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Journals / Gazi University Journal of Science Part A: Engineering and Innovation / 2024 / Volume: 11 Issue: 4

Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm

Oğuz Taşdemir(KIRŞEHİR AHİ EVRAN ÜNİVERSİTESİ)0000-0003-1782-0024
Pages
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Publication Date
2024-12-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results indicate that the TLBO algorithm demonstrates high accuracy for photovoltaic power forecasting and provides an effective alternative model in this field.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The need for electrical energy has increased considerably due to technological developments.
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Key Findings & Data:However, there are significant power quality problems in transferring the generated photovoltaic energy to the grid.
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Conclusion & Impact:These results indicate that the TLBO algorithm demonstrates high accuracy for photovoltaic power forecasting and provides an effective alternative model in this field.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Photovoltaic Power · Current Developments · Photovoltaic Power Estimation

Abstract

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.

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Cite This Article

Taşdemir, O. (2024). Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm. Gazi University Journal of Science Part A: Engineering and Innovation, Volume: 11 Issue: 4. https://doi.org/10.54287/gujsa.1581828

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