A novel method to forecast 24 h of global solar irradiation

Saoud, Lyes Saad; Rahmoune, F.; Tourtchine, Victor; Baddari, Kamel

doi:10.1007/s12667-016-0218-4

Cited by 12 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Solar radiation was studied by Saoud et al (2018) through a quaternion-valued neural network for short-term forecasting purposes. Some works use neural network techniques to determine PV energy production (see, for example, Monteiro et al 2017; Yousif et al 2017;Graditi et al 2016).…”

Section: Climatic Variablesmentioning

confidence: 99%

A multivariate model for hybrid wind–photovoltaic power production with energy portfolio optimization

Casula¹,

D’Amico²,

Masala³

et al. 2022

JEM

View full text Add to dashboard Cite

Generation of renewable energy is destined to grow further, motivated, for example, by the Paris Agreement, aimed at reducing the production of greenhouse gases. In particular, hybrid production plants allow the exploitation of different climatic sources to generate electricity. We analyze the electricity generation of a mixed wind-photovoltaic (PV) system, considering a multivariate model that involves the required climatic variables. We include the price of electricity in our model in order to evaluate the profitability of the system through its expected income. In addition, we investigate the optimal choice between these two production technologies via Markowitz's classic portfolio selection theory. To this end, we then consider a portfolio of the income deriving from both wind and PV production. We determine the most efficient components that maximize the overall income of our portfolio. This analysis is enriched by taking into consideration the loss of load hours of efficient

show abstract

Section: Climatic Variablesmentioning

confidence: 99%

A multivariate model for hybrid wind–photovoltaic power production with energy portfolio optimization

Casula¹,

D’Amico²,

Masala³

et al. 2022

JEM

View full text Add to dashboard Cite

show abstract

“…The developed approach is found to Corresponding author: Sahbi Boubaker require a large meteorological data set which is usually unavailable. A quaternion-valued neural network (QVNN) based method has been developed for forecasting GHI in [3]. Presenting the meteorological variables to the quaternion domain in a unique variable including the latitude, the longitude and the time indexes has resulted on three times reduction of the input-output layers which decreased computation time.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia

Boubaker

Kamel

Kchaou

2020

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

In this paper, several different Feed Forward Artificial Neural Networks (FFANNs) were used for forecasting the one-day-ahead Global Horizontal Irradiation (GHI) in Hail region, Saudi Arabia. The main motivation behind predicting GHI is that it is a critical parameter in sizing and planning photovoltaic water pumping systems. The novelty of the proposed approach is that it employs only the historical values of the GHI itself as explanatory variables and a fast training algorithm (resilient-propagation). In terms of performance metrics, the rp-trained FFANNs provided better results than Quasi-Newton (bfg) algorithm trained FFANNs for almost all the studied combinations of the FFANN structure. It has been also shown that increasing the number of neurons per layer didn’t improve the performance. Medium structures with fast training algorithms are recommended.

show abstract