different states across India. Analysis of technoeconomic benefit derivable in selected textile industries using FPC for hot water generation and PTC for steam generation is presented. In the case of FPC-based SIPH systems, the maximum value of solar fraction is estimated to be 0.30 and the corresponding estimation for the levelized cost of useful thermal energy (LCUTE) delivered comes out as INR 790/GJ to INR 1020/GJ. On the other hand, in case of parabolic-trough-solar-concentrator-based SIPH systems, LCUTE is estimated in the range of INR 1030/GJ to INR 1610/GJ. From a critical analysis of financial viability in consideration of related factors such as payback time and return on investment in SIPH, it appears that the SIPH systems for hot water generation may be a good choice, whereas SIPH systems for steam generation are seen to have longer payback periods and lower returns on investment, and, therefore, it seems that adequate financial support from central and state governments with additional supports from bilateral or multilateral organizations may enable them to become a sustainable technology option.

Variability in solar irradiance has an impact on the stability of solar systems and the grid’s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m2) and the Plane Of Array (POA) irradiance (unit: W/m2) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), and R2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too.

Solar irradiance models contribute to mitigating the lack of measurement data at a ground station. Conventionally, the models relied on physical calculations or empirical correlations. Recently, machine learning as a sophisticated statistical method has gained popularity due to its accuracy and potential. While some studies compared machine learning models with other models, a study has not yet been performed that compares them side-by-side to assess their performance using the same datasets in different locations. Therefore, this study aims to evaluate the accuracy of three representative models for estimating solar irradiance using atmospheric variables measurement and cloud amount derived from satellite images as the input parameters. Based on its applicability and performance, this study selected the fast all-sky radiation model for solar applications (FARMS) derived from the radiative transfer approach, the Hammer model that simplified atmospheric correlation, and the long short-term memory (LSTM) model specialized in sequential datasets. Global horizontal irradiance (GHI) data were modeled for five distinct locations in South Korea and compared with hourly measurement data of two years to yield the error metrics. When identical input parameters were used, LSTM outperformed the FARMS and the Hammer model in terms of relative root mean square difference (rRMSD) and relative mean bias difference (rMBD). Training an LSTM model using the input parameters of FARMS, such as ozone, nitrogen, and precipitable water, yielded more accurate results than using the Hammer model. The result shows unbiased and accurate estimation with an rRMSD and rMBD of 23.72% and 0.14%, respectively. Conversely, the FARMS has a faster processing speed and does not require significant data to make a fair estimation.

The rapid growth of wind and solar energy penetration has created critical issues, such as fluctuation, uncertainty, and intermittence, that influence the power system stability, grid operation, and the balance of the power supply. Improving the reliability and accuracy of wind and solar energy predictions can enhance the power system stability. This study aims to contribute to the issues of wind and solar energy fluctuation and intermittence by proposing a high-quality prediction model based on neural networks (NNs). The most efficient technology for analyzing the future performance of wind speed and solar irradiance is recurrent neural networks (RNNs). Bidirectional RNNs (BRNNs) have the advantages of manipulating the information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BRNN’s output layer concurrently receives information from both the backward layers and the forward layers. The bidirectional long short-term memory (BI-LSTM) prediction model was designed to predict wind speed, solar irradiance, and ambient temperature for the next 169 h. The solar irradiance data include global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The historical data collected from Dumat al-Jandal City covers the period from 1 January 1985 to 26 June 2021, as hourly intervals. The findings demonstrate that the BI-LSTM model has promising performance in terms of evaluation, with considerable accuracy for all five types of historical data, particularly for wind speed and ambient temperature values. The model can handle different sizes of sequential data and generates low error metrics.

In this paper, we introduce a model representing the key characteristics of high frequency variations of solar irradiance and photovoltaic (PV) power production based on Clear Sky Index (CSI) data. The model is suitable for data-driven decision-making in electrical distribution grids, e.g., descriptive/predictive analyses, optimization, and numerical simulation. We concentrate on solar irradiance data since the power production of a PV system strongly correlates with solar irradiance at the site location. The solar irradiance is not constant due to the Earth’s orbit and irradiance absorption/scattering from the clouds. To simulate the operation of a PV system with one-minute resolution for a specific coordinate, we have to use a model based on the CSI of the solar irradiance data, capturing the uncertainties caused by cloud movements. The proposed model is based on clustering the days of each year into groups of days, e.g., (i) cloudy, (ii) intermittent cloudy, and (iii) clear sky. The CSI data of each group are divided into bins of magnitudes and the transition probabilities among the bins are identified to deliver a Markov Chain (MC) model to track the intraday weather condition variations. The proposed model is tested on the measurements of two PV systems located at two different climatic regions: (a) Yverdon-les-Bains, Switzerland; and (b) Oahu, Hawaii, USA. The model is compared with a previously published N-state MC model and the performance of the proposed model is elaborated.

Fast-growing energy demand of the world makes the researchers focus on finding new energy sources or optimizing already-developed approaches. For an efficient use of solar and wind energy in an energy system, correct design and sizing of a power system is of high importance and improving or optimizing the process of data obtaining for this purpose leads to higher performance and lower cost per unit of energy. It is essential to have the most precise possible estimation of solar and wind energy potential and other local weather parameters in order to fully feed the demand and avoid extra costs. There are various methods for obtaining local data, such as local measurements, official organizational data, satellite obtained, and reanalysis data. In this paper, the Modern-Era Retrospective analysis for Research and Applications dataset version 2 (MERRA-2) dataset provided by NASA is introduced and its performance is evaluated by comparison to various locally measured datasets offered by meteorological institutions such as Meteonorm and Deutscher Wetterdienst (DWD, or Germany’s National Meteorological Service) around the world. After comparison, correlation coefficients from 0.95 to 0.99 are observed for monthly global horizontal irradiance values. In the case of air temperature, correlation coefficients of 0.99 and for wind speed from 0.81 to 0.99 are observed. High correlation with ground measurements and relatively low errors are confirmed, especially for irradiance and temperature values, that makes MERRA-2 a valuable dataset, considering its world coverage and availability.

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

This research presents a method to improve data accuracy for the more efficient data management of the studied applications. The data accuracy was improved using the preciseness function learning model (PFL model). It contains a database in which the amount of data is more or less dependent on all of the possible behavior of the studied application. The proposed model improves data with functions obtained by optimizing curves to represent the data at each point, which estimate the database’s diffusion behavior, and functions can be built around all of the various forms of databases. The proposed model always updates its database after processing. It has been learning to optimize the processing precision. In order to verify the precision of the proposed model through its application to a PV system simulation model, the process’s database should contain at least one year. This is because the overall behavior of the PV power output in Thailand depends on the seasonal weather; Thailand has three seasons in a period of one year. The testing was performed by comparing the PV power output. The simulation results with the actual measurement data (12 MW PV system) can be divided into two conditions: the daily comparison and the seasonal PV power output. As a result, the proposed model can accurately simulate the PV power output despite the sudden daily climate change. The average nRMSE (normalized RMSE) of the proposed model is very low (1.23%), and ranges from 0.30% to 2.26%. Therefore, it has been proven that this model is very accurate.

Presently, deep learning models are an alternative solution for predicting solar energy because of their accuracy. The present study reviews deep learning models for handling time-series data to predict solar irradiance and photovoltaic (PV) power. We selected three standalone models and one hybrid model for the discussion, namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network-LSTM (CNN–LSTM). The selected models were compared based on the accuracy, input data, forecasting horizon, type of season and weather, and training time. The performance analysis shows that these models have their strengths and limitations in different conditions. Generally, for standalone models, LSTM shows the best performance regarding the root-mean-square error evaluation metric (RMSE). On the other hand, the hybrid model (CNN–LSTM) outperforms the three standalone models, although it requires longer training data time. The most significant finding is that the deep learning models of interest are more suitable for predicting solar irradiance and PV power than other conventional machine learning models. Additionally, we recommend using the relative RMSE as the representative evaluation metric to facilitate accuracy comparison between studies.

Solar irradiance prediction is significant for maximizing energy-saving effects in the predictive control of buildings. Several models for solar irradiance prediction have been developed; however, they require the collection of weather data over a long period in the predicted target region or evaluation of various weather data in real time. In this study, a long short-term memory algorithm–based model is proposed using limited input data and data from other regions. The proposed model can predict solar irradiance using next-day weather forecasts by the Korea Meteorological Administration and daily solar irradiance, and it is possible to build a model with one-time learning using national and international data. The model developed in this study showed excellent predictive performance with a coefficient of variation of the root mean square error of 12% per year even if the learning and forecast regions were different, assuming that the weather forecast was correct.

The modelling of stochastic feed-ins and demands becomes essential for transmission grid operation and planning due to the extension of renewable energy sources (RES). Neglecting the correlation between uncertain variables and/or oversimplifying the distribution through the assumption of Normal distributions leads to the inaccurate determination of future network states. Therefore, the uncertainties need to be accurately modelled in order to be used in a probabilistic load flow approach. This paper analyses the characteristics of wind speed and solar irradiance for different locations throughout countries and models the dependencies between them. In addition, the total electrical load and the energy exchange between neighbouring countries are analysed. All of these uncertainties are modelled together in a high-dimensional joint probability distribution using pair-copula constructions. The model is applied to generate samples and determine the probability of extreme events, e.g. high RES production and low demand. The probability for rather high load (>65 GW) and low RES production with wind speed less than 3 m/s and solar irradiance less than 100 W m ² at 90% of all stations is e.g. 0.064%. In addition, the model is integrated in a probabilistic load flow approach in order to analyse the German transmission grid for a future scenario of the year 2025. With the copula, samples are generated as an input for the Monte Carlo simulation approach. The approach enables the assessment of planned HVDC lines. When considering the HVDC lines, the load on the AC lines can be significantly reduced.

The attenuating effects of clouds and aerosols on global horizontal irradiance (GHI) and ultraviolet erythemal irradiance (UVER) were evaluated and compared using data from four sites in South Korea (Gangneung, Pohang, Mokpo, and Gosan) for the period 2005−2016. It was found that GHI and UVER are affected differently by various attenuating factors, resulting in an increase in the ratio of UVER to GHI with a decrease in the clearness index of GHI. A comparative analysis of the clearness indices of GHI and UVER identified an almost linear relationship between two transmittances by applying UVER with fixed slant ozone ( UVER 300 ) and there was a latitudinal difference in the relationship. Some nonlinearity remained in this relationship, which suggests a contribution by other factors such as clouds and aerosols. Variations of the UVER 300 ratio to GHI with cloud cover and aerosol optical depth were analyzed. The ratio increased with cloud cover and decreased with aerosol optical depth, indicating that clouds attenuate GHI more efficiently than UVER and that the attenuation by aerosols is greater for UVER than for GHI. A multiple linear regression analysis of the clearness indices of GHI and UVER 300 quantitively demonstrates differences in the radiation-reducing effects of clouds and aerosols, with some regional differences by site that can be attributed to local climatic characteristics in South Korea.

Incident solar radiation modelling has become of vital importance not only in architectural design considerations, but also in the estimation of the energy production of photovoltaic systems. This is particularly true in the case of buildings with integrated photovoltaics (PV) systems having a wide range of orientations and inclinations defined by the skin of the building. Since solar radiation data at the plane of interest is hardly ever available, this study presents the analysis of two of the most representative transposition models used to obtain the in-plane irradiance using as input data the global and diffuse irradiation on the horizontal plane, which can be obtained by satellite-based models or ground measurements. Both transposition models are validated with experimental measurements taken in Murcia (southeast of Spain) and datasets provided by the photovoltaic geographical information system (PVGIS) and the National Renewable Energy Laboratory (NREL) for vertical surfaces facing the four cardinal points. For the validation, the mean bias deviation, root mean square error and forecasted skill were used as indicators. Results show that the error rate decreases slightly for clear days. Better results are also obtained by dismissing data with low solar elevation angles so as to avoid shadowing effects from the surroundings in the early and late hours of the day, which affects mainly the performance of the transposition models for west and east surfaces. The results highlight the potential of equator-facing façades in winter time when the received irradiation can be twice as much as the one collected by the horizontal plane. It is also noteworthy that the operating conditions of all façades are mainly low irradiance and medium temperature at these locations.

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.

Finding optimal panel tilt angle of photovoltaic system is an important matter as it would convert the amount of sunlight received into energy efficiently. Numbers of studies used various research methods to find tilt angle that maximizes the amount of radiation received by the solar panel. However, recent studies have found that conversion efficiency is not solely dependent on the amount of radiation received. In this study, we propose a solar panel tilt angle optimization model using machine learning algorithms. Rather than trying to maximize the received radiation, the objective is to find tilt angle that maximizes the converted energy of photovoltaic (PV) systems. Considering various factors such as weather, dust level, and aerosol level, five forecasting models were constructed using linear regression (LR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and gradient boosting (GB). Using the best forecasting model, our model showed increase in PV output compared with optimal angle models.

Solar renewable energy (SRE) applications are substantial in eradicating the rising global energy shortages and reversing the approaching environmental apocalypse. Hence, effective solar irradiance forecasting models are crucial in utilizing SRE efficiently. This paper introduces a partially amended hybrid model (PAHM) by the implementation of a new algorithm. The algorithm innovatively utilizes bi-directional gated unit (Bi-GRU), autoregressive integrated moving average (ARIMA) and naive decomposition models to predict solar irradiance in 5-min and 60-min intervals. Meanwhile, the models’ generalizability strengths would be tested under an 11-fold cross-validation and are further classified according to their computational costs. The dataset consists of 32 months’ solar irradiance and weather conditions records. A fundamental result of this study was that the single models (Bi-GRU and ARIMA) outperformed the hybrid models (PAHM, classical hybrid model) in the 5-min predictions, negating the assumptions that hybrid models oust single models in every time interval. PAHM provided the highest accuracy level in the 60-min predictions and improved the accuracy levels of the classical hybrid model by 5%, on average. The single models were rigorous under the 11-fold cross-validation, performing well with different datasets; although the computational efficiency of the Bi-GRU model was, by far, the best among the models.

In this study we design and test a novel solar tracking generation system. Moreover, we show that this system could be successfully used as an advanced solar power source to generate power in greenhouses. The system was developed after taking into consideration the geography, climate, and other environmental factors of northeast China. The experimental design of this study included the following steps: (i) the novel solar tracking generation system was measured, and its performance was analyzed; (ii) the system configuration and operation principles were evaluated; (iii) the performance of this power generation system and the solar irradiance were measured according to local time and conditions; (iv) the main factors affecting system performance were analyzed; and (v) the amount of power generated by the solar tracking system was compared with the power generated by fixed solar panels. The experimental results indicated that compared to the power generated by fixed solar panels, the solar tracking system generated about 20% to 25% more power. In addition, the performance of this novel power generating system was found to be closely associated with solar irradiance. Therefore, the solar tracking system provides a new approach to power generation in greenhouses.

Variation in solar irradiance causes power generation fluctuations in solar power plants. Power grid operators need accurate irradiance forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and time of day. Cloud cover is one of the most important variables that affects solar power generation, but is also characterized by a high degree of variability and uncertainty. Deep learning methods have the ability to learn long-term dependencies within sequential data. We investigate the application of Gated Recurrent Units (GRU) to forecast solar irradiance and present the results of applying multivariate GRU to forecast hourly solar irradiance in Phoenix, Arizona. We compare and evaluate the performance of GRU against Long Short-Term Memory (LSTM) using strictly historical solar irradiance data as well as the addition of exogenous weather variables and cloud cover data. Based on our results, we found that the addition of exogenous weather variables and cloud cover data in both GRU and LSTM significantly improved forecasting accuracy, performing better than univariate and statistical models.

In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data from weather stations in Germany, U.S.A, Switzerland, and South Korea, which all have distinct climate types. Experiment results show that the proposed approach is more accurate than FFNN, and achieves the accuracy of up to 60.31 W/m2 in terms of root-mean-square error (RMSE). Moreover, compared with the persistence model, the proposed model achieves average forecast skill of 50.90% and up to 68.89% in some datasets. In addition, to demonstrate the effect of using a particular forecasting model on the microgrid operation optimization, we simulate a one-year operation of a commercial building microgrid. Results show that the proposed approach is more accurate, and leads to a 2% rise in annual energy savings compared with FFNN.

Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP) for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV), and the Levenberg-Marquardt algorithm (LMA) is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS), and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

Over the years, sustainability and impact on the environment, as well as operation expenditure, have been major concerns in the deployment of mobile cellular base stations (BSs) worldwide. This is because mobile cellular BSs are known to consume a high percentage of power within the mobile cellular network. Such energy consumption contributes to the emission of greenhouse gases (GHGs) through the use of conventional diesel generating set (gen-set). As a result, mobile cellular operators are faced with the dilemma of minimizing the power consumption, GHG emissions, and operation cost, while improving the quality of service (QoS) of the networks. In attempting to find a solution, this study presents the feasibility and simulation of a solar photovoltaic (PV)/battery hybrid power system (HPS), as a predominant source of power for a specific mobile cellular BS site situated in the Soshanguve area of the city of Pretoria, South Africa. It also presents the technical development and shows the environmental advantage and cost benefits of using a solar PV/battery HPS to power a BS site with a 24 h daily load of 241.10 kWh/d and peak load of 20.31 kW as compared to using the HPS with a solar PV/diesel gen-set/battery. The solar resource pattern for the city of Pretoria was collected from the National Aeronautics and Space Administration and was modeled statistically. The statistical modeling done using solar radiation resource exposure characteristic patterns of Pretoria, South Africa revealed an average annual daily solar radiation of 5.4645 Wh/m2/d and a 0.605 clearness index. The simulation and the design were done using Hybrid Optimization Model for Electric Renewables (HOMER) and Matlab/Simulink software. The simulation finding showed that the HPS of the solar PV/battery combination has about a 59.62% saving in the net present cost (NPC) and levelized cost of energy (LCOE) and an 80.87% saving in operating cost as against conventional BSs powered with a gen-set/battery.

With the continuous increase of grid-connected photovoltaic (PV) installed capacity and the urgent demand of synergetic utilization with the other power generation forms, the high-precision prediction of PV power generation is increasingly important for the optimal scheduling and safe operation of the grid. In order to improve the power prediction accuracy, using the response characteristics of PV array under different environmental conditions, a data driven multi-model power prediction method for PV power generation is proposed, based on the seasonal meteorological features. Firstly, through the analysis of PV power characteristics in typical seasons and seasonal distribution of the weather factors, such as solar irradiance and ambient temperature, the influences of different weather factors on PV power prediction are studied. Then, according to the meteorology characteristics of Beijing, different seasons can be divided. The historical data corresponding to different seasons are acquired and then the seasonal PV power forecasting models are established based on optimized multi-layer back propagation neural network (BPNN), realizing the multi-model prediction of PV power. Finally, effectiveness of the seasonal PV power forecasting method is compared and validated. The performance analysis of the neural network forecasting model under typical seasonal conditions shows that the multi-model forecasting method based on seasonal characteristics of PV power generation is better than that of single power forecasting model for the whole year. The results show that the proposed method can effectively improve the power forecasting accuracy of PV power.

Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model’s performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, thereby improving the reliability of the power they supply. The study site was Tainan in southern Taiwan, which receives abundant sunlight because of its location at a latitude of approximately 23°. Four forecasting models of surface solar irradiance were constructed, using the multilayer perceptron (MLP), random forests (RF), k-nearest neighbors (kNN), and linear regression (LR), algorithms, respectively. The forecast horizon ranged from 1 to 12 h. The findings are as follows: first, solar irradiance was effectively estimated when a combination of ground weather data and solar position data was applied. Second, the mean absolute error was higher in MLP than in RF and kNN, and LR had the worst predictive performance. Third, the observed total solar irradiance was 1.562 million w/m2 per year when the solar-panel tilt angle was 0° (i.e., the non-tilted position) and peaked at 1.655 million w/m2 per year when the angle was 20–22°. The level of the irradiance was almost the same when the solar-panel tilt angle was 0° as when the angle was 41°. In summary, the optimal solar-panel tilt angle in Tainan was 20–22°.