Uzun Kısa Dönemli Bellek Ağına Dayalı Saatlik Güneş Işınımının Gelecek Ay Tahmini

Year 2023, , 225 - 232, 30.03.2023

İnayet Özge Aksu

https://doi.org/10.21605/cukurovaumfd.1273795

Cited By: 2

Abstract

Günümüzde nüfus artışına ve teknolojinin ilerlemesine paralel olarak ülke yöneticileri açısından kalkınma kaygıları ortaya çıkmaya başlamıştır. Bu nedenle klasik enerji kaynaklarına alternatif çözümler aranmaktadır. Yenilenebilir enerji kaynakları günümüzde önerilen enerji kaynaklarından biridir. Güneş enerjisi de dahil olmak üzere yenilenebilir enerji kaynaklarının popülaritesi her geçen gün artmaktadır. Güneş enerjisi, diğer yenilenebilir enerji kaynaklarından daha hızlı yayılma potansiyeline ve erişilebilirliğine sahiptir. Türkiye genel olarak güneş kuşağı olarak adlandırılan güneş enerjisi potansiyeli yüksek bir bölgede yer aldığından bölgemizde enerji kaynağı olarak güneş enerjisini tercih etmek doğru bir karardır. Bu çalışmada, kısa dönem güneş ışınımı tahmini için Uzun Kısa Süreli Bellek (LSTM) Ağı yöntemi kullanılarak zaman serisi tahmini kullanılmıştır. Sonuçların başarısını ortaya koymak için Yapay Sinir Ağları (YSA) yöntemi ile karşılaştırma yapılmıştır. Son olarak güneş ışınımının tahmin sonuçları istatistiksel testlerle karşılaştırılmış ve hata analizleri sayısal olarak verilmiştir.

Keywords

Uzun kısa süreli hafıza, Güneş ışınımı, Zaman serisi tahmini, Güneş enerjisi

Next-Month Prediction of Hourly Solar Irradiance based on Long Short-Term Memory Network

Abstract

Today, in parallel with the population growth and the advancement of technology, development concerns have started to arise in terms of country administrators. Therefore, alternative solutions to classical energy sources are sought. Renewable energy sources are one of the preferred energy sources today. The popularity of renewable energy sources, including solar energy, is increasing day by day. Solar energy has the potential and accessibility to spread faster than other renewable energy sources. Since Türkiye is located in a region with a high potential in terms of solar energy, which is generally called the sun belt, it is a right decision to prefer solar energy as an energy source in our region. In this study, time series prediction using Long Short-Term Memory (LSTM) Network method is used for short-term solar irradiance estimation. In order to demonstrate the success of the results, a comparison was made with the Artificial Neural Network (ANN) method. Finally, prediction results of solar irradiance were compared with statistical tests and error analyzes were given in numerically.

Keywords

Long short-term memory, Solar irradiance, Time series prediction, Solar energy

References

• ⦁ Bayrakcı, A.G., Koçar, G., 2012. Utilization of Renewable Energies in Turkey's Agriculture. Renewable and Sustainable Energy Reviews, 16(1), 618-633.
• ⦁ Angstrom, A., 1924. Solar and Terrestrial Radiation. Report to the International Commission for Solar Research on Actinometric Investigations of Solar and Atmospheric Radiation. Quarterly Journal of the Royal Meteorological Society, 50(210), 121-126.
• ⦁ Sonmete, M.H., Ertekin, C., Menges, H.O., Hacıseferoğullari, H., Evrendilek, F., 2011. Assessing Monthly Average Solar Radiation Models: A Comparative Case Study in Turkey. Environmental Monitoring and Assessment, 175, 251–77.
• ⦁ Badescu, V., Gueymard, C.A., Cheval, S., Oprea, C., Baciu, M., Dumitrescu, A., Iacobescu, F., Rada, C., 2012. Computing Global and Diffuse Solar Hourly Irradiation on Clear Sky. Review and Testing of 54 Models. Renewable and Sustainable Energy Reviews, 16(3), 1636-1656.
• ⦁ Khatib, T., Mohamed, A., Sopian, K., 2012. A Review of Solar Energy Modeling Techniques. Renewable and Sustainable Energy Reviews 16, 2864-9.
• ⦁ Behrang, M.A., Assareh, E., Noghrehabadi, A.R., Ghanbarzadeh, A., 2011. New Sunshinebased Models for Predicting Global Solar Radiation Using PSO (Particle Swarm Optimization) Technique. Energy, 36, 3036-49.
• ⦁ Al-Alawi, S.M., Al-Hinai, H.A., 1998. An ANN-based Approach for Predicting Global Radiation in Locations with No Direct Measurement Instrumentation, Renewable Energy, 14, 1-4, 199-204.
• ⦁ Mellit, A., Pavan, A.M., 2010. A 24-h Forecast of Solar Irradiance Using Artificial Neural Network: Application for Performance Prediction of a Grid-connected PV Plant at Trieste, Italy. Solar Energy, 84(5), 807-821.
• ⦁ Khatib, T., Mohamed, A., Sopian, K., Mahmoud, M., 2012. Solar Energy Prediction for Malaysia Using Artificial Neural Networks. International Journal of Photoenergy, 2012.
• ⦁ Alharbi, M.A., 2013. Daily Global Solar Radiation Forecasting Using ANN and Extreme Learning Machines: A Case Study in Saudi Arabia (Master of Applied Science Thesis). Halifax, Nova Scotia: Dalhousie University.
• ⦁ Pang, Z., Niu, F., O’Neill, Z., 2020. Solar Radiation Prediction Using Recurrent Neural Network and Artificial Neural Network: A Case Study with Comparisons. Renewable Energy, 156, 279-289.
• ⦁ Azeez, M.A.A., 2011. Artificial Neural Network Estimation of Global Solar Radiation Using Meteorological Parameters in Gusau, Nigeria. Archives of Applied Science Research, 3(2), 586-95.
• ⦁ Qing, X., Niu, Y., 2018. Hourly Day-ahead Solar Irradiance Prediction Using Weather Forecasts by LSTM. Energy, 148, 461-468.
• ⦁ Kara, A., 2019. Global Solar Irradiance Time Series Prediction Using Long Short-Term Memory Network. Gazi Üniversitesi Fen Bilimleri Dergisi, Part C: Tasarım ve Teknoloji, 4, 7.
• ⦁ Yildirim, A., Bilgili, M., Ozbek, A., 2023. One-hour-ahead Solar Radiation Forecasting by MLP, LSTM, and ANFIS Approaches. Meteorology and Atmospheric Physics, 135(1), 10.
• ⦁ Bounoua, Z., Mechaqrane, A., 202). Hourly and Sub-hourly Ahead Global Horizontal Solar Irradiation Forecasting via A Novel Deep Learning Approach: A Case Study. Sustainable Materials and Technologies, e00599.
• ⦁ Marinho, F.P., Rocha, P.A., Neto, A.R., Bezerra, F.D., 2023. Short-Term Solar Irradiance Forecasting Using CNN-1D, LSTM, and CNN-LSTM Deep Neural Networks: A Case Study with the Folsom (USA) Dataset. Journal of Solar Energy Engineering, 145(4), 041002.
• ⦁ Hochreiter, S., Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
• ⦁ Pustokhin, D.A., Pustokhina, I.V., Dinh, P.N., Phan, S.V., Nguyen, G.N., Joshi, G.P., 2023. An Effective Deep Residual Network Based Class Attention Layer with Bidirectional LSTM for Diagnosis and Classification of COVID-19. Journal of Applied Statistics, 50(3), 477-494.