Accurate Solar Radiation Prediction Research Articles

Forecasting daily solar radiation: An evaluation and comparison of machine learning algorithms

Rising energy demand, resource depletion, and environmental issues tied to fossil fuels demand a transition to renewable energy. Solar power, abundant and well-established, presents a promising solution to address our expanding energy requirements. The sun radiates an astonishing amount of energy every second, far more than humanity’s current and future energy needs. Accurate solar radiation prediction is crucial for optimizing solar panel design, placement, and grid integration. This paper aims to predict daily global solar radiation data for six Pakistani cities: Karachi, Lahore, Islamabad, Quetta, Peshawar, and Multan. It highlights the importance of advanced algorithms and introduces an innovative data collection method using pyranometer sensors and microcontrollers, making data storage and analysis more affordable and efficient while reducing the financial burdens associated with traditional equipment. Focusing on Pakistan’s diverse solar radiation potential, this research evaluates eight machine learning algorithms using seven key statistical metrics to understand and compare their performance in predicting solar radiation. Four algorithms, k-nearest neighbors, Random Forest Regression, Gradient Boosting Regression, and Support Vector Regression (SVR), consistently exhibit remarkable precision, achieving outstanding R2 values of up to 99%. This highlights the crucial role of algorithm selection in solar radiation prediction, with SVR emerging as the top choice. SVR’s precise and reliable forecasts empower renewable energy planning and decision-making. This study provides valuable guidance for decision-makers to optimize solar energy utilization across diverse geographical regions and contributes invaluable insights to the field of renewable energy forecasting.

Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNet

Solar energy has emerged as a key industry in the field of renewable energy due to its universality, harmlessness, and sustainability. Accurate prediction of solar radiation is crucial for optimizing the economic benefits of photovoltaic power plants. In this paper, we propose a novel spatiotemporal attention mechanism model based on an encoder-translator-decoder architecture. Our model is built upon a temporal AttUNet network and incorporates an auxiliary attention branch to enhance the extraction of spatiotemporal correlation information from input images. And utilize the powerful ability of edge intelligence to process meteorological data and solar radiation parameters in real-time, adjust the prediction model in real-time, thereby improving the real-time performance of prediction. The dataset utilized in this study is sourced from the total surface solar incident radiation (SSI) product provided by the geostationary meteorological satellite FY4A. After experiments, the SSIM has been improved to 0.86. Compared with other existing models, our model has obvious advantages and has great prospects for short-term prediction of surface solar incident radiation.

Enhanced Convolutional Neural Network for solar radiation nowcasting: All-Sky camera infrared images embedded with exogeneous parameters

Electrical power production by renewable energy sources is unpredictable in nature and this may cause imbalance between power generation and demand. Therefore, an accurate prediction of solar radiation is crucial for the stability and efficient management of electric grid. This study focuses on very short-term forecasts of solar radiation with a horizon in the range of 5–15 min. In this paper, a Convolutional Neural Network is proposed that uses sequences of infrared images captured by an All-Sky Imager to forecast the Global Horizontal Irradiance on different time horizon. A real case study, exploiting six months of high-resolution data, is analyzed. Additionally, an innovative technique, the Enhanced Convolutional Neural Network (ECNN), is proposed in which exogenous data, as the solar radiation measurement, is encoded in terms of colored pixels in the upper corner of the images. Considering the naïve persistence method as a baseline, a clear improvement across the key metrics has been noted with the proposed methodology. A deeper analysis of the results reveals that the proposed models are more accurate than persistence when high fluctuations of solar radiation are experienced. In that case, the ECNN achieves a forecast skill exceeding 19% for all the tested forecast horizons.

Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model

Solar energy, as a clean energy source, has tremendous potential for utilization. The advancement of solar energy utilization technology has led to an increasing demand for solar energy, resulting in a growing need for the accurate prediction of solar radiation. The main objective of this study is to develop a novel model for predicting solar radiation intervals, in order to obtain accurate and high-quality predictions. In this study, the daily sunshine duration (SD), average relative humidity (RHU), and daily average temperature (AT) were selected as the indicators affecting the daily global solar radiation (DGSR). The empirical study conducted in this research utilized daily solar radiation data and daily meteorological data collected at the Hami station in Xinjiang from January 2009 to December 2016. In this study, a novel solar radiation interval prediction model was developed based on the concept of “point prediction + interval prediction”. The Conformer model was employed for the point prediction of solar radiation, while the Generalized Laplace (GLaplace) distribution was chosen as the prior distribution to account for the prediction error. Furthermore, the Solar DeepAR Forecasting (SDAR) model was utilized to estimate parameters of the fitted residual distribution and achieve the interval prediction of solar radiation. The results showed that both models performed well, with the Conformer model achieving a Mean Squared Error (MSE) of 0.8645, a Mean Absolute Error (MAE) of 0.7033 and the fitting coefficient R2 of 0.7751, while the SDAR model demonstrated a Coverage Width-based Criterion (CWC) value of 0.068. Compared to other conventional interval prediction methods, our study’s model exhibited superior accuracy and provided a more reliable solar radiation prediction interval, offering valuable information for ensuring power system safety and stability.

Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction

The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction.

Construction and Research of Ultra-Short Term Prediction Model of Solar Short Wave Irradiance Suitable for Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau region has abundant solar energy, which presents enormous potential for the development of solar power generation. Accurate prediction of solar radiation is crucial for the safe and cost-effective operation of the power grid. Therefore, constructing a suitable ultra-short-term prediction model for the Tibetan Plateau region holds significant importance. This study was based on the autoregressive integrated moving average model (ARIMA), random forest model (RF), and long short-term memory model (LSTM) to construct a prediction model for forecasting the average irradiance for the next 10 min. By locally testing and optimizing the model parameter, the study explored the applicability of each model in different seasons and investigates the impact of factors such as training dataset and prediction time range on model accuracy. The results showed that: (1) the accuracy of the ARIMA model was lower than the persistence model used as a reference model, while both the RF model and LSTM model had higher accuracy than the persistence model; (2) the sample size and distribution of the training dataset significantly affected the accuracy of the models. When both the season (distribution) and sample size were the same, RF achieved the highest accuracy. The optimal sample sizes for ARIMA, RF, and LSTM models in each season were as follows: spring (3564, 1980, 4356), summer (2772, 4752, 2772), autumn (3564, 3564, 4752), and winter (3168, 3168, 4752). (3) The prediction forecast horizon had a significant impact on the model accuracy. As the forecast horizon increased, the errors of all models gradually increased, reaching a peak between 80 and 100 min before slightly decreasing and then continuing to rise. When both the season and forecast horizon were the same, RF had the highest accuracy, with an RMSE lower than ARIMA by 65.6–258.3 W/m2 and lower than LSTM by 3.7–83.3 W/m2. Therefore, machine learning can be used for ultra-short-term forecasting of solar irradiance in the Qinghai–Tibet Plateau region to meet the forecast requirements for solar power generation, providing a reference for similar studies.

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

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

English

Renewable sources are essential in fulfilling affordable, clean, and sustainable energy requirements. Consistent wind speed forecasting and accurate solar radiation prediction are required to minimize economic losses and enhance the security of power usage. Predicting solar radiation and wind speed is difficult due to their uncertain behaviour. Motivated by these aspects, this study implemented an Internet of Things (IoT) and artificial neural network (ANN) based system for predicting solar radiation and wind speed. Initially, IoT and long-range (LoRa) enabled hardware was developed for obtaining real-time data. In this study, customized hardware was developed with the amalgamation of LoRa, WI-Fi and many sensors. Solar grid and windmill sensor nodes are customized hardware deployed to collect real-time data to create datasets that predict solar radiation and wind speed using wind speed, solar radiation, humidity, temperature, voltage, and current parameters. For the prediction, in this study, we have employed the ANN techniques, i.e., Levenberg Marquardt (LM) and Bayesian regularization (BR) methods. We have found that the proposed ANN framework using the LM algorithm provides accuracy in the prediction compared to the BR algorithm with less root mean square error (RMSE), mean square error (MSE), fast convergence speed, and more significant Correlation coefficient (R).

Multi-model approach applied to meteorological data for solar radiation forecasting using data-driven approaches

The accuracy of the solar forecasting model depends upon various factors such as attributes of the data, time horizon, time interval, length of the data, etc. This means attributes of data selection are an important feature of any solar forecasting model. A lot of research has been done on solar forecasting using deep learning and machine learning approaches, providing better accuracy for a particular type of model and data. However, how to accurately apply the multi-model techniques in the data-driven approaches of the solar radiation prediction model is still a challenging issue due to the complexity of the temporal resolution, length, and attribute selection of the data. This paper proposes a multi-model technique using data-driven approaches to get an improvised solar forecasting model with respect to the attributes of data. In this research, a multi-model method based on data-driven approaches is developed for short-term solar radiation forecasting. Three years of weather data with different temporal resolutions taken from reliable online sources were used for training and testing in this work. The weather data considered various input parameters such as temperature, humidity, pressure, wind speed, and wind direction; solar radiation data is considered the model’s target variable. This work is a two-stage process. In the first stage, multi-model methods are analyzed based on solar forecasts at different temporal resolutions, and best-performing models are selected. The optimization technique is applied to the best model in the second stage to obtain an improvised solar forecasting model. It is observed that the multi-model forecasts provide better accuracy in terms of R2, nRMSE, and MAE than the conventional forecasting model. In this work, the performance parameters of nRMSE = 0.34, MAE = 4.71, and R2 = 0.93 are obtained before optimization, and nRMSE = 0.25, MAE = 1.39 and R2 = 0.96 are obtained after optimization in the testing stage for an hourly dataset. For daily datasets, the performance parameters nRMSE = 0.24, MAE = 3.19, and R2 = 0.93 are obtained before optimization, and nRMSE = 0.16, MAE = 2.61, and R2 = 0.95 are obtained after optimization in the testing stage. As a result, the proposed algorithm focused on an efficient model to predict solar radiation. Accurate solar radiation prediction models are highly in demand in various smart grid applications. Therefore, the proposed work may help in suggesting a suitable model for solar energy forecasting.

Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output

Solar radiation's intermittent and fluctuating nature poses severe limitations for most applications. Accurate prediction of solar radiation is an essential factor in predicting the output power of a photovoltaic power system. For this purpose, the potential of the 20 MW solar photovoltaic power plant in Zahedan city has been evaluated in this article. With the help of monthly data (1984–2021) and MLP, LSTM, GRU, CNN, and CNN-LSTM models, solar radiation and temperature are predicted for the next ten years. CNN exhibits the best performance compared to other models with four input parameters: global horizontal irradiance, temperature, surface pressure, relative humidity (RH), and two outputs of temperature and radiation. The root mean square error values for global horizontal irradiance and temperature were 12.68 W/m2 and 1.75 °C, respectively. Relative humidity exhibited more significant effect on the model in comparison with surface pressure. Finally, the average annual power output for ten years from 2022 to 2031 is calculated and predicted to be 50.37 GWh.

Solar irradiance prediction based on self-attention recursive model network

In recent years, with the continued development and popularity of sustainable energy sources and the increasing utilization of solar energy, accurate solar radiation prediction has become important. In this paper, we propose a new model based on deep learning, Feature-enhanced Gated Recurrent Unit, hereafter referred to as FEGRU, for solar radiation prediction. This model takes the source data with one-dimensional convolution and self-attention to feature attention and processes the data features, and then GRU performs feature extraction on solar irradiance data. Finally, the data dimensionality is transformed by a fully connected layer. The main advantage of FEGRU is that it does not require auxiliary data, but only time series data of solar irradiance can be used for good solar irradiance prediction. Our experiments with solar irradiance samples in Lyon, France, show that our model has better prediction results than the baseline model.

Inter-Hour Forecast of Solar Radiation Based on Long Short-Term Memory with Attention Mechanism and Genetic Algorithm

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are used to predict the global horizontal irradiance and direct normal irradiance after 5, 10, and 15 min. The proposed AGA-LSTM model was trained and tested with two years of data from the public database Solar Radiation Research Laboratory site of the National Renewable Energy Laboratory. The experimental results show that under the three prediction scales, the prediction performance of the AGA-LSTM model is below 20%, which effectively improves the prediction accuracy compared with the continuous model and some public methods.

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

Solar radiation is the Earth’s primary source of energy and has an important role in the surface radiation balance, hydrological cycles, vegetation photosynthesis, and weather and climate extremes. The accurate prediction of solar radiation is therefore very important in both the solar industry and climate research. We constructed 12 machine learning models to predict and compare daily and monthly values of solar radiation and a stacking model using the best of these algorithms were developed to predict solar radiation. The results show that meteorological factors (such as sunshine duration, land surface temperature, and visibility) are crucial in the machine learning models. Trend analysis between extreme land surface temperatures and the amount of solar radiation showed the importance of solar radiation in compound extreme climate events. The gradient boosting regression tree (GBRT), extreme gradient lifting (XGBoost), Gaussian process regression (GPR), and random forest models performed better (poor) prediction capabilities of daily and monthly solar radiation. The stacking model, which included the GBRT, XGBoost, GPR, and random forest models, performed better than the single models in the prediction of daily solar radiation but showed no advantage over the XGBoost model in the prediction of the monthly solar radiation. We conclude that the stacking model and the XGBoost model are the best models to predict solar radiation.

Evolutionary artificial neural networks for accurate solar radiation prediction

This paper evaluates the performance of different evolutionary neural network models in a problem of solar radiation prediction at Toledo, Spain. The prediction problem has been tackled exclusively from satellite-based measurements and variables, which avoids the use of data from ground stations or atmospheric soundings. Specifically, three types of neural computation approaches are considered: neural networks with sigmoid-based neurons, radial basis function units and product units. In all cases these neural computation algorithms are trained by means of evolutionary algorithms, leading to robust and accurate models for solar radiation prediction. The results obtained in the solar radiation estimation at the radiometric station of Toledo show an excellent performance of evolutionary neural networks tested. The structure sigmoid unit-product unit with evolutionary training has been shown as the best model among all tested in this paper, able to obtain an extremely accurate prediction of the solar radiation from satellite images data, and outperforming all other evolutionary neural networks tested, and alternative Machine Learning approaches such as Support Vector Regressors or Extreme Learning Machines.

Solar radiation prediction based on random forest of feature-extraction

In order to use solar energy efficiently, the accurate prediction of solar radiation is extremely important. The aim of this paper is to discuss and build the solar radiation prediction model, and an efficient method based on improving random forest is proposed in here. The random forest regression model is created for predicting solar radiation. The nine variables are taken as input of the original model, and the solar radiation as the output. The original data is normalized using Min-Max method and is mapped to the values in the range of zero to one. The large amount of radiation data has been dealt with univariate feature extraction. The classifier of the random forest tree is constructed by characteristic factors that can influence solar radiation, and the optimal parameters of the model are selected by the OOB error analysis. The relationship between M-try and OOB can be obtained by computing of the R platform. Hence, a prediction model of improved random forest solar radiation based on feature extraction has been described. Under the specific area of solar radiation forecast, the studies have been done, and the solar radiation is analyzed and compared with the different prediction methods. The simulation results prove the validity of the improved method; the root mean square error is reduced and the precision of prediction is improved. It is significant for the prediction of the amount of solar radiation under complex environment and the effective use of photovoltaic power generation.

A Hybrid PSO-ANFIS Approach for Horizontal Solar Radiation Prediction in Nigeria

For efficient and reliable hydrogen production via solar photovoltaic system, it is important to obtain accurate solar radiation data. Though there are equipment specifically designed for solar radiation prediction but are very expensive and have high maintenance cost that most countries like Nigeria are unable to purchase. In this study, the accuracy of a hybrid PSO-ANFIS method is examined to predict horizontal solar radiation in Nigeria. The prediction is done based on the available meteorological data obtained from NIMET Nigeria. The meteorological data used for this study are monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours, which serves as inputs to the developed model. The model accuracy is evaluated using two statistical indicators Root Mean Square Error (RMSE) and Coefficient of determination (R²). The accuracy of the proposed model is validated using ANFIS, GA-ANFIS models and other literatures. Based on the statistical parameters used for the model evaluation, the results obtained proves PSO-ANFIS as a good model for predicting solar radiation with the values of RMSE=0.68318, R²=0.9065 at the training stage and RMSE=1.3838, R²=0.8058 at the testing stage. This proves the potentiality of PSO-ANFIS technique for accurate solar radiation prediction

The potential of novel data mining models for global solar radiation prediction

Advance knowledge of solar radiation is highly essential for multiple energy devotions such as sustainability in energy production and development of solar energy system. The current research investigates the capability of four data mining computation models, namely random forest (RF), random tree, reduced error pruning trees and hybrid model of random committee with random tree reduce (RC) for predicting daily measured solar radiation at four locations of Burkina Faso, i.e., Bur Dedougou, Bobo-Dioulasso, Fada-Ngourma and Ouahigouya. Daily data of seven climatic variables, namely maximum and minimum air temperature, maximum and minimum relative humidity, wind speed, evaporation and vapor pressure deficit, for the period 1998–2012 are used for solar radiation prediction. Different combinations of input variables are used according to correlation coefficient between the predictors and predictand, and the best input combination is selected based on the sensitivity of model output measured in terms of statistical indices. The obtained results are found consistence for all the meteorological stations. The highest accuracy in prediction is found when all the climate variables are used as input. The RC and RF showed the minimal absolute error in prediction at all the stations. The RMSE and NSE are found in the range of 0.03–0.05 and 0.77–0.91 for RC and 0.03–0.05 and 0.78–0.92 for RF at different stations. The results indicate that the proposed data mining models can be used for accurate prediction of solar radiation over the Burkina Faso.

Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation

Solar energy plays a vital role in the field of sustainable energy by providing clean, efficient and reliable alternative source of energy. Where, the output of solar energy systems is highly dependent on the solar radiation. Thus, accurate prediction of solar radiation is considered as a very important factor for such applications. In this paper, standalone adaptive neuro-fuzzy inference system and hybrid models have been developed to predict monthly global solar radiation from different meteorological parameters such as sunshine duration S(h), and air temperature. The proposed hybrid models include particle swarm optimization, genetic algorithm and differential evolution. To evaluate the capability and efficiency of the proposed models, several statistical indicators such as; root mean square error, co-efficient of determination and mean absolute bias error are used. All prediction models’ results showed good agreements with measured datasets. The performance evaluation over different statistical indicators showed high correlation for all developed modules. Whereas, hybrid particle swarm optimization has achieved the best statistical indicators over all models in training and testing models. A detailed comparison with other studies is carried out to validate the prediction accuracy and suitability of the proposed models. The results showed that the developed hybrid models have the most reliable and accurate estimation capability and deemed to be the efficient methods for predicting global solar radiation for various applications.

Solar radiation forecast using hybrid SARIMA and ANN model

Solar Energy have an enormous potential for generating renewable electricity. In the tropics solar energy are abundance all year long but suffer from uncertainty caused by rain and clouds. Accurate prediction of solar radiation can increase the affectivity and productivity of solar energy sources. Monthly average of solar radiation data are obtained from stations in Malaysia. The data are modeled using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, artificial neural network (ANN) model and Hybrid ANN and SARIMA model. The SARIMA model is a reliable tool in forecasting seasonal data, on the other hand the ANN model have been proven to be a good model in forecasting non-linear data. By combining both model a more accurate model can be obtained. Finally the forecasting performance each model is compared by using mean absolute error (MAE), the mean absolute percentage error (MAPE) and root mean square error (RMSE). The result shows that the hybrid model is better in forecasting solar radiation data.

Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method

Accurate prediction of solar radiation is essential for optimal design of solar systems. This paper presents an innovative artificial intelligence approach for the determination of the daily solar radiation. A new nonlinear model was developed to predict the daily solar radiation on horizontal surface using a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA. This method uses SA-based temperature cycling to improve the ANN calibration performance. A calculation procedure was presented to interpret the ANN/SA model and transform it into a practical design equation. The ANN/SA technique formulates the daily solar radiation in terms of several meteorological parameters. Thousands of daily observations during 1995–2014 in a nominal city in Iran were used to develop the solar radiation models. Validity of the model was verified through different phases. Sensitivity analysis was conducted and discussed. The ANN/SA model accurately predicts the daily solar radiation and outperforms the ANN, support vector machines (SVM), and existing regression and machine learning models.