Intra-hour Forecasting of Direct Normal Solar Irradiance Using Variable Selection with Artificial Neural Networks

2018 ◽  
pp. 281-290
Author(s):  
Hanane Atmani ◽  
Hassen Bouzgou ◽  
Christian A. Gueymard
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Variable Selection ◽  
Solar Irradiance ◽  
Artificial Neural

scholarly journals Prediction of Solar Irradiance Based on Artificial Neural Networks

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 45 ◽  
Author(s):  
Waleed I. Hameed ◽  
Baha A. Sawadi ◽  
Safa J. Al-Kamil ◽  
Mohammed S. Al-Radhi ◽  
Yasir I. A. Al-Yasir ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Irradiance ◽  
Open Circuit Voltage ◽  
Low Cost ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Open Circuit ◽  
Pv Module ◽  
Artificial Neural

Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.

Sky image-based solar irradiance prediction methodologies using artificial neural networks

2019 ◽  
Vol 134 ◽  
pp. 837-845 ◽  
Author(s):  
Jane Oktavia Kamadinata ◽  
Tan Lit Ken ◽  
Tohru Suwa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Irradiance ◽  
Artificial Neural

Solar Irradiance Fluctuation Prediction Methodology Using Artificial Neural Networks

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
Jane Oktavia Kamadinata ◽  
Tan Lit Ken ◽  
Tohru Suwa
Keyword(s):  
Neural Networks ◽  
Power Generation ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Solar Irradiance ◽  
Movement Direction ◽  
Simple Method ◽  
Photovoltaic Power ◽  
Sampling Points ◽  
Artificial Neural

Abstract Renewable energy is an attractive alternative source of energy to fossil fuels, as it can help prevent global warming and air pollution. Solar energy, one of the most promising renewable energy sources, can be converted into electricity using photovoltaic power generation systems. Anywhere on the Earth, solar irradiance generally fluctuates during the day but depends on atmospheric conditions. Thus, when a photovoltaic power generation system is connected to a conventional electricity network, predicting near-future global solar irradiance, especially its drastic increases and decreases, is critical to stabilize the network. In this research, a simple method utilizing artificial neural networks to predict large increases and decreases in global solar irradiance is developed. The red–blue ratio (RBR) values, which are extracted from a set of sampling points in images of the sky, as well as the corresponding global solar irradiance values, are used as the artificial neural network inputs. The direction of the movement of clouds is predicted using RBR data at the sampling points. Then, solar irradiance is predicted using the RBR values along the axis closest to the predicted cloud movement direction and the corresponding solar irradiance measurements. The proposed methodology is able to predict both large increases and decreases in solar irradiance greater than 50 through 100 W/m2 1 min in advance with a 40% prediction error. A significant reduction in computational effort is achieved compared to existing sky image-based methodologies using limited sky image data.

Intra-day Solar Irradiance Forecasting Based on Artificial Neural Networks

2019 ◽  
Author(s):  
Spyros Theocharides ◽  
Marios Kynigos ◽  
Marios Theristis ◽  
George Makrides ◽  
George E. Georghiou
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Irradiance ◽  
Artificial Neural

scholarly journals Analysis of Artificial Neural Networks for Forecasting Photovoltaic Energy Generation with Solar Irradiance

2021 ◽  
Vol 64 (spe) ◽  
Author(s):  
Joylan Nunes Maciel ◽  
Victor Hugo Wentz ◽  
Jorge Javier Gimenez Ledesma ◽  
Oswaldo Hideo Ando Junior
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Irradiance ◽  
Energy Generation ◽  
Photovoltaic Energy ◽  
Artificial Neural

scholarly journals Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural Networks

2018 ◽  
Vol 8 (1) ◽  
pp. 497 ◽  
Author(s):  
Mohammad H. Alomari ◽  
Jehad Adeeb ◽  
Ola Younis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Systems ◽  
Output Power ◽  
Solar Irradiance ◽  
Machine Learning Algorithms ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Intensive Training ◽  
Artificial Neural

In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.

scholarly journals Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249206
Author(s):  
Linus Aronsson ◽  
Roland Andersson ◽  
Daniel Ansari
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Variable Selection ◽  
Lasso Regression ◽  
Term Survival ◽  
Long Term Survival ◽  
Artificial Neural ◽  
Invasive Ipmn

Prediction of long-term survival in patients with invasive intraductal papillary mucinous neoplasm (IPMN) of the pancreas may aid in patient assessment, risk stratification and personalization of treatment. This study aimed to investigate the predictive ability of artificial neural networks (ANN) and LASSO regression in terms of 5-year disease-specific survival. ANN work in a non-linear fashion, having a potential advantage in analysis of variables with complex correlations compared to regression models. LASSO is a type of regression analysis facilitating variable selection and regularization. A total of 440 patients undergoing surgical treatment for invasive IPMN of the pancreas registered in the Surveillance, Epidemiology and End Results (SEER) database between 2004 and 2016 were analyzed. The dataset was prior to analysis randomly split into a modelling and test set (7:3). The accuracy, precision and F1 score for predicting mortality were 0.82, 0.83 and 0.89, respectively for ANN with variable selection compared to 0.79, 0.85 and 0.87, respectively for the LASSO-model. ANN using all variables showed similar accuracy, precision and F1 score of 0.81, 0.85 and 0.88, respectively compared to a logistic regression analysis. McNemar´s test showed no statistical difference between the models. The models showed high and similar performance with regard to accuracy and precision for predicting 5-year survival status.

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