Compatibility of Hybrid Neuro-Fuzzy Model to Predict Reference Evapotranspiration in Distinct Climate Stations

Abdullahi, Jazuli; Elkiran, Gözen; Malami, Salim Idris; Rotimi, Abdulazeez; Haruna, Sadi İbrahim; Abba, S.I.

doi:10.1109/icmeas52683.2021.9692423

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BTA-M1, on the other hand, has relatively high scores in RMSE, MAPE, MAE, and PBIAS, indicating lower performance than other models. It is important to note that these metrics alone may not be sufficient to determine the best model for a specific task and other factors, such as computational efficiency and interpretability, should also be considered [57][58][59]. Similarly, Ref.…”

Section: Gpr-m1mentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Gpr-m1mentioning

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

View full text Add to dashboard Cite

show abstract

Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete

Jibril¹,

Zayyan²,

Malami³

et al. 2023

Applications in Engineering Science

View full text Add to dashboard Cite

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

Abubakar¹,

Jibril²,

Almeida³

et al. 2023

Preprint

View full text Add to dashboard Cite

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 their strengths for 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 done for PV array fault detection with DC power (DCP) as output. The second scenario is done 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. Real-world solar energy dataset is used to evaluate the proposed technique, with results compared to existing detection techniques and obtained results show that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified 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.

show abstract

Compatibility of Hybrid Neuro-Fuzzy Model to Predict Reference Evapotranspiration in Distinct Climate Stations

Cited by 4 publications

References 20 publications

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

Implementation of nonlinear computing models and classical regression for predicting compressive strength of high-performance concrete

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

Contact Info

Product

Resources

About