A novel recurrent neural network approach in forecasting short term solar irradiance

Jaihuni, Mustafa; Basak, Jayanta Kumar; Khan, Fawad; Okyere, Frank Gyan; Sihalath, Thavisak; Bhujel, Anil; Park, Jihoon; Lee, Deog Hyun; Kim, Hyeon Tae

doi:10.1016/j.isatra.2021.03.043

Cited by 54 publications

(21 citation statements)

References 17 publications

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“…The Bi-GRU model specifications given in Table 1, were amended to fit the new 32 months 5-min dataset; the number of hidden layers and total neurons were increased, as it was necessary for the deep RNN model to learn proportionally new abstract features of the bigger datasets [14]. As for the 60 min solar irradiance predictions, the corresponding dataset was smaller; hence, a shallower RNN architecture was selected, i.e., fewer neurons and hidden layers.…”

Section: Single Model Resultsmentioning

confidence: 99%

“…As explained thoroughly in the previous work of this paper's author [14], bidirectional RNN (Bi-RNN) models are very effective in predicting short term solar irradiance. These models can carry knowledge from the past sequences as well as future sequences of a time series, hence they are called bidirectional.…”

Section: Deep Rnn Architecture Developmentmentioning

confidence: 90%

“…Hence, the PAHM would be fundamentally different from the classical hybrid models. The deep RNN models which were developed in the previous work of this paper's author [14] would be utilized. In the mentioned work, a bi-directional gated recurrent unit (Bi-GRU) gave the best solar irradiance predictions; hence, for the first time the Bi-GRU model is proposed along with the ARIMA model, here.…”

Section: Objectivesmentioning

confidence: 99%

“…There are two classes of Bi-RNN models which are bidirectional gated recurrent networks (Bi-GRU) and bidirectional long-short term memory (Bi-LSTM). In this study, the former type is selected, as its superior performance, in predicting solar irradiance, over the latter type has been shown in [14]. Table 1 shows the respective hyper-parameters like transfer or activation function, epoch, optimizer, number of neurons, and hidden layers were selected after trial and error processes in [14].…”

Section: Deep Rnn Architecture Developmentmentioning

confidence: 99%

“…In this study, the former type is selected, as its superior performance, in predicting solar irradiance, over the latter type has been shown in [14]. Table 1 shows the respective hyper-parameters like transfer or activation function, epoch, optimizer, number of neurons, and hidden layers were selected after trial and error processes in [14]. However, in the current work, the architectural parameters of the Bi-GRU model would be changed as per the new datasets in order to provide reasonable accuracy levels.…”

Section: Deep Rnn Architecture Developmentmentioning

confidence: 99%

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A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms

et al. 2020

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Solar renewable energy (SRE) applications are substantial in eradicating the rising global energy shortages and reversing the approaching environmental apocalypse. Hence, effective solar irradiance forecasting models are crucial in utilizing SRE efficiently. This paper introduces a partially amended hybrid model (PAHM) by the implementation of a new algorithm. The algorithm innovatively utilizes bi-directional gated unit (Bi-GRU), autoregressive integrated moving average (ARIMA) and naive decomposition models to predict solar irradiance in 5-min and 60-min intervals. Meanwhile, the models’ generalizability strengths would be tested under an 11-fold cross-validation and are further classified according to their computational costs. The dataset consists of 32 months’ solar irradiance and weather conditions records. A fundamental result of this study was that the single models (Bi-GRU and ARIMA) outperformed the hybrid models (PAHM, classical hybrid model) in the 5-min predictions, negating the assumptions that hybrid models oust single models in every time interval. PAHM provided the highest accuracy level in the 60-min predictions and improved the accuracy levels of the classical hybrid model by 5%, on average. The single models were rigorous under the 11-fold cross-validation, performing well with different datasets; although the computational efficiency of the Bi-GRU model was, by far, the best among the models.

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Section: Single Model Resultsmentioning

confidence: 99%

Section: Deep Rnn Architecture Developmentmentioning

confidence: 90%

Section: Objectivesmentioning

confidence: 99%

Section: Deep Rnn Architecture Developmentmentioning

confidence: 99%

Section: Deep Rnn Architecture Developmentmentioning

confidence: 99%

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A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms

et al. 2020

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Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

Hanif,

Siddique,

et al. 2024

Advcd Theory and Sims

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Effective solar energy utilization demands improvements in forecasting due to the unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two innovative forecasting models across different locations: the Sequential Deep Artificial Neural Network (SDANN) and the Deep Hybrid Random Forest Gradient Boosting (RFGB). SDANN, leveraging deep learning, aims to identify complex patterns in weather data, while RFGB, combining Random Forest and Gradient Boosting, proves more effective by offering a superior balance of efficiency and accuracy. The research highlights the SDANN model's deep learning capabilities along with the RFGB model's unique blend and their comparative success over existing models such as eXtreme Gradient Boosting (XGBOOST), Categorical Boosting (CatBOOST), Gated Recurrent Unit (GRU), and a K‐Nearest Neighbors (KNN) and XGBOOST hybrid. With the lowest Mean Squared Error (147.22), Mean Absolute Error (8.77), and a high R2 value (0.80) in a studied region, RFGB stands out. Additionally, detailed ablation studies on meteorological feature impacts on model performance further enhance accuracy and adaptability. By integrating cutting‐edge AI in SI forecasting, this research not only advances the field but also sets the stage for future renewable energy strategies and global policy‐making.

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Analysis of a Solar Photovoltaic System Using Fractional-Order Neural Network

Joshi,

Bhosale,

Vyawahare

2024

Communications in Computer and Information Science

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A novel recurrent neural network approach in forecasting short term solar irradiance

Cited by 54 publications

References 17 publications

A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms

A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

Analysis of a Solar Photovoltaic System Using Fractional-Order Neural Network

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