Prediction Of Global Solar Radiation Research Articles

Solar Radiation Forecasting Models and their Thermodynamic Analysis in Asaba: Least Square Regression and Machine Learning Approach

Least square regression and machine learning tools were used for the development of global solar radiation forecasting models for Asaba region. Data from the year 2013-2022 from Nigerian Meteorological Agency, Asaba was used for this study. The least square regression method was used to develop four global solar radiation -based models, tagged H1, H2, H3 and H4 with characteristic day length, solar declination angle, rainfall amount, etc. as its model terms while the machine learning models produced multilayer perceptron, coarse Gaussian model (SVM-based model) and XGBoost model. The prediction factors like mean bias error, mean percentage error, root mean square error, Nash-Sutcliffe equation, coefficient of correlation (R), t-test, and coefficient of determination (R2) were considered using the model terms. The results indicates that H4 model outperformed H1, H2, H3, machine learning models (SVM-based model, multilayer perceptron and XGBoost) and other existing models (MA-MME and MLR) with a mean percentage error value of 0.740, RMSE value of 46.588, Nash-Sutcliffe equation value of 0.739, higher R2 value of 0.7391, t-test value of 2.595E-24 and mean bias error value of -6.88E-12. Thus, H4 model results fell within accepted range. Additionally, the exergy of the global solar radiation of Asaba varied from 20-185 W/m2 which are good. This shows that a more efficient and ideal global solar radiation prediction model (H4) has been developed for Asaba and other regions that share similar climatic conditions.

Evaluation and performance comparison of different models for global solar radiation forecasting: a case study on five cities

AbstractRecently, solar energy has emerged as the most promising renewable energy source to meet the world’s energy demands. However, to harness the potential of solar energy, accurate data on solar radiation are crucial. It is considered the first step in assessing solar resources for various applications and achieving energy sustainability goals. Due to the unavailability of solar radiation measurements in many parts of the world, several models have been developed to predict global solar radiation (GSR) at these locations. Thus, this study aims to evaluate the proficiency of several GSR models at five new locations and determine the most suitable one for GSR prediction. The study has further developed solar radiation models for these new locations, as well as general ones for the entire region, which does not have any GSR models despite the existence of many planned solar energy facilities in this area. Additionally, the study investigates the effect of changing the length of the validation dataset on models’ performance and accuracy, as well as assesses the introduced models’ generalization capability. To achieve these objectives, the observed data of GSR for approximately 37 years at studied locations are used to develop and validate the proposed models. The study’s findings reveal that Model 1 provides the best performance at all locations, with accuracy, coefficient of determination ($$R^{2}$$ R 2 ), ranging from 95 to 98%, except for the coastal location, where it is from 91 to 95%. The remaining performance indicators of the best models, such as RMSE, MABE, MAPE, and $$r$$ r , are good, and their values range from 0.7863 to 1.9097 (MJ m−2 day−1), from 0.6430 to 1.7060 (MJ m−2 day−1), from 3.4319 to 10.0890 (%), and from 0.9914 to 0.9981, respectively. The length of the validation dataset has a slight effect on the models’ performance, ranging from about 1% to 2%. Therefore, Model 1 is the recommended solar radiation model, which can provide precise and rapid estimates of global solar radiation. This approach could be used in the design and performance evaluation of many solar applications. The primary benefit of this approach in the current investigation is that temperature data are continuously and effortlessly recorded for various purposes.

Enhanced Daily Global Solar Radiation Prediction Through Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System with Meta-Heuristic Algorithm Integration

Enhanced Daily Global Solar Radiation Prediction Through Hybrid Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System with Meta-Heuristic Algorithm Integration

Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm

Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).

Hourly global solar radiation prediction based on seasonal and stochastic feature

Accurate and detailed solar radiation data play a crucial role in the simulation of building thermal and photovoltaic systems. However, developing a highly precise and dependable solar radiation model using cost-effective data has proven challenging. This work proposes a new attenuation solar radiation model formed by conducting a comprehensive analysis of existing models and gaining new insights into solar radiation's seasonal and stochastic properties. Meanwhile, the model is constructed using easily obtainable surface meteorological parameters. The results demonstrate that the proposed model exhibits good performance in terms of prediction accuracy. Moreover, the majority of existing hourly solar radiation models have been primarily developed for clear-sky conditions. However, there is a growing demand for solar radiation hourly estimations that can uphold a high level of accuracy and reliability even in different weather state. Conversely, the proposed model is developed and validated by more than twenty year's meteorological data encompassing various weather conditions in Japan. It effectively captures the stochastic nature of solar radiation by utilizing turbidity parameters, even on cloudy and rainy days. Additionally, the inclusion of interaction variables significantly enhances its interpretability.

Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer

The number of existing global solar radiation (GSR) observation stations is limited, and it is challenging to meet the demand for scientific research and production. Different forecasting horizons of solar radiation correspond to various applications. Therefore, it is critical to design realistic models to predict the GSR of varying sequence lengths. This study proposes a prediction model based on the analysis of the Pearson correlation between GSR and each input parameter and the establishment of different input combinations related to the result of Pearson analysis in four high-quality datasets. The proposed Informer model compares the results with five classical machine learning models on four datasets with R2, RMSE, and skill score (S) as evaluation metrics. This study examines the proposed model's prediction performance for five prediction lengths, four climate zones, three sampling frequencies, and two input types. The results showed that the Informer model performs well with the clearness index and pressure as the input. Besides, the RRMSE values are less than 10% under optimal input in long sequence forecasting. The findings suggested that the proposed advanced Informer model is a reliable alternative for GSR prediction due to its high predictive accuracy under diverse prediction lengths, sampling frequencies, climate zones, and the number of input parameters.

An evolutionary-assisted machine learning model for global solar radiation prediction in Minas Gerais region, southeastern Brazil

An evolutionary-assisted machine learning model for global solar radiation prediction in Minas Gerais region, southeastern Brazil

A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism

The conventional multi-step ahead solar radiation prediction method ignores the time-dependence of a future solar radiation time series. Therefore, according to sequence-to-sequence (seq2seq) model theory, this paper proposes the seq2seq long- and short-term memory model (seq2seq-LSTM), the seq2seq-LSTM model with an attention mechanism (seq2seq-at-LSTM), and a transformer model, which consists only of the attention mechanism. The hourly global solar radiation data between 2016 and 2018 from Shaanxi, China, is used to train and validate the models. The results show that the introduction of the attention mechanism can effectively improve the prediction accuracy of the seq2seq-LSTM model. However, the model is still not very good at capturing the long-distance dependence of the solar radiation time series due to the inherent properties of LSTM. In comparison, the transformer model, which is based entirely on the attention mechanism, performs much better at capturing the long-distance dependence of the solar radiation time series. Furthermore, as the number of time-steps increases, the performance of the solar radiation prediction decreases relatively smoothly and slowly. The obtained average coefficient of determination, root mean square error (RMSE), relative RMSE, and mean bias error are 0.9788, 72.91 W/m2, 25.25%, and 38.35 W/m2, respectively. In addition, the average skill score of the transformer model is around 44.9%, which is 20.54% higher than that of the seq2seq-at-LSTM model and about 40.84% higher than that of the seq2seq-LSTM model. Besides, the use of the attention mechanism can explain the improved prediction compared to other models. This model developed in this study could also be used for predictions in other fields, such as wind energy predictions and building energy predictions.

The applicability of sunshine-based global solar radiation models modified with meteorological factors for different climate zones of China

With the development of renewable energy, the exploitation and utilization of solar energy resources also need continuous progress, but solar radiation data shortage has become a serious concern. A method for estimating global solar radiation has been developed to address this issue. The sunshine-based model is currently the most widely used model due to its high calculation accuracy and few input parameters. This paper will first review 13 subcategories (8 categories in total) of the global solar radiation prediction model based on sunshine. Subsequently, the astronomical factors were introduced to modify empirical coefficients, and 8 new categories of models based on sunshine rate were introduced. The radiation data from 83 meteorological stations in China was used to train and validate the model, and the performance of the model was evaluated by using evaluation indicators, such as coefficient of determination (R2), root mean square error (RMSE), mean absolute bias error (MABE), mean bias error (MBE), and global performance index (GPI). The results show that the R2 value of the unmodified empirical model is in a range of 0.82–0.99, and the RMSE value is in a range of 0.018–3.09. In contrast, with the introduction of the astronomical factor, the model accuracy improves significantly, and the modified power function model (N3) gains its best performance. The R2 of model N3 is in a range of 0.86–0.99, and the RMSE value is in a range of 0.018–2.62. The R2 increases by 0.49%, while the RMSE value 6.44%. Above all, it does not require the input of other meteorological parameters for predicting the value of global solar radiation.

Estimation of Global Solar Radiation at Mid Hill Region, Okhaldhunga

Global solar radiation knowledge can be valuable in fulfilling energy demands and exploring numerous applications of solar technology. Since only a few meteorological stations are available and the installation of additional stations is complicated because the cost is very high, a comparative study of various models is essential for accurate global solar radiation prediction rather than measurement. In the present study, four models based on sunshine hour, temperature, and relative humidity for estimating daily global solar radiation were compared using the regression technique for Okhaldhunga (Lat.27.30810 N, Lon.86.50420 E, Alt.1720m.a.s.l.). These models were evaluated and compared based on four statistical error tests. The basis of analysis, model M4, which uses sunshine hour, maximum temperature, and relative humidity, was used for estimating daily global solar radiation with acceptable accuracy. The empirical constants obtained for M4 models were a=0.62, b=0.47, c= -0.003, and d= -0.003. The site's annual average global solar radiation was 4.16 kWh/m2 /day. The results of this study help estimate global solar radiation for similar geographical locations in Nepal.

Predicting daily global solar radiation in various climatic regions of China based on hybrid support vector machines with meta-heuristic algorithms

Accurate prediction of global solar radiation (Rs) is vital for investment decisions and solar energy distribution. In this study, three hybrid models (ACO-SVM, CS-SVM, and GWO-SVM) based on ant colony optimization (ACO), cuckoo search (CS) and grey wolf optimization (GWO) algorithms were proposed to optimize support vector machine (SVM) for predicting Rs in four climate zones of China (temperate continental zone TCZ, mountain plateau zone MPZ, temperate monsoon zone TMZ, and subtropical monsoon zone SMZ). They were compared with the standalone backpropagation neural network model, decision tree, and support vector machines. The results demonstrated that among the standalone models, support vector machines performed best with the highest accuracy in Rs estimation in each climate zone of China, followed by the decision tree and backpropagation neural network models, with a coefficient of determination (R2) in 0.707–0.882, 0.694–0.881, and 0.681–0.850, respectively. In contrast, the hybrid models exhibited higher accuracy than standalone support vector machines in four climatic regions of China, with the coefficient of determination (R2) increasing by 5.361%, 5.476%, 7.382%, and 10.965%, respectively. Among hybrid models, GWO-SVM performed better than CS-SVM, and both had higher accuracy than ACO-SVM, with the coefficient of determination (R2) in 0.809–0.927, 0.804–0.926, and 0.793–0.930, respectively. Therefore, the hybrid models (ACO-SVM, CS-SVM, and GWO-SVM), especially GWO-SVM and CS-SVM, can significantly improve the accuracy for predicting Rs in various regions of China.

A hybrid approach based on complete ensemble empirical mode decomposition with adaptive noise for multi-step-ahead solar radiation forecasting

Abstract Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely. On the other hand, estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components. In this research, a novel hybrid forecasting model, namely complete ensemble empirical mode decomposition with adaptive noise–Gaussian process regression (CEEMDAN–GPR), has been developed for daily global solar radiation prediction. The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets. After that, the GPR model uses these subsets as inputs to perform its prediction. According to the results of this research, the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting, namely wavelet–GPR and wavelet packet–GPR, in terms of mean square error, root mean square error, coefficient of determination and relative root mean square error values, which reached 3.23 MJ/m2/day, 1.80 MJ/m2/day, 95.56%, and 8.80%, respectively (for one-step forward forecasting). The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.

Coupling meteorological stations data and satellite data for prediction of global solar radiation with machine learning models

The simulation of solar radiation is of great significance to the sustainable development of energy, engineering, and many other fields. The Himawari series of satellites has the characteristics of high temporal, spatial resolution, which helps to solve the problem of insufficient ground radiation observation in China. However, the accuracy of this data needs to be further improved. Thus, four machine learning models with 13 ground and satellite-based input combinations were used to simulate daily solar radiation. The results showed that the simulation accuracy of the model based on a combination of meteorological data from different sources was significantly improved compared with the model based on single-source data. The RMSE was 32.4% and 44.6% lower than those of the model based on the ground meteorological stations data and the model based on the satellite data, respectively. SVM13 model showed the optimal simulation performance compared with other models, and its RMSE and R2 were 1.732 MJ m−2 day−1 and 0.939 in each climate region, respectively. Overall, we conclude that the SVM13 model is the most suitable model, and the model with a complex combination of more meteorological factors as input has higher simulation accuracy than the model with a relatively simple input combination.

Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction

Global solar radiation (GSR) prediction plays an essential role in planning, controlling and monitoring solar power systems. However, its stochastic behaviour is a significant challenge in achieving satisfactory prediction results. This study aims to design an innovative hybrid prediction model that integrates a feature selection mechanism using a Slime-Mould algorithm, a Convolutional-Neural-Network (CNN), a Long–Short-Term-Memory Neural Network (LSTM) and a final CNN with Multilayer-Perceptron output (SCLC algorithm hereafter). The proposed model was applied to six solar farms in Queensland (Australia) at daily temporal horizons in six different time steps. The comprehensive benchmarking of the obtained results with those from two Deep-Learning (CNN-LSTM, Deep-Neural-Network) and three Machine-Learning (Artificial-Neural-Network, Random-Forest, Self-Adaptive Differential-Evolutionary Extreme-Learning-Machines) models highlighted a higher performance of the proposed prediction model in all the six selected solar farms. From the results obtained, this work establishes that the designed SCLC algorithm could have a practical utility for applications in renewable and sustainable energy resource management.

Development of a hybrid computational intelligent model for daily global solar radiation prediction

Providing an accurate and reliable solar radiation prediction is highly significant for optimal design and management of thermal and solar photovoltaic systems. It is massively essential for producing renewable and clean energy production. In the current research, an exploration of newly developed computation intelligent models based on the hybridization of Covariance Matrix Adaptive Evolution Strategies (CMAES) with Extreme Gradient Boosting (XGB) and Multi-Adaptive Regression Splines (MARS) models for building robust predictive models for daily scale solar radiation. The proposed hybrid models are examined at four meteorological stations (i.e., Boromo, Dori, Gaoua, and Po) located in the Burkina Faso region, Sub-Sahara Africa. The prediction matrix is constructed using several related climate parameters. Based on the statistical and graphical evaluation, the proposed models demonstrated an outstanding predictability performance. The computational experiments showed that the proposed framework consistently enhanced prediction accuracy by 17.2%, 4.9%, 39.8%, and 44.5%, at Boromo, Dori, Gaoua, and Po stations using the Root Mean Squared Error over the testing phase against the existing methods proposed in the literature. The resulting method has the potential to be used as a surrogate tool for solar radiation prediction on the stations. The findings of this study contribute to a growing body of literature towards advancing the study of machine learning methods for predicting renewable energy production.

Hybridization of statistical machine learning and numerical models for improving beam, diffuse and global solar radiation prediction

Prediction, separation, and improvement of beam (Hb), diffuse (Hd), and global solar radiation (H) using an efficient Gumbel probabilistic model (GP) and hybridization of GP with auto-regression integrated moving average (ARIMA) is essential in regions of Southern Africa and the Middle East. This is because, most of the inhabitants of these localities are not connected to the national grid due to extreme cost implications, frequent power outages, and in most cases, unavailability of the national power supply; as well as the fact that most government meteorological stations are technologically or financially unable to routinely measure these radiometric parameters in most metropolitan cities, developing cities, and remote villages, where there is a severe need for electricity. Although the prediction of H has many advantages in adapting and deploying clean and affordable energy infrastructure through solar photovoltaic (PV) systems, the separation of H into Hb and Hd will further increase cleaner and more affordable systems like solar PV thermal/concentrators, which require Hb for its use. In this era of climate change, Hb, Hd, and H information are needed to detect and adapt climate mitigation plans in places adversely affected by climate change and global warming externalities. Eight different configurations of input combination elements were assembled to stimulate their hybrid evolutionary ARIMA-GP controlled, swapped model through the instrumentality of generalized solar meteorological datasets needed for modeling. The result revealed that the swapped ARIMA models outperformed the controlled and controlled ARIMA models using Gumbel's numerical approach. The best min beam and diffuse irradiance frocontrolled ARIMA models were probabilistically optimized using the Gumbel (GP) probabilistic model to produce ARIMA-GP. From the evaluations of the error metrics, the ARIMA-GP models outperformed the swapped and controlled ARIMA models, as well as the Gumbel models used to separate HB and Hd from H. The selected best performing models produced the RMSE-induced decrease and increase in R2 of 73.73% and 15.25% respectively for GSRML-GP3 (model H), 71.71%, and 14.19% for DSRML-GP5 (model Hd), and 61.91% and 11.70% for DNIML-GP5 (model Hb). These high values of the percentage improvement of the suitable model obtained for the Hb, Hd, and H modeling suggest that the proposed hybrid evolutionary ARIMA-GP model can be adopted to improve the prediction of solar beam, diffuse and global radiation in any region of the world, as the input parameters incorporate the data sets on geo-climatic conditions needed for global modeling. This study also suggests that it is more realistic to use the generalized functional forms GP and ARIMA-GP to separate Hb and Hd from the parameter H and also to generate their corresponding coefficients in the studied regions than applying empirical and machine learning models consolidated in the literature. The proposed ARIMA-GP models are sufficient as a valid evolutionary hybrid model that will accelerate a holistic understanding of the solar resources available in the selected regions and beyond, as well as spread the application of solar photovoltaic/thermal technologies within these countries.

Estimation of monthly global solar radiation over twelve major cities of Libya

This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.

Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms

This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction problems, based on deep learning approaches. Predictive models are useful ploys in solar energy industries to optimize the performance of photovoltaic power systems. Specifically, in this work we develop a new 4-phase hybrid CXGBRFR framework, which includes a deep learning Convolutional Neural Network, an Extreme Gradient Boosting with Random Forest Regression, and a Harris Hawks Optimization for initial feature selection. The proposed system has been tested in a problem of daily GSR prediction at six solar farms in Australia. Data from three global climate models (GCM) (CSIRO-BOM ACCESS1-0, MOHC Hadley-GEM2-CC and MRI MRI-CGCM3) have been considered as predictive (input) variables for the proposed approach. The variables from these GCMs contain enough information to obtain an accurate prediction of the GSR at each solar farm. The performance of the proposed approach is compared against different deep and shallows learning approaches: Deep Belief Network, Deep Neural Network, Artificial Neural Network, Extreme Learning Machine and Multivariate Auto-Regressive Spline models. We show that the proposed approach exhibits an excellent performance in GSR prediction, against all alternative approaches in all solar farms considered.

Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries

The prediction of global solar radiation in a region is of great importance as it provides investors and politicians with more detailed knowledge about the solar resource of that region, which can be very beneficial for large-scale solar energy development. In this sense, the main objective of this study is to predict the daily global solar radiation data of 27 cities (Brussels, Paris, Lisbon, Madrid…), located in 27 countries, which have mostly different solar radiation distributions in Europe. In this research, six different machine-learning algorithms (Linear model (LM), Decision Tree (DT), Support Vector Machine (SVM), Deep Learning (DL), Random Forest (RF) and Gradient Boosted Trees (GBT)) are used. In the training of these algorithms, daily air temperature(Ta), wind speed(Va), relative humidity(RH) and solar radiation of these cities are used. The data is supplied from the Meteonorm tool and cover the last years grouped in two periods (1960–1990; 2000–2019). To decide on the success of these algorithms, four different statistical metrics (Average Relative Error (ARE), Average absolute Error (AAE), Root Mean Squared Error (RMSE), and R2 (R-Squared)) are discussed in the study. In addition, the forecasting of air temperature and global solar radiation of these cities in 2050 and 2100 were made using three of the most recent Intergovernmental Panel on Climate Change (IPCC) scenarios (RCP2.6; RCP 4.5, and RCP 8.5). The results show that ARE, R,2 and RMSE values of all algorithms are ranging from 0.114 to 6.321, from 0.382 to 0.985, from 0.145 to 2.126 MJ/m2, respectively. By analysing all the algorithms, it is noticed that the Decision tree exhibited the worst result in terms of R,2 and RMSE metrics. Among the six prediction algorithms, the DL was recognized as the only algorithm that exceeded the t-critical value (The t-critical value is the cutoff between retaining or rejecting the null hypothesis). Globally, all the six machine learning algorithms used in this research can be applied to predict the daily global solar radiation data with good accuracy. Despite this, the SVM model is the best model among all the six models used. It is followed by the DL, LM, GB, RF and DT, respectively.

Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction

Global Solar Radiation (GSR) prediction models are critical to improve the dispatch, control, and stabilization of solar renewable power, and to integrate the solar energy into the electrical grid system. GSR, especially on a short-term scale, can have important fluctuations, which may affect the total energy expected to be supplied to the grid. To overcome this issue, prediction models with a high forecasting performance are needed. In this paper a novel framework based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Deep Residual Network with Bidirectional long short-term memory, i.e., DRESNET model, is proposed for obtaining accurate multi-step ahead GSR predictions. To train the proposed ICEEMDAN-DRESNET hybrid model, minute-level daylight data from Energy Sector Management Assistance Program in Nepalgunj (mid-western Nepal) are used. The results demonstrate ICEEMDAN-DRESNET model is an excellent tool for short-term solar energy monitoring, yielding excellent predictions, in all metrics such as MAE 9.769 W/m2, MAPE 5.657%, TIC 1.143, CPI 4.739, and TIC 0.023 for 5-min time-horizon predictions, improving the results from the benchmark models. As the forecasting time-horizon is increased, the ICEEMDAN-DRESNET model accuracy drops, with MAE 33.672 W/m2; MAPE 31.749% for 1-hr, MAE 22.625 W/m2; MAPE 18.312% (30-min) and MAE 14.897 W/m2; MAPE 10.358% (15-min), also better than the benchmark models. The results confirm the competitive merit of ICEEMDAN and DRESNET integration to improve deep learning and the potential of proposed model for the monitoring of solar or other renewable (e.g., wind or solar) energies.