Estimation of Hourly Global Solar Radiation Using Deep Learning Algorithms

2020 ◽  
Author(s):  
Chibuzor N Obiora ◽  
Ahmed Ali ◽  
Ali N Hasan
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Learning Algorithms ◽  
Global Solar Radiation

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

scholarly journals Multi-step ahead forecasting of global solar radiation for arid zones using deep learning

2020 ◽  
Vol 167 ◽  
pp. 626-635 ◽  
Author(s):  
Deeksha Chandola ◽  
Harsh Gupta ◽  
Vinay Anand Tikkiwal ◽  
Manoj Kumar Bohra
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Arid Zones

Estimation of daily global solar radiation using deep learning model

Energy ◽  
2018 ◽  
Vol 162 ◽  
pp. 126-135 ◽  
Author(s):  
Kazım Kaba ◽  
Mehmet Sarıgül ◽  
Mutlu Avcı ◽  
H. Mustafa Kandırmaz
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Learning Model ◽  
Deep Learning Model

Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions

2021 ◽  
Author(s):  
Olusola Bamisile ◽  
Ariyo Oluwasanmi ◽  
Chukwuebuka Ejiyi ◽  
Nasser Yimen ◽  
Sandra Obiora ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Solar Radiation ◽  
Learning Algorithms ◽  
Diffuse Solar Radiation

scholarly journals A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

2020 ◽  
Vol 34 (5) ◽  
pp. 563-569
Author(s):  
Mohammed Ali Jallal ◽  
Abdessalam El Yassini ◽  
Samira Chabaa ◽  
Abdelouhab Zeroual ◽  
Saida Ibnyaich
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Solar Radiation ◽  
Missing Values ◽  
Learning Algorithm ◽  
Meteorological Data ◽  
Electrical Power ◽  
Autoregressive Process ◽  
Global Solar Radiation ◽  
Elman Neural Network

Nowadays, the studies that address solar radiation (SR) forecasting tend to focus on the implementation of conventional techniques. This provides good results, but researchers should focus on the creation of new methodologies that help us in going further and boost the prediction accuracy of SR data. The prime aim of this research study is to propose an efficient deep learning (DL) algorithm that can handle nonlinearities and dynamic behaviors of the meteorological data, and generate accurate real-time forecasting of hourly global solar radiation (GSR) data of the city of El Kelaa des Sraghna (32°2’53”N 7°24’30”W), Morocco. The proposed DL algorithm integrates the dynamic model named Elman neural network with a new input configuration-based autoregressive process in order to learn from the seasonal patterns of the historical SR measurements, and the actual measurements of air temperature. The attained performance proves the reliability and the accuracy of the proposed model to forecast the hourly GSR time series in case of missing values detection or pyranometer damage. Hence, electrical power engineers can adopt this forecasting tool to improve the integration of solar power resources into the power grid system.

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.

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