Effect of Feature Selection on the Prediction of Direct Normal Irradiance

Boutahir, Mohamed Khalifa; Farhaoui, Yousef; Azrour, Mourade; Zeroual, Imad; Allaoui, Ahmad El

doi:10.26599/bdma.2022.9020003

Cited by 34 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quantity of solar radiation that is received by a surface that is always perpendicular (or normal) to the incoming rays from the sun is known as direct normal irradiance (DNI). The equation obtained is GHI = DNI x cos(θ) +DHI [12].…”

Section: Discussionmentioning

confidence: 99%

Solar energy utilization in desalination power plan

Erdiansyah

Kristyadi

Permana

2023

jttm

View full text Add to dashboard Cite

A steam power plant (PLTU) uses demin water to generate 3x350 MW of electrical energy. Reverse Osmosis Desalination system is used to treat the 190 m3/hour of desalinated water that is produced from seawater. Solar energy will replace the 225.41 kW of electrical energy required for the RO desalination pump in order to lessen pollution and the greenhouse effect caused by PLTU. The PLTU is situated in Pelabuhan Ratu Sukabumi, West Java. This region has a solar radiation potential of 3.189 kWp/day/m2 and a deployment space for PV modules of 6,167 m2, divided into two zones: the ground and the roof. The needs analysis is carried out by utilizing software simulations that estimate the power requirements of desalination pumps and the potential of electrical energy from solar radiation based on land availability. According to the modeling findings, PLTS can produce 1024 MWh/year of electrical energy with 1,458 pieces of 450 Wp PV modules and 5 units of 120 kW inverters.

show abstract

Section: Discussionmentioning

confidence: 99%

Solar energy utilization in desalination power plan

Erdiansyah

Kristyadi

Permana

2023

jttm

View full text Add to dashboard Cite

show abstract

“…Several investigations are concerned with the development of different methods, like artificial neural networks, for estimating solar radiation levels (Berrizbeitia et al, 2020). The performance of the artificial neural networks model is significant in solar-level detection (Boutahir et al, 2022). These models are required to display the hard correlation between weather conditions and solar radiation, which results in exact prediction compared to the conventional models, similar linear regression or fuzzy logic models (Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Solar Radiation Prediction: Case Study, Al-Qadisiyah, Iraq

Al-rubaye,

M. Al-Khuzaie

2024

Jou. Eng. Res.

View full text Add to dashboard Cite

For solar energy to develop a clean, renewable alternative to fossil fuels, it is important to be able to correctly predict surface longwave radiation. To improve cost-efficiency and accuracy in surface longwave radiation (SLR) predictions, forecast systems are increasingly utilizing artificial neural networks (ANNs). This study uses two different procedures for predicting solar radiation in great detail. The first model uses weather statistics from a station in Al-Qadisiyah, Iraq. The second model, on the other hand, uses daily statistics from 2022 from NASA's Prediction of Worldwide Energy dataset for the same site. The scaled conjugate gradient technique was used in both models. The goal is to find the best mix of meteorological factors that can be used with an ANN model to achieve accurate predictions. Based on the findings of this study, temperature, relative humidity, and Rainfall all seem to have a big effect on SLR. On the other hand, windy weather doesn't have much of an effect on SLR. ANN models also did very well when trained with data from NASA's Prediction of Worldwide Energy, with an R2-value of 0.823 and an RMSE value of 0.0106. The results show that this mix does better than other models in terms of performance score.

show abstract

“…For solar energy systems to be designed, operated, and controlled in the most efficient manner possible, (1,2,3) a precise forecasting of solar irradiance is necessary. One of the most crucial variables in solar irradiance prediction models, global horizontal irradiance (GHI), describes the total quantity of solar radiation that impacts a horizontal surface at a specific point.…”

Section: Introductionmentioning

confidence: 99%

Impact of feature selection on the prediction of global horizontal irradiation under ouarzazate city climate

Benchikh,

Jarou,

Roa

et al. 2024

Data and Metadata

View full text Add to dashboard Cite

Ensuring accurate forecasts of Global Horizontal Irradiance (GHI) stands as a pivotal aspect in optimizing the efficient utilization of solar energy resources. Machine learning techniques offer promising prospects for predicting global horizontal irradiance. However, within the realm of machine learning,the importance of feature selection cannot be overestimated, as it is crucial in determining performance and reliability of predictive models. To address this, a comprehensive machine learning algorithm has been developed, leveraging advanced feature importance techniques to forecast GHI data with precision. The proposed models draw upon historical data encompassing solar irradiance characteristics and environmental variables within the Ouarzazate region, Morocco, spanning from 1st January 2018, to 31 December 2018, with readings taken at 60-minute intervals. The findings underscore the profound impact of feature selection on enhancing the predictive capabilities of machine learning models for GHI forecasting. By identifying and prioritizing the most informative features, the models exhibit significantly enhanced accuracy metrics, thereby bolstering the reliability, efficiency, and practical applicability of GHI forecasts. This advancement not only holds promise for optimizing solar energy utilization but also contributes to the broader discourse on leveraging machine learning for renewable energy forecasting and sustainability initiatives.

show abstract

Effect of Feature Selection on the Prediction of Direct Normal Irradiance

Cited by 34 publications

References 24 publications

Solar energy utilization in desalination power plan

Solar energy utilization in desalination power plan

Artificial Neural Networks for Solar Radiation Prediction: Case Study, Al-Qadisiyah, Iraq

Impact of feature selection on the prediction of global horizontal irradiation under ouarzazate city climate

Contact Info

Product

Resources

About