Short-Term PV Power Forecasting Using a Hybrid TVF-EMD-ELM Strategy

Khelifi, Reski; Guermoui, Mawloud; Rabehi, Abdelaziz; Taallah, Ayoub; Zoukel, Abdelhalim; Ghoneim, Sherif S. M.; Bajaj, Mohit; AboRas, Kareem M.; Зайцев, Євген

doi:10.1155/2023/6413716

Cited by 18 publications

(7 citation statements)

References 36 publications

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“…In addition to technological factors, this transition is a worldwide necessity motivated by the pressing requirement for sustainability and the preservation of the environment 9 , 10 . The increasing worldwide energy demand, along with the recognition of the depletion of conventional energy sources, necessitates a crucial reassessment of our energy situation 11 . The need to reduce environmental deterioration is in line with the objectives of ecological stewardship, representing a crucial point where energy transitions connect with wider ecological resilience 12 .…”

Section: Introductionmentioning

confidence: 99%

A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights

Mfetoum,

Ngoh,

Molu

et al. 2024

Sci Rep

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Promoting renewable energy sources, particularly in the solar industry, has the potential to address the energy shortfall in Central Africa. Nevertheless, a difficulty occurs due to the erratic characteristics of solar irradiance data, which is influenced by climatic fluctuations and challenging to regulate. The current investigation focuses on predicting solar irradiance on an inclined surface, taking into consideration the impact of climatic variables such as temperature, wind speed, humidity, and air pressure. The used methodology for this objective is Artificial Neural Network (ANN), and the inquiry is carried out in the metropolitan region of Douala. The data collection device used in this research is the meteorological station located at the IUT of Douala. This station was built as a component of the Douala sustainable city effort, in partnership with the CUD and the IRD. Data was collected at 30-min intervals for a duration of around 2 years, namely from January 17, 2019, to October 30, 2020. The aforementioned data has been saved in a database that underwent pre-processing in Excel and later employed MATLAB for the creation of the artificial neural network model. 80% of the available data was utilized for training the network, 15% was allotted for validation, and the remaining 5% was used for testing. Different combinations of input data were evaluated to ascertain their individual degrees of accuracy. The logistic Sigmoid function, with 50 hidden layer neurons, yielded a correlation coefficient of 98.883% between the observed and estimated sun irradiation. This function is suggested for evaluating the intensities of solar radiation at the place being researched and at other sites that have similar climatic conditions.

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Section: Introductionmentioning

confidence: 99%

A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights

Mfetoum,

Ngoh,

Molu

et al. 2024

Sci Rep

Self Cite

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“…The study area covers the three selected sites. The first PV plant station is Lekhneg, in the Laghouat region (33° 43′ 26.74″ N, 2° 48′ 45.27″ E)with a capacity of 60 MWp located in the south of Algeria 55 . The second grid-connected PV power is Oud Nechou in the Ghardaia region 56 , 57 , (32° 36′ 1.46″ N, 3° 42′ 3.42″ E) located also in the south of Algeria with a capacity of 1.1MWp.…”

Section: Materials and Monitoringmentioning

confidence: 99%

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

Guermoui,

Fezzani,

Mohamed

et al. 2024

Sci Rep

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Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply–demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach's performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454–1.54] for the three studied PV stations.

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“…Its primary goal is to project datasets onto lower dimensional spaces that enhance class distinguishability, mitigating overfitting concerns and reducing computational complexity. Serving as a method of linear transformation for dimensionality reduction, LDA shares similarities with PCA [84,85].…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review

Rabehi,

Helal,

Zappa

et al. 2024

Applied Sciences

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An electronic nose, designed to replicate human olfaction, captures distinctive ‘fingerprint’ data from mixed gases or odors. Comprising a gas sensing system and an information processing unit, electronic noses have evolved significantly since their inception in the 1980s. They have transitioned from bulky, costly, and energy-intensive devices to today’s streamlined, economical models with minimal power requirements. This paper presents a comprehensive and systematic review of the electronic nose technology domain, with a special focus on advancements over the last five years. It highlights emerging applications, innovative methodologies, and potential future directions that have not been extensively covered in previous reviews. The review explores the application of electronic noses across diverse fields such as food analysis, environmental monitoring, and medical diagnostics, including new domains like veterinary pathology and pest detection. This work aims to underline the adaptability of electronic noses and contribute to their continued development and application in various industries, thereby addressing gaps in current literature and suggesting avenues for future research.

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Short-Term PV Power Forecasting Using a Hybrid TVF-EMD-ELM Strategy

Cited by 18 publications

References 36 publications

A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights

A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review

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