Deep Neural Networks for Wind and Solar Energy Prediction

2017 ◽  
Vol 46 (3) ◽  
pp. 829-844 ◽  
Author(s):  
David Díaz–Vico ◽  
Alberto Torres–Barrán ◽  
Adil Omari ◽  
José R. Dorronsoro
Keyword(s):  
Neural Networks ◽  
Solar Energy ◽  
Deep Neural Networks ◽  
Energy Prediction

Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

2015 ◽  
Vol 34 (5) ◽  
pp. 1528-1535
Author(s):  
Ali Wadi Abbas Al-Fatlawi ◽  
Nasrudin Abdul Rahim ◽  
Rahman Saidur ◽  
Thomas Arthur Ward
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Peninsular Malaysia ◽  
Complex Topography ◽  
Energy Prediction ◽  
Artificial Neural

scholarly journals Solar Energy Prediction for Malaysia Using Artificial Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
K. Sopian ◽  
M. Mahmoud
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Clearness Index ◽  
Energy Prediction ◽  
Global Solar Irradiation ◽  
Artificial Neural

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

scholarly journals Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2407 ◽  
Author(s):  
Ghimire ◽  
Deo ◽  
Raj ◽  
Mi
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Renewable Energy ◽  
Deep Learning ◽  
Solar Radiation ◽  
Solar Energy ◽  
Deep Neural Networks ◽  
Global Solar Radiation ◽  
Hidden Layer

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks

Energy Supply Management Using the Solar Energy Prediction in Smart Grid

2016 ◽  
Vol 11 (5) ◽  
pp. 486
Author(s):  
Abdelilah Kahaji ◽  
Rachid Alaoui ◽  
Sadik Farhat ◽  
Lahoussine Bouhouch
Keyword(s):  
Solar Energy ◽  
Smart Grid ◽  
Energy Supply ◽  
Supply Management ◽  
Energy Prediction
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