Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study

The global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Relizane) in the west region of Algeria. The inputs used in the neural network are: time (h), day, month, year, temperature (k), relative humidity (%), pressure (mbar), wind speed (m/s), wind direction (°), and rainfall (kg/m2). The neural network-optimal model was trained and tested using 80 %, and 20 % of whole data, respectively. The best results were obtained with the structure 10-25-1 (10 inputs, 25 hidden, and 1 output neurons) presented an excellent agreement between the calculated and the experimental data during the test stage with a correlation coefficient of R = 0.9879, root means squared error of RMSE = 47.7192 (Wh/m2), mean absolute error MAE = 27.7397 (Wh/m2), and mean squared error MSE = 2.2771e+03(Wh/m2), considering a three-layer Feedforward neural network with Regularization Bayesienne (trainbr)  training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. The results demonstrate proper ANN’s predictions with a root mean square error (RMSE) of less than 0.50 (Wh/m2) and coefficient of correlation (R) higher than 0.98, which can be considered very acceptable. This model can be used for designing solar energy systems in the hottest regions

Developed nonlinear model based on bootstrap aggregated neural networks for predicting global hourly scale horizontal irradiance

This research study examines the use of two models of artificial intelligence based on a single neural network (SNN) and bootstrap aggregated neural networks (BANN) for the prediction value of hourly global horizontal irradiance (GHI) received over one year in Tamanrasset City (Southern Algeria). The SNN and BANN were created using overall data points. To improve the accuracy and durability of neural network models generated with a limited amount of training data, stacked neural networks are developed. To create many subsets of training data, the training dataset is re-sampled using bootstrap re-sampling with replacement. A neural network model is created for each set of training datasets. A stacked neural network is created by combining multiple individual neural networks (INN). For the testing phase, higher correlation coefficients (R = 0.9580) were discovered when experimental global horizontal irradiance (GHI) was compared to predicted global horizontal irradiance (GHI). The performance of the models (INN, BANN, and SNN) demonstrates that models generated with BANN are more accurate and robust than models built with individual neural networks (INN) and (SNN)

Predviđanje globalnog Sunčeva zračenja po satu: usporedba neuronske mreže / bootstrap agregacija

This research work explores the use of single neural networks and bootstrap aggregated neural networks for predicting hourly global solar radiation. A database of 3606 data points were from the Renewable Energies Development Center, radiometric station ‘Shems’ of Bouzareah. The single neural networks and bootstrap aggregated neural networks were built together. The precision and durability of neural network models generated with an incomplete quantity of training datasets were improved using bootstrap aggregated neural networks. To produce numerous sets of training data points, the training data was re-sampled utilising bootstrap resampling by replacement. A neural network model was built for each of the data points. The individual neural network models were then combined to produce the bootstrap aggregated neural networks. The experimental and predicted values of global solar radiation were compared, and lower root mean squared errors (68.3968 and 62.4856 Wh m–2) were discovered during the testing phases for single neural networks and bootstrap aggregated neural networks, respectively. The results of these models show that the bootstrap aggregated neural networks model is more accurate and robust than single neural networks.U ovom radu istražena je primjena pojedinačnih i bootstrap agregiranih neuronskih mreža u predviđanju globalnog Sunčeva zračenja po satu. Baza od 3606 podatkovnih točaka dobivena je iz Centra za razvoj obnovljivih izvora energije, radiometrijske postaje ‘Shems’ u Bouzareahu. Pojedinačne neuronske mreže i bootstrap agregirane neuronske mreže izgrađene su zajedno. Preciznost i trajnost modela neuronskih mreža generiranih uz nepotpuni set podataka za treniranje poboljšani su primjenom bootstrap agregiranih neuronskih mreža. Da bi se proizveli brojni setovi podataka za treniranje, primijenjeno je ponovljeno uzorkovanje podataka primjenom metodologije slučajnog uzorkovanja sa zamjenom. Za svaku podatkovnu točku izgrađena je neuronska mreža. Pojedinačne neuronske mreže su potom kombinirane u bootstrap agregirane neuronske mreže. Uspoređene su eksperimentalne i predviđene vrijednosti globalnog Sunčeva zračenja te su tijekom faza testiranja dobivene niže vrijednosti srednje kvadratne pogreške za pojedinačne odnosno bootstrap agregirane neuronske mreže (68,3968 i 62,4856 Wh m–2). Rezultati su pokazali da je model bootstrap agregiranih neuronskih mreža precizniji i robusniji od pojedinačnih neuronskih mreža

Prediction of Hourly Global Solar Radiation: Comparison of Neural Networks / Bootstrap Aggregating

This research work explores the use of single neural networks and bootstrap aggregated neural networks for predicting hourly global solar radiation. A database of 3606 data points were from the Renewable Energies Development Center, radiometric station ‘Shems’ of Bouzareah. The single neural networks and bootstrap aggregated neural networks were built together. The precision and durability of neural network models generated with an incomplete quantity of training datasets were improved using bootstrap aggregated neural networks. To produce numerous sets of training data points, the training data was re-sampled utilising bootstrap resampling by replacement. A neural network model was built for each of the data points. The individual neural network models were then combined to produce the bootstrap aggregated neural networks. The experimental and predicted values of global solar radiation were compared, and lower root mean squared errors (68.3968 and 62.4856 Wh m−2) were discovered during the testing phases for single neural networks and bootstrap aggregated neural networks, respectively. The results of these models show that the bootstrap aggregated neural networks model is more accurate and robust than single neural networks

Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations in Algeria with different climatic zones were used to develop the model. Various error measurements were used to determine the accuracy of the prediction models, including the correlation coefficient, the mean absolute error, and the root mean square error (RMSE). The optimal support vector machine (SVM) model showed exceptional efficiency during the training phase, with a high correlation coefficient (R = 0.99) and a low mean absolute error (MAE = 26.5741 Wh/m2), as well as an RMSE of 38.7045 Wh/m² across all phases. Overall, this study highlights the importance of accurate prediction models in the renewable energy, which can contribute to better energy management and planning.Validerad;2024;Nivå 2;2024-01-25 (signyg);Full text license: CC BY-4.0</p