Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm DOI
Oğuz Taşdemır

Gazi University Journal of Science Part A Engineering and Innovation, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

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

Language: Английский

Single and Multi-Objective Optimal Power Flow Based on JAYA Algorithm with Teaching-Learning Based Optimization DOI Creative Commons
Oğuz Taşdemır, Salih Ermis

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract This paper deals with the Optimal Power Flow (OPF) in an IEEE standard bus (30-bus) power system and presents a multi-objective optimization approach to minimize generation costs, active losses voltage deviations. The OPF problem is of critical importance for reliable, efficient economical operation systems. However, solution this complex due its nonlinear nature large number constraints. Conventional methods are often insufficient overcome challenges inherent OPF. In addressing these challenges, study employs metaheuristic algorithms, namely Teaching-Learning Based Optimisation (TLBO), JAYA hybrid TLBO-JAYA, enhance efficiency convergence speed process. To manage problem, Pareto optimisation utilised identify set that balances conflicting objectives. outcomes demonstrate TLBO-JAYA algorithm offers balanced enhancement terms cost, loss stability, thereby providing versatile framework contemporary These findings underscore potential algorithms problems

Language: Английский

Citations

0
KIRŞEHİR’İN RÜZGAR ENERJİSİ POTANSİYELİ VE İÇ ANADOLU BÖLGESİ KURULU RÜZGAR ENERJİSİ SANTRALLERİNİN GÜÇ ANALİZİ DOI Open Access
Müjdat Öztürk, Ramazan Kayabaşı, Oğuz Taşdemır

et al.

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, Journal Year: 2025, Volume and Issue: 28(1), P. 189 - 201

Published: March 3, 2025

Türkiye’nin zengin ve çeşitlilik içeren yenilenebilir enerji potansiyeli, son yıllarda hızla değerlendirilmeye başlanmıştır. Özellikle rüzgar enerjisi, elektrik üretiminde önemli bir rol oynamakta kurulu güç içerisindeki payını sürekli artırmaktadır. Çevre dostu kaynağı olan kırsal bölgelerde de yüksek üretim kapasitesine sahiptir. Bu çalışmada, İç Anadolu Bölgesi illerinin potansiyeli santral kapasiteleri; nüfus gelişmişlik düzeyleriyle ilişkilendirilerek incelenmiştir. Özel olarak Kırşehir bölgesi ele alınmış 2024-2028 yılları arasında bölgedeki enerjisi kapasitesi Yapay Sinir Ağları (YSA) tabanlı model ile tahmin edilmiştir. Analiz sonuçlarına göre, 2024 yılında potansiyelinde yaklaşık %1’lik düşüş yaşanması öngörülmüş, ancak 2025-2028 her yıl artış kaydedilmiştir. 2023 yılındaki üretime kıyasla, 2026 tahmini üretimi %3,5 oranında göstermiştir. Aynı şekilde, 2027 2028 yıllarında da yükseliş devam etmiştir. Çalışma, Bölgesi’nin potansiyelini detaylı şekilde değerlendirirken, ilinde yer alan santrali özelinde arasındaki tahminini ortaya koymuştur. Sonuç olarak, bölgenin mevcut potansiyel yıllara göre değişimi kapsamlı analiz

Citations

0
Research on Photovoltaic Long-Term Power Prediction Model Based on Superposition Generalization Method DOI Open Access
Yun Chen, Jilei Liu, Bei Liu

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(5), P. 1263 - 1263

Published: April 22, 2025

The integration of renewable energy sources, specifically photovoltaic generation, into the grid at a large scale has significantly heightened volatility and unpredictability power system. Consequently, this presents formidable challenges to ensuring reliable operation grid. This study introduces novel stacked model for prediction, integrating multiple conventional data processing methods as base learners, including Group Method Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), Emotional (ENN). A Backpropagation (BPNN) serves meta-learner, utilizing outputs learners input features enhance overall prediction accuracy by mitigating individual errors. To assess model’s effectiveness, five evaluation metrics are employed: Bayesian Information Criterion (BIC), Percent Mean Average Relative Error (PMARE), Legates McCabe Index (LM), Absolute Deviation (MAD), Root Square (RMSE), long-term stability in output forecasting. Additionally, effectiveness validated using operational from plants particular province China. results indicate that model, after training, testing, validation on performance metrics, surpasses baseline single models performance.

Language: Английский

Citations

0
RR intervals prediction method for cardiovascular patients optimized LSTM based on ISSA DOI

Wenjie Yu,

Zhilin Pan,

Dayang Tang

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 106904 - 106904

Published: Sept. 23, 2024

Language: Английский

Citations

1
Wind Turbine Power Curve Fitting Using Mountain Gazelle Optimizer and Parametric Functions DOI

Mehmet Yeşilbudak,

Ahmet Özcan

Published: May 27, 2024

Language: Английский

Citations

0
Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm DOI
Oğuz Taşdemır

Gazi University Journal of Science Part A Engineering and Innovation, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

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

Language: Английский

Citations

0