Ultra-Short Term Load Forecasting Based on Elman Neural Network

Di, Pengyu; Dong, Kai; Du, Jiang; Dong, Chao; He, Xiangzhen; Guan, Yi; Gao, Haixiang; Li, Jiaming; Liang, Yanjie

doi:10.1109/isgt-asia.2019.8881187

Cited by 6 publications

(2 citation statements)

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“…This allows the network to capture sequential information and make predictions based on past inputs. The network is trained using backpropagation through time, where the error is propagated through the network and the weights are updated accordingly [30]. The ENN has been used for numerous purposes, both in science and engineering problems [31,32].…”

Section: Elman Neural Network (Enn)mentioning

confidence: 99%

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

Abubakar,

Jibril,

Almeida

et al. 2023

Processes

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Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out. The first scenario is conducted for PV array fault detection with DC power (DCP) as output. The second scenario is conducted for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. A real-world solar energy dataset is used to evaluate the proposed technique with results compared to existing detection techniques and obtained results showing that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified its reliably among all the models with MAPE = 0.0393 and MAE = 0.002 for inverter fault detection, and MAPE = 0.091 and MAE = 0.000 for PV array fault detection.

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Section: Elman Neural Network (Enn)mentioning

confidence: 99%

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

Abubakar,

Jibril,

Almeida

et al. 2023

Processes

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“…Another application of Elman recurrent neural network contributing to the literature of forecasting was applied by [74]. The proposed technique aims to forecast load.…”

Section: Elman Recurrent Neural Networkmentioning

confidence: 99%

Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method

Krichene¹,

Ouarda²,

Chabchoub³

et al. 2021

Preprint

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A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets.

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Taylor-based optimized recursive extended exponential smoothed neural networks forecasting method

et al. 2022

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Ultra-Short Term Load Forecasting Based on Elman Neural Network

Cited by 6 publications

References 1 publication

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method

Taylor-based optimized recursive extended exponential smoothed neural networks forecasting method

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