A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends

El-Amarty, Naima; Marzouq, Manal; Fadili, Hakim El; Bennani, Saad Dosse; Ruano, A. E.

doi:10.1007/s11356-022-24240-w

Cited by 20 publications

(8 citation statements)

References 136 publications

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“…The role of pre-processing or data feature selection has already been emphasised as a stage that improves the quality of data and thus increases the accuracy of the forecast [4,5,55,59,61,66] even in the first review works [28]. Attention is paid to the post-processing phase to model local effects [28,46,61] as a practice to improve the initial forecasts.…”

Section: Solar Forecasting Process and Datamentioning

confidence: 99%

“…Strong volatility and intermittency of solar energy generation require the leveraging advance of adequate forecasting methods concerning meteorological and geographical characteristics of plant location [1,2]. Forecasting solar irradiance is essential in planning and operations to deal with energy supply and demand uncertainty, balance and optimise the system, and ensure power continuity [3][4][5][6][7][8]. Accurate forecasting is crucial at all levels of an energy system, including control, operation, management, financial viability of energy companies, and the trajectories of sustainable and responsible innovation.…”

Section: Introductionmentioning

confidence: 99%

“…Spatial resolution and time horizon determine the application of forecasts. Controlling power distribution, ensuring network stability and voltage regulation requires a time 2 horizon in seconds [4,9], forecasts from minutes to hours support power reserve management and load optimisation [10], day-ahead forecasts are used for transmission planning and unit commitment [5,7], and a year scale for capacity/network global management [6,11].…”

Section: Introductionmentioning

confidence: 99%

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Solar Radiation Forecasting — State of Art and Methodological Discussion

Chodakowska,

Nazarko,

Nazarko

et al. 2024

Preprint

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Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. Due to the technological and econonomic limitations of energy storage solutions, using other, mosty conventional, sources to cover energy shortfalls and at the same time utilising solar surpluses production becomes necessary. The need to optimize energy systems, ensuring power continuity, and balancing energy supply and demand has led to the development of various forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasizes the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses the emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. This includes defining research questions, selecting eligibility criteria, literature search, data extraction, synthesis, and assessing bias and quality. The article contributes to the solar forecasting field by providing up-to-date knowledge, along with insights on the emerging trends, future research directions, and anticipating implications for the theory and practicce.

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Section: Solar Forecasting Process and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solar Radiation Forecasting — State of Art and Methodological Discussion

Chodakowska,

Nazarko,

Nazarko

et al. 2024

Preprint

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“…To increase the prediction's precision, Wu et al proposed an information model similar to a daily clustered convolutional neural network (CNN) to predict PV power generation [6]. El-Amarty et al in order to create an ANN model for predicting solar irradiation, a number of factors including data kinds, data horizons, data preprocessing, forecasting horizons, feature selection, and model type must first be reviewed, analyzed, and provided with an overview [7]. Guo et al to estimate short-term PV power, offer a deep learning architecture based on 7.5-min-ahead and 15-min-ahead techniques [8].…”

Section: Introductionmentioning

confidence: 99%

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

mao,

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Accurate prediction of PV power is essential to maximize solar energy utilization, enhance the performance of PV power generation systems, and ensure the dependability of power infrastructures. In this paper, an approach based on CS-LSTM neural network is proposed for short term power forecasting. The proposed approach involves processing the data using CEEMDAN, followed by the application of the CS algorithm to determine the optimal LSTM hyperparameters. To evaluate the accuracy and dependability of the model, CEEMDAN-CS-LSTM is compared to other models. Experiments show that CEEMDAN-CS-LSTM outperforms the other models in terms of prediction accuracy and stability.

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“…Compared with non-renewable fossil energy, clean energy is renewable and hardly produces harmful substances (Belmahdi et al 2022a). With the advantages of wide distribution, abundant energy storage, and low operating cost, solar energy has been utilized in multiple fields through photovoltaic (PV) power generation (Korkmaz 2021;El-Amarty et al 2023). With respect to PV power generation, the off-grid mode is gradually replaced by the grid-connected mode, which means that PV power generation is incorporated into the grid as a microgrid.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid model for photovoltaic output power prediction

Zou,

Wei,

Song

et al. 2023

Environ Sci Pollut Res

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Recently, with the development of renewable energy technologies, photovoltaic (PV) power generation is widely used in the grid. However, as PV power generation is influenced by external factors, such as solar radiation fluctuation, PV output power is intermittent and volatile, and thus the accurate PV output power prediction is imperative for the grid stability. To address this issue, the artificial rabbits optimization is firstly improved by various strategies, then based on convolutional neural network and bidirectional long short-term memory (CBiLSTM) with improved artificial rabbits optimization (IARO), a new hybrid model denoted by IARO-CBiLSTM is proposed to predict PV output power. Moreover, inputs of IARO-CBiLSTM are optimized by analyzing influential factors of PV output power with Pearson correlation coefficient method. Finally, in order to verify the prediction accuracy, IARO-CBiLSTM is compared with other well-known methods under different weather conditions and different seasons, and the compared results show that IARO-CBiLSTM performs better in terms of various evaluation metrics.

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A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends

Cited by 20 publications

References 136 publications

Solar Radiation Forecasting — State of Art and Methodological Discussion

Solar Radiation Forecasting — State of Art and Methodological Discussion

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

A new hybrid model for photovoltaic output power prediction

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