Global Horizontal Irradiance Research Articles

Spatio-temporal dynamic clustering modeling for solar irradiance resource assessment

Nowadays, with the development of international policies and agreements to promote the integration of renewable energy sources, mainly solar and wind, modeling the solar resource by including the spatio-temporal variability is crucial to determine future PV power plant locations and estimate potential power generation performances. However, contributions involving long-term periods and different time windows to explore such potential solar resource variability are generally scarce. Under this framework, the present paper proposes a methodology focused on characterizing and clustering the spatio-temporal solar resource variability through the global horizontal irradiance analysis. Hierarchical clustering technique is firstly used to classify the spatial data. Different time windows — from short-term to long-term data — can be subsequently evaluated by using various sources of information. The Spanish territory is selected as case study, considering 22-year period data (1999–2020) and 1,936,917 observations from online satellite database. Spatial variability and geographical clustering differences are discussed and compared depending on the selected time windows, identifying relevant spatial variations for some specific months. Additionally, some years present more variability as well, in line with the sunspot peak of the solar cycles. The proposed approach gives an alternative comprehensive spatio-temporal clustering and characterization of GHI evolution, providing a suitable methodology to help the current European sustainable energy transition.

Research Method for Assessing the Potential of Solar Energy Source: Case Study in Thanh Hoa Province


 This research uses the GHI (Global Horizontal Irradiation Index) raster data source of solar radiation potential map developed by The World Bank Group and the land use map data, infrastructure in 2019 of Thanh Hoa province to calculate the solar radiation technical potential in Thanh Hoa province. In addition, GIS techniques are also applied to overlay thematic maps to determine the available solar energy development area according to the method of the Institute of Energy. The results show that the available solar energy development area is 54,621.15 ha, accounting for 5% of the province's natural area, the technical potential is 21,848.46 MWp. The well-developed regions are concentrated in the lowland and midland districts due to favorable conditions of land scale and infrastructure, less affected by natural disasters such as storms, floods and landslides. Regarding the method of determining technical potential, it is required to be more specific to identify available and exclusion areas with some types of land such as water surface, forest and paddy rice land.

A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting

The randomness and volatility of solar irradiance pose a challenge to efficient solar energy development and utilization across the world, which increases the necessity of developing an efficient solar irradiance forecasting model. However, previous studies rarely emphasized the importance of transfer learning and nonlinear feature selection, especially for the studies associated with newly-built photovoltaic plants. Moreover, multi-step ahead forecasting research is limited, despite its significance to dispatching efficiency improvement of photovoltaic system. Aiming to address the research gaps, a novel hybrid deep learning framework (HDLF), consisting of the modules of feature selection, feature convolution, forecasting, self-attention, transfer learning, performance evaluation, and performance analysis, is newly proposed in this paper to perform multi-step ahead solar irradiation forecasting. Specifically, the HDLF is pre-trained based on the studied datasets of global horizontal irradiation in the source domains, and the abstract information obtained from pre-training is transferred to the HDLF of the target domain to enhance its performance. To validate the effectiveness of the proposed HDLF, two simulation experiments are carried out based on the datasets from California, USA, with a resolution of 1 h. The corresponding experimental results from the modules of performance evaluation and performance analysis indicate that the maximum improvements in mean absolute error reach 80.19% and 35.67% in two experiments, respectively, thereby confirming the superiority and feasibility of the HDLF.

Deep Learning Approaches for Long-Term Global Horizontal Irradiance Forecasting for Microgrids Planning

Providing sustainable energy to rural communities is considered in Sustainable Development Goal 7. Off-grid renewable energy systems arise as an affordable solution due to their portability and the availability of renewable sources for rural communities. In this work, to deal with the uncertainties of solar resources, we employ two deep learning models (feed forward and recurrent neural networks) to predict renewable sources in a long-term horizon. To this aim, the approach presented takes into account the necessity of a high enough resolution in the forecasting output. As a case study, we employ open source data for a location in Michoacan, Mexico as well as open source programming frameworks to ensure the replicability of the numerical experiments. The results show that our prediction model performs excellently with respect to the baseline methods (ARIMA, exponential smoothing, and seasonal naive) in terms of the evaluation metrics MASE (18.5% of reduction with respect to seasonal naive), RMSE (24.7%), WAPE (13.1%), MAE (12.9%), and APB (8.9%).

Ensemble learning based multi-modal intra-hour irradiance forecasting

Accurate intra-hour irradiance forecasting plays an important role in improving the effectiveness of photovoltaic power management. More and more sensors, for example, total sky imager and cloud sensors, are widely used in photovoltaic plants, so that various factors could be comprehensively concerned to improve the accuracy of forecast. However, the existing methods have difficulty in encoding heterogeneous multi-source data dynamically. To mitigate these problems and improve the practicality of the model, an ensemble learning based multi-modal intra-hour irradiance forecasting method is proposed to forecast the global horizontal solar irradiance in the next 10 min. First, six base learners are built by selecting different combinations of multi-modal data as inputs for considering various factors. Then, a linear ridge regressor is used to integrate the pre-trained base learners to obtain outputs. Furthermore, to improve the robustness of the ensemble models, a dynamical fine-tuning scheme based on clear sky index is proposed. The weather states are predicted by a hidden Markov model based on the clear sky index. Experiments show that the proposed forecasting method effectively improves the accuracy of intra-hour irradiance forecasting by 11.6% when compared with commonly used models.

SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), the normalized versions of these three metrics (nMAE, nRMSE, nMAPE), forecast skill (FS), and relative forecasting error. The long short-term memory-autoencoder model (LSTM-AE) outperformed the other four forecasting models and achieved the best results (nMAE = nRMSE = nMAPE = 0.02). The LSTM-AE model is the most accurate in all weather conditions. Predictions for sunny days are more accurate than for cloudy days as well as for summer compared to winter. SENERGY can predict the best forecasting model with 81% accuracy. The proposed auto-selective approach can be extended to other research problems, such as wind energy forecasting, and to predict forecasting models based on different criteria such as the energy required or speed of model execution, different input features, different optimizations of the same models, or other user preferences.

Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations.

A case study of NeuralProphet and nonlinear evaluation for high accuracy prediction in short-term forecasting in PV solar plant

Prediction of the energy, active production from the PV solar plant is a challenge in cloudy weather or with clouds over the solar plant; therefore, it has impact in the planning of the power system, especially in the season analysis and prediction accuracy adjustments, for example in holidays. In 2022, some authors published some analysis associated to horizontal pyranometers and the limits in the evaluation of the data, the Mean Bias Error of Daily Solar Irradiation average (MIEave) ranges from 0.17% to 2.86% associated a sudden change in the weather, it increases the “risk of misestimating the potential electricity generation” with short-term error of more than 50% and the Global Horizontal Irradiance (GHI) has a mean bias error (MBE) of at least ±8% [1]. In this research article, a novel proposal for short-term forecasting combines the satellite with meteorological station data and statistical model associated to the new seasonality analysis by using two approaches: i) NeuralProphet, Ridge regression, ii) Long Short-Term Memory with convolutional neural networks. Besides, it requires three KPI as feedback, it is the mean absolute error (MAE), relative Root mean square error (RMSE), and mean absolute percentage error (MAPE). The results demonstrate a MAPE of 5.93% and a computational time 852.10 s and the comparison with new predictions methods from 2019 to 2021. This research article illustrates the new approach with the forecasting method in a case of the PV solar plant in Peru and proves the robustness and seasonality results, and new short-terms improvements associated to external influence as cloudy conditions and resource availability. Our findings are an improvement of the model MAPE 12.14%–5.93%; even compared with the literature and currently models as ARIMA-LSTM with 10.57%, LSTM with NN and G, SARIMA and SVM considering Gaussian White Noise with 8.14% and Prophet with SVM with 8.81%.

Sun-blocking index from sky image to estimate solar irradiance

To optimize the balance between indoor comfort and minimal energy usage, several automated methods have been proposed for smart window control systems. However, owing to the high variability of solar irradiance, it is difficult for window controllers to operate properly. The variability of solar irradiance is mostly caused by cloud motion in the sky. Cloud information can be obtained from human observations, satellite images, and sky images, and parameters such as cloud amount, cloud fraction, and cloud cover (CC) have been conventionally used for considering cloud effects. These conventional parameters focus only on the amount of cloud over the entire sky while failing to account for the amount of cloud locally around the sun disk. Consequently, rapid changes in the cloud at short time scales have not been considered properly, and the high variability of solar irradiance has not been addressed accurately. Therefore, in this study, a new parameter, the sun-blocking index (SBI), is introduced to model the partial blocking of the sun by clouds and hence enhance the estimation accuracy of solar irradiance under fickle weather. In addition, an image processing model to calculate SBI using sky images was proposed. Once the correlation between the clearness index and SBI is established, the SBI-dependent clearness index is applied to solar radiation models. The results showed that the global horizontal irradiance was estimated with a relative root-mean-square error (rRMSE) of 16.21%. Remarkably, when direct normal irradiance (DNI) was estimated, the proposed model outperformed conventional models with rRMSE of less than 28%. • Partial blocking of the sun by cloud is considered for assessing solar irradiance. • The sun-blocking index (SBI) is proposed to measure the partial blocking. • A methodology for calculating the SBI from sky images is developed. • The relationship between SBI and clearness index is investigated. • Solar irradiance is more accurately estimated via the SBI-dependent clearness index.

Use of physics to improve solar forecast: Part II, machine learning and model interpretability

Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy’s Atmospheric Radiation Measurement’s Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions.

Assessment and performance analysis of roof-mounted crystalline stand-alone photovoltaic (SAPV) system at selected sites in South Africa

BackgroundTechnology is deployed to take the advantage of the ultimate energy from the sun (solar energy) to be used as heat or clean electricity. This energy is classified as “sustainable energy” or “renewable energy” because it requires a short period to naturally replenish the used energy. The application of solar energy involves the conversion of the natural energy resource into a usable form, either as heat or as electricity. The device consists of solar cells made from semiconductor materials, such as silicon, cadmium telluride, gallium arsenide, and so on. Solar potential is both location- and climate-dependent; it is characterised by low energy intensity and intermittency, which limit its application; an improvement in photovoltaic (PV) system performance will facilitate more deployment of the clean electricity system. Therefore, this study provides PV potential and system information required for reliable and optimised solar PV systems at chosen locations. This work uses a 5-stage solar PV system assessment and system performance evaluation utilising Solargis Prospect software. The PV potential and system performance of nine selected site locations in South Africa was conducted using this method. The nine PV site locations are Bloemfontein (Free State), Germiston (Gauteng), Mahikeng (North-West), Mbombela (Mpumalanga), Musgrave (Kwazulu-Natal), Musina (Limpopo), Port Nolloth (Northern Cape), Port Elizabeth (Eastern Cape), and Worcester (Western Cape).ResultThe results of the study were categorised into PV meteorological and system performance parameters as follows. Photovoltaic meteorological parameters—the site in Mahikeng has the highest global horizontal irradiance (GHI), 2156 kWh/m2, and a corresponding specific PV power output (1819.3 kWh/kWp), closely followed by Bloemfontein (2111.5 kWh/m2, 1819.4 kWh/kWp) and Port Nolloth (2003.2 kWh/m2, 1820.5 kWh/kWp). The lowest GHI (1645.1 kWh/m2) and specific PV power output (1436.6 kWh/kWp) were recorded in Musgrave. Photovoltaic system performance parameters—the range of performance ratio (PR) between 75.8 and 77.7% was reported across the nine sites. This ratio met the acceptable benchmark of PR. The highest specific PV power output loss, 118.8 kWh/kWp, was obtained at sites in Bloemfontein, Mahikeng, and Port Nolloth, while the lowest, 93.8 kWh/kWp, was in Musgrave.ConclusionsThe results of the solar PV potential assessment and the evaluation of PV systems performance in the chosen sites across the nine provinces of South Africa show huge PV potential and energy yield. From the results, it was observed that the range of the yearly average of: (1) GHI among the sites is 1645.1–2156 kWh/m2; (2) direct normal irradiation among the sites is 1785.3–2559.3 kWh/m2; (3) diffuse horizontal irradiation among the sites is 512.5–686kWh/m2; (4) global tilted irradiation among the sites is 1849.2–2397.1 kWh/m2; (5) the temperature (TEMP) among the sites is 16–23 °C; (6) specific PV power output (PVOUT specific) among the sites is 1436.6–1820.5 kWh/kWp; (7) total PV power output (PVOUT total) among the sites is 14.366–2397.1 MWh; and (8) the performance ratio among the sites is 75.8–77.7%. Based on the solar resource and performance results of the PV system obtained, the deployment of monocrystalline solar PV technology in all the considered sites across South Africa is technically viable.

Solar Irradiance Nowcasting System Trial and Evaluation for Islanded Microgrid Control Purposes

The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. PV systems and optionally controllable distributed energy resources (DERs) in IMGs can be operated in an optimised way based on nowcasting (forecasting up to 60 min ahead). This study aims to evaluate the performance under Perth, Western Australian conditions, of an all-sky imager (ASI)-based nowcasting system, installed at Murdoch University in Perth, Western Australia (WA). Nowcast direct normal irradiance (DNI) and global horizontal irradiance (GHI) are inputted into a 5 kWp solar PV system with a direct current (DC) power rating/alternating current (AC) power rating ratio of 1.0. A newly developed classification method provided a simplified irradiance variability classification. The obtained nowcasting system evaluation results show that the nowcasting system’s accuracy decreases with an increase in lead time (LT). Additionally, the nowcasting system’s accuracy is higher when the weather is either mostly clear (with a recorded LT15 mean absolute deviation (MAD) of 0.38 kW) or overcast (with a recorded LT15 MAD of 0.19 kW) than when the weather is intermittently cloudy with varying cloud conditions (with a recorded LT15 MAD of 0.44 kW). With lower errors observed in lower LTs, overall, it might be possible to integrate the nowcasting system into the design of IMG controllers. The overall performance of the nowcasting system at Murdoch University was as expected as it is comparable to the previous evaluations in five other different sites, namely, PSA, La Africana, Evora, Oldenburg, and Julich.

Validating global horizontal irradiance estimated by McClear model under clear-sky and all-sky conditions in Pakistan

The availability of accurate and reliable solar radiation data is very important to explore solar energy potential for commercial power plants. This study presents the validation of global horizontal irradiance (GHI) from McClear model under clear-sky and all-sky conditions against measured data for complex topography of Pakistan. The McClear GHI shows good agreement with measured GHI under clear-sky conditions for nine stations and good correlation was achieved with correlation coefficient ranging from 0.972 to 0.992. The relative mean bias error (rMBE) and relative root mean square error (rRMSE) using GHI data having temporal resolution of 10 minutes was ranging from −2.3 to 6.4% and 3.6 to 7.8% respectively. A seasonal statistical analysis shows variation of errors during seasons, significant variation of errors was observed for monthly statistical analysis, the overall errors for all stations were maximum in January and minimum in June. Cloud fraction (CF) from three state-of-the-art datasets (ERA-5, MERRA-2 and MODIS) was incorporated into McClear clear-sky GHI (GHIMC,clear-sky) to derive commercially resourceful all-sky GHI data (GHIMC,all-sky) using a noval approach. The rMBE of GHIMC,all-sky using CF from ERA-5, MERRA-2 and MODIS ranges from 0.1 to 8.4%, 0.1 to 14.1% and 0.2 to 16.8% respectively. The application of ERA-5 cloud fraction shows good correlation with correlation coefficient ranging from 0.871 to 0.966 and gave best results as rMBE for all stations is less than 4% except one station, the long-term GHIMC,all-sky data can be used for the solar resource assessment of Pakistan in the absence of measured data.

Estimation of global and diffuse horizontal irradiance by machine learning techniques based on variables from the Heliosat model

The increased interest in the sources of renewable electricity has drawn attention to the rapidly developing solar energy sector owing to its high cost-benefit ratio. To accurately calculate the potential electricity output of photovoltaic (PV) panels, the global horizontal irradiance (GHI) and diffuse horizontal irradiance (DHI) must be known. The worldwide presence of satellite imagery provides an efficient way to estimate current and historical GHI instead of using the high time- and cost-consuming in-situ measurement. The most of past studies utilized more satellite bands as the inputs for machine learning algorithms to estimate GHI. The further estimation of DHI was usually based on weather-related parameters. Thus, there is a research gap in using only one satellite band for both GHI and DHI estimations. Therefore, this study used machine learning algorithms to estimate GHI and DHI, with inputs delivered from the Heliosat model based on band 3 of Himawari-8 satellite imageries. The results were compared with the original and site-adapted Heliosat models and seven DHI separation models. The results indicated that the machine learning models were capable of performing with the same accuracy as the Heliosat models. However, their performance was better while estimating DHI, in which case they outperformed even the best separation model. Higher accuracy and precision were observed in those models where the additional solar zenith at time t+1h was used together with other input features. This highlighted the possibility of using only one satellite band together with the calculated solar position variables as the input. Overall, this research has established a new method for estimating GHI and DHI with high confidence based on satellite imageries and the Heliosat model through the application of machine learning techniques.

Extremal Dependence Modelling of Global Horizontal Irradiance with Temperature and Humidity: An Application Using South African Data

The interaction between global horizontal irradiance (GHI) and temperature helps determine the maximum amount of solar power generated. As temperature increases, GHI increases up to the point that it increases at a decreasing rate and then decreases. Therefore, system operators need to know the maximum possible solar power which can be generated. Using the multivariate adaptive regression splines, extreme value theory and copula models, the present paper seeks to determine the maximum temperature that will result in the generation of the maximum GHI ceteris paribus. The paper also discusses extremal dependence modelling of GHI with temperature and relative humidity (RH) at one radiometric station using South African data from 16 November 2015 to 16 November 2021. Empirical results show that the marginal increases of GHI converge to 0.12 W/m2 when temperature converges to 44.26 °C and the marginal increases of GHI converge to −0.1 W/m2 when RH converges to 103.26%. Conditioning on GHI, the study found that temperature and RH variables have a negative extremal dependence on large values of GHI. Due to the nonlinearity and different structure of the dependence on GHI against temperature and RH, unlike previous literature, we use three Archimedean copula functions: Clayton, Frank and Gumbel, to model the dependence structure. The modelling approach discussed in this paper could be useful to system operators in power utilities who must optimally integrate highly intermittent renewable energies on the grid.

Worldwide validation of an Earth Polychromatic Imaging Camera (EPIC) derived radiation product and comparison with recent reanalyses

A very recent gridded product for the hourly global horizontal irradiance (GHI), derived from the measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) launched by a NOAA/NASA/USAF consortium, is validated at 31 locations worldwide, from January, 2017 to June, 2019. In contrast to those traditional methods that leverage (simplified) radiative transfer, this EPIC-derived product uses machine learning – a random forest model – to map out the connection between satellite-observed variables of various kinds and GHI. Nonetheless, the detailed validation conducted here shows that the quality of this EPIC-derived GHI dataset not only does not outperform those traditional gridded solar radiation datasets, but also contains undesirable artifacts that can be possibly attributed to inadequacies in the machine-learning procedure. For these reasons, it is not recommended to use this EPIC-derived dataset in its current form for solar resource assessment purposes.

Comparison of Satellite-Based and Ångström–Prescott Estimated Global Horizontal Irradiance under Different Cloud Cover Conditions in South African Locations

The study compares the performance of satellite-based datasets and the Ångström–Prescott (AP) model in estimating the daily global horizontal irradiance (GHI) for stations in South Africa. The daily GHI from four satellites (namely SOLCAST, CAMS, NASA SSE, and CMSAF SARAH) and the Ångström–Prescott (AP) model are evaluated by validating them against ground observation data from eight radiometric stations located in all six macro-climatological regions of South Africa, for the period 2014-19. The evaluation is carried out under clear-sky, all-sky, and overcast-sky conditions. CLAAS-2 cloud fractional coverage data are used to determine clear and overcast sky days. The observed GHI data are first quality controlled using the Baseline Surface Radiation Network methodology and then quality control of the HelioClim model. The traditional statistical benchmarks, namely the relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) provided information about the performance of the datasets. Under clear skies, the estimated datasets showed excellent performance with maximum rMBE, rMAE, and rRMSE less than 6.5% and a minimum R2 of 0.97. In contrast, under overcast-sky conditions there was noticeably poor performance with maximum rMBE (24%), rMAE (29%), rRMSE (39%), and minimum R2 (0.74). For all-sky conditions, good correlation was found for SOLCAST (0.948), CMSAF (0.948), CAMS (0.944), and AP model (0.91); all with R2 over 0.91. The maximum rRMSE for SOLCAST (10%), CAMS (12%), CMSAF (12%), and AP model (11%) was less than 13%. The maximum rMAE for SOLCAST (7%), CAMS (8%), CMSAF (8%), and AP model (9%) was less than 10%, showing good performance. While the R2 correlations for the NASA SSE satellite-based GHI were less than 0.9 (0.896), the maximum rRMSE was 18% and the maximum rMAE was 15%, showing rather poor performance. The performance of the SOLCAST, CAMS, CMSAF, and AP models was almost the same in the study area. CAMS, CMSAF, and AP models are viable, freely available datasets for estimating the daily GHI at South African locations with quantitative certainty. The relatively poor performance of the NASA SSE datasets in the study area could be attributed to their low spatial resolution of 0.5° × 0.5° (~55 km × 55 km). The feasibility of the datasets decreased significantly as the proportion of sky that was covered by clouds increased. The results of the study could provide a basis/data for further research to correct biases between in situ observations and the estimated GHI datasets using machine learning algorithms.

Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad

The growing threat of global climate change stemming from the huge carbon footprint left behind by fossil fuels has prompted interest in exploring and utilizing renewable energy resources. Several statistical, Machine and Deep Learning techniques exist and have been used for many years for a range of forecasting problems. This study is based on the data recorded for 4 years and 9 months using precise instruments, in Islamabad, Pakistan. For this purpose we use statistical and Deep Learning architectures for forecasting solar Global Horizontal Irradiance which not only helps in grid management and power distribution, but also brings attention towards the potential of solar power production in Pakistan and its part to play in tackling global climate change. We have used statistical methods such as Seasonal Auto-Regressive Integrated Moving Average Exogenous (SARIMAX) and Prophet, and Machine Learning methods such as Long Short-Term Memory (LSTM) which is an extension of Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The selected forecast methods in our study are based on their ability to work with time series data and we have used different models configurations to see which performs best for our dataset. The performance of every model is studied using different error metrics such as Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The major contribution of this study is the data collected to carry out research towards the goal of renewable energy future, and from the test methods used on the data in this study, it can be intuitively determined that ANN, CNN, and LSTM architectures perform best for short-term forecasts, while SARIMAX and Prophet are efficient for long-term forecasts.

PERENCANAAN PLTS OFF GRID DI DESA TOLONUO SELATAN KECAMATAN TOBELO UTARA KABUPATEN HALMAHERA UTARA

Electrical energy continues to increase along with the increasing number of human population and the more modern human lifestyle is one of the causes of energy consumption. The use of several electrical and electronic equipment to support the needs of society in today's modern era. Electrical energy is one of the main human needs in supporting people's daily lives, especially the upper middle class, therefore this need must be fulfilled and its utilization must be used efficiently. The PV mini-grid system requires a very large cost for a village scale from an economic point of view. Apart from the very large initial investment cost of Rp.2. 189.50 0.000,-, in general, if the infrastructure is improved, tourists will use higher electricity consumption, so that the area can improve the economy and village welfare. type of quantitative research. The quantitative aspect of this study is the collection of primary data regarding daily electrical energy needs in South Tolonuo Village using interviews and field observations. The daily energy requirement in South Tolonuo Village is 97 kWh/day or 97,000 Wh/day. To determine the peak power of PLTS, Global Horizontal Irradiance (GHI) data is required.

The Sky-Status Climatology of Greece: Emphasis on Sunshine Duration and Atmospheric Scattering

The aim of this work is the study of the sky conditions climatology over Greece based on the diffuse-fraction (kd) limits, for clear, kd ∈ [0, 0.26]; intermediate, kd ∈ (0.26, 0.78); and overcast, kd ∈ (0.78, 1) skies. kd is, therefore, used here to characterise the sky conditions over a site. Its values are estimated from diffuse and global horizontal solar irradiances the typical meteorological years of 43 selected Greek sites. The kd values in each specific range are equivalent to sunshine durations (SSDs) under the particular sky conditions. Annual, seasonal, and intra-annual variations in SSDs are estimated with regression equations to fit their means. Clear skies comprise 33%, intermediate 40%, and overcast 27% of the time in a year. kd, as an atmospheric scattering index (ASI), shows dependence on the sites’ geographical latitude: best-fit lines mean ASIs are derived showing no trend, while overcast skies show a slight negative trend. A comparison of the clear-sky SSDs for four Greek sites from the Global Climate Data and one site from the Academy of Sciences of Moldova with those derived from kd shows a remarkable difference. A new methodology is developed that results in much smaller differences. Finally, maps of the annual SSDs and ASIs are derived for Greece.