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Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2019 / Cilt: 19 - Sayı: 1

Comparison of Performance of Two and Multi-Dimensional Linear Prediction Filters for Modeling Wind Speed Data Keywords Linear prediction

Rüzgar Hızı Verilerinin Modellenmesinde İki ve Çok Boyutlu Filtrelerin Performaslarının Karşılaştırılması

Fatih Onur HOCAOĞLU
Emre AKARSLAN
Pages
121–128
Publication Date
2022-10-12
DOI
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AI Summary & Key Takeaways

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

The results indicated that both the two-dimensional and the multi-dimensional linear prediction filters have been successful in modeling short-term wind speeds.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The reasons such as; development on the technology, continuing increase in population etc. has caused continues increase on the demand of energy.
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Key Findings & Data:The energy to be generated from a wind turbine is directly related to the wind speed in that region.
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Conclusion & Impact:The results indicated that both the two-dimensional and the multi-dimensional linear prediction filters have been successful in modeling short-term wind speeds.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The reasons such as; development on the technology, continuing increase in population etc. has caused continues increase on the demand of energy. Nevertheless, depleted fossil fuels have forced the production of energy from different sources as well as the generation of energy from sustainable sources. Wind energy generation systems are both sustainable and environmentally friendly and are an important source of production. The energy to be generated from a wind turbine is directly related to the wind speed in that region. In this study, it is aimed to model the average wind speed data collected in ten minutes period from a meteorological station at the campus area of Afyon Kocatepe University of Turkey, successfully by designing linear prediction filters. Both two-dimensional and multidimensional linear prediction filters are used in the study. In this study, for the aim of prediction of short term wind speed data measured; wind speed, wind speed-temperature and wind speedtemperature-pressure data are employed and different filter templates have been built. Considering the results obtained from the filters, both the success of linear prediction filters on short-term wind speed modeling has been investigated and the successes of two-dimensional and multidimensional linear prediction filters have been compared. The results indicated that both the two-dimensional and the multi-dimensional linear prediction filters have been successful in modeling short-term wind speeds.

Özet

Teknolojinin gelişimi, devam eden nüfus artışı gibi nedenler enerji ihtiyacının sürekli artmasına nedenolmaktadır. Bununla beraber tükenen fosil yakıtlar, hem farklı kaynaklardan enerji üretimini zorunlukılmış, hem de sürdürülebilir kaynaklardan enerji üretimini zorunlu hale getirilmiştir. Rüzgardan enerjiüreten sistemler hem sürdürülebilir hem de çevre dostu olmaları sebebi ile yaygın kullanım alanınasahiptir. Bir rüzgar türbiniden üretilecek enerji o bölgedeki rüzgar hızı ile doğrudan bağlantılıdır. Buçalışmada, Doğrusal Tahmin Filtreleri tasarlanarak, Afyon Kocatepe Üniversitesi kampüs alanına tesisedilmiş bir meteoroloji istasyonundan 10 dakikalık periyotlarda toplanan ortalama rüzgar hızı verilerininbaşarılı bir şekilde modellenmesi hedeflenmiştir. Çalışmada hem iki boyutlu hem de çok boyutludoğrusal tahmin filtreleri tasarlanmıştır. Gerçekleştirilen çalışmada kısa dönem rüzgar hızı verilerinintahmini amacıyla geçmişte ölçülmüş; rüzgar hızı, rüzgar hızı-sıcaklık ve rüzgar hızı-sıcaklık-basınçverilerini kullanan farklı doğrusal tahmin filtreleri tasarlarnmıştır. Filtrelerden elde edilen sonuçlargözönüne alınarak, hem doğrusal tahmin filtrelerinin kısa dönem rüzgar hızı modellemesindeki başarısıincelenmiş, hem de iki boyut ve çok boyutlu doğrusal tahmin filtrelerinin başarısı birbirleri ilekarşılaştırılmıştır. Elde edilen sonuçlar hem iki boyutlu hem de çok boyutlu doğrusal tahmin filtrelerinkısa dönem rüzgar hızı modellemedeki başarısını ortaya koymuştur.

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Cite This Article

HOCAOĞLU, F. O., AKARSLAN, E. (2022). Comparison of Performance of Two and Multi-Dimensional Linear Prediction Filters for Modeling Wind Speed Data Keywords Linear prediction. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 19 - Sayı: 1, 121–128. https://ajindex.com/articles/429899

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References (13)

  1. Akarslan, E., Hocaoglu, F.O. and Edizkan R., 2014. A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting. Energy, 73, 978–986.
  2. Akarslan, E. and Hocaoglu, F.O., 2015. An application of MDLPF models for solar radiation forecasting. International Journal of Smart Grid and Clean Energy, 4(4), 299-304.
  3. Akarslan, E., Hocaoglu, F.O. and Edizkan R., 2018. Novel short term solar irradiance forecasting models. Renewable Energy, 123, 58–66. Akarslan E. and Hocaoglu F.O., 2017. A Novel Method Based on Similarity for Hourly Solar Irradiance Forecasting. Renewable Energy, 112, 337-346.
  4. Atal, B. and Hanauer, L., 1971. Speech Analysis and Synthesis by Linear Prediction of the Speech Wave. The Journal of the Acoustical Society of America, 50(2), 637-655.
  5. Bohlin, T., 1973. Comparison of Two Methods of Modeling Stationary EEG Signals. IBM Journal of Research and Development, 17(3), 194-205.
  6. Hocaoğlu, F.O., Gerek, O.N. and Kurban, M., 2007. A novel 2-D model approach for the prediction of hourly solar irradiance. LNCS Springer, 4507, 741-749.
  7. Hocaoğlu, F.O., Gerek, O.N. and Kurban, M., 2008. Hourly Solar Radiation Forecasting Using Optimal Coefficient 2-D Linear Filters and Feed-Forward Neural Networks. Solar Energy, 82 (8), 714–726.
  8. Köse, B., Recebl, Z. and Özkaymak, M., 2018. Stokastik Modellerle Rüzgar Hızı Tahmini; Karabük Örneği. Accessed September 22, 2018. http://www.isites.info/PastConferences/ISITES2014/ ISITES2014/papers/A12-ISITES2014ID128.pdf. Hui, l., Mi, X. and Li, Y., 2018. An Experimental Investigation of Three New Hybrid Wind Speed Forecasting Models Using Multi-Decomposing Strategy and ELM Algorithm. Renewable Energy, 123, 694–705.
  9. Loukatou, A., Sydney, H., Paul, J. and Peter, D., 2018. Stochastic Wind Speed Modelling for Estimation of Expected Wind Power Output. Applied Energy, 228, 1328-1340.
  10. Ma, J., Mitra, F. and Antoine, G., 2018. Flexible Wind Speed Generation Model: Markov Chain with an Embedded Diffusion Process. Energy, 164, 316-328.
  11. Markel, J. and Gray, A.Jr., Linear Prediction of Speech, Springer-Verlag Berlin Heidelberg, New York, 1976.
  12. Masseran, N., 2018. Integrated Approach for the Determination of an Accurate Wind-Speed Distribution Model. Energy Conversion and Management, 173, 56–64.
  13. Nascimento, C., Do, H., Lucio, P.S., Verçosa Leal Junior, J.B. and Marques de Carvalho, P.C., 2018. A Hybrid Model Based on Time Series Models and Neural Network for Forecasting Wind Speed in the Brazilian Northea