Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm

Oğuz Taşdemir

Makale detayları

Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm

Yazarlar:

Oğuz Taşdemir

Yayıncı:

Gazi University

Ana Başlıklar:

Photovoltaic Power Systems | Electrical Engineering (Other)

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.11 Issue 4, pp.780-791

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336836

pISSN:

2147-9542

Anahtar kelimeler:

Özet:

 The need for electrical energy has increased considerably due to technological developments. Reducing costs and losses, especially in the supply of electrical energy, is among the goals of energy companies. Photovoltaic energy has been an important alternative in reducing energy costs. However, there are significant power quality problems in transferring the generated photovoltaic energy to the grid. Therefore, the generated photovoltaic energy needs to be accurately estimated to be transferred to the grid smoothly. In the literature, many forecasting models have been used for photovoltaic power forecasting. Each of these forecasting models has estimated photovoltaic power using different input parameters, different estimation intervals, and different estimation algorithms. This paper was conducted using the Teaching-Learning Based Optimization TLBO algorithm as an alternative approach to photovoltaic power forecasting models. According to the forecasting results, the root mean square error RMSE for the test subset was obtained as 270.32 kW, and the mean absolute percentage error MAPE was found to be 3.87. These results indicate that the TLBO algorithm demonstrates high accuracy for photovoltaic power forecasting and provides an effective alternative model in this field.

 The need for electrical energy has increased considerably due to technological developments. Reducing costs and losses, especially in the supply of electrical energy, is among the goals of energy companies. Photovoltaic energy has been an important alternative in reducing energy costs. However, there are significant power quality problems in transferring the generated photovoltaic energy to the grid. Therefore, the generated photovoltaic energy needs to be accurately estimated to be transferred to the grid smoothly. In the literature, many forecasting models have been used for photovoltaic power forecasting. Each of these forecasting models has estimated photovoltaic power using different input parameters, different estimation intervals, and different estimation algorithms. This paper was conducted using the Teaching-Learning Based Optimization TLBO algorithm as an alternative approach to photovoltaic power forecasting models. According to the forecasting results, the root mean square error RMSE for the test subset was obtained as 270.32 kW, and the mean absolute percentage error MAPE was found to be 3.87. These results indicate that the TLBO algorithm demonstrates high accuracy for photovoltaic power forecasting and provides an effective alternative model in this field.

 The need for electrical energy has increased considerably due to technological developments. Reducing costs and losses, especially in the supply of electrical energy, is among the goals of energy companies. Photovoltaic energy has been an important alternative in reducing energy costs. However, there are significant power quality problems in transferring the generated photovoltaic energy to the grid. Therefore, the generated photovoltaic energy needs to be accurately estimated to be transferred to the grid smoothly. In the literature, many forecasting models have been used for photovoltaic power forecasting. Each of these forecasting models has estimated photovoltaic power using different input parameters, different estimation intervals, and different estimation algorithms. This paper was conducted using the Teaching-Learning Based Optimization TLBO algorithm as an alternative approach to photovoltaic power forecasting models. According to the forecasting results, the root mean square error RMSE for the test subset was obtained as 270.32 kW, and the mean absolute percentage error MAPE was found to be 3.87. These results indicate that the TLBO algorithm demonstrates high accuracy for photovoltaic power forecasting and provides an effective alternative model in this field.

 The need for electrical energy has increased considerably due to technological developments. Reducing costs and losses, especially in the supply of electrical energy, is among the goals of energy companies. Photovoltaic energy has been an important alternative in reducing energy costs. However, there are significant power quality problems in transferring the generated photovoltaic energy to the grid. Therefore, the generated photovoltaic energy needs to be accurately estimated to be transferred to the grid smoothly. In the literature, many forecasting models have been used for photovoltaic power forecasting. Each of these forecasting models has estimated photovoltaic power using different input parameters, different estimation intervals, and different estimation algorithms. This paper was conducted using the Teaching-Learning Based Optimization TLBO algorithm as an alternative approach to photovoltaic power forecasting models. According to the forecasting results, the root mean square error RMSE for the test subset was obtained as 270.32 kW, and the mean absolute percentage error MAPE was found to be 3.87. These results indicate that the TLBO algorithm demonstrates high accuracy for photovoltaic power forecasting and provides an effective alternative model in this field.

Makale detayları

Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm

Yazarlar:

Oğuz Taşdemir

Yayıncı:

Gazi University

Ana Başlıklar:

Photovoltaic Power Systems | Electrical Engineering (Other)

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.11 Issue 4, pp.780-791

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336836

pISSN:

2147-9542

Photovoltaic Power

Current Developments

Photovoltaic Power Estimation

TLBO

Sayı

Makale detayları

Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm

Yazarlar: Oğuz Taşdemir

Yayıncı: Gazi University

Ana Başlıklar: Photovoltaic Power Systems | Electrical Engineering (Other)

Dergi: Gazi University Journal of Science Part A: Engineering and Innovation Vol.11 Issue 4, pp.780-791

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001336836

pISSN: 2147-9542

Photovoltaic Power

Current Developments

Photovoltaic Power Estimation

TLBO

Sayı

Yazarlar:

Oğuz Taşdemir

Yayıncı:

Gazi University

Ana Başlıklar:

Photovoltaic Power Systems | Electrical Engineering (Other)

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.11 Issue 4, pp.780-791

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001336836

pISSN:

2147-9542