Power forecasting of three silicon-based PV technologies using actual field measurements

Nour-eddine, Id omar; Boukhattem, Lahcen; Hassani, Fahd Oudrhiri; Bennouna, A.; Oukennou, Aziz

doi:10.1016/j.seta.2020.100915

“…The results were compared in terms of mean bias error (MBE), mean absolute error (MAE), MAPE, RMSE, and nRMSE. The ANN-based model demonstrated a 2.107% MAE and 2.645% RMSE against 2.406% and 5.185%, respectively, for the persistence model [16]. Akhter et al proposed a model for an hour-ahead prediction on a yearly basis of three different PV plants, based on available data for wind speed, module, ambient temperature, and solar irradiation employing a long short-term memory (LSTM) recurrent neural network (RNN) with a deep learning method, with the results compared with regression, hybrid Adaptive neuro-fuzzy inference system (ANFIS), and machine learning methods [17].…”

Section: Introductionmentioning

confidence: 95%

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Ρουμπακιάς

¹

,

Stamatelos

²

2022

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Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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“…The root mean square error (RMSE) and mean absolute error (MAE) were used to analyze the training and test errors of different ANNs [28,45]. The other ANN parameter settings are listed in Table 4.…”

Section: First Step: Building the Ann Ann Trainingmentioning

confidence: 99%

Efficiency Analysis of the Photovoltaic Shading and Vertical Farming System by Employing the Artificial Neural Network (ANN) Method

Hao

¹

,

Tablada

²

,

Shi

³

et al. 2023

Buildings

2

0

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Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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“…proposed a hybrid deep learning model combining wavelet packet decomposition (WPD) and long short‐term memory (LSTM) networks to predict PV power [11]. Id Omar Nour‐eddine did not take any deep learning method but proposed both linear and non‐linear models compared with the ones presented in the literature including the persistence and an Artificial Neural Network (ANN) model widely used for short‐term PV output predicting [12]. Ying et al.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al proposed a hybrid deep learning model combining wavelet packet decomposition (WPD) and long short-term memory (LSTM) networks to predict PV power [11]. Id Omar Nour-eddine did not take any deep learning method but proposed both linear and non-linear models compared with the ones presented in the literature including the persistence and an Artificial Neural Network (ANN) model widely used for short-term PV output predicting [12]. Ying et al used a clustering algorithm and neural network to build a power predicting model (PFM) based on real data which can effectively characterize the uncertainty of PV power generation and EV charging load [13,14].…”

Section: Introductionmentioning

confidence: 99%

A rapid prediction model of photovoltaic power generation for autonomous long‐duration aerostat

Liu

¹

,

Xu

²

,

Li

³

et al. 2023

IET Renewable Power Gen

Get access via publisher Add to dashboard Cite

Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

show abstract

Power forecasting of three silicon-based PV technologies using actual field measurements

Cited by 23 publications

References 57 publications

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Efficiency Analysis of the Photovoltaic Shading and Vertical Farming System by Employing the Artificial Neural Network (ANN) Method

A rapid prediction model of photovoltaic power generation for autonomous long‐duration aerostat

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