Prediction of Photovoltaic Panel Power Outputs using Time Series and Artificial Neural Network Methods

Year 2021, , 457 - 469, 07.09.2021

Aylin Duman Altan , Bahar Diken , Birol Kayişoğlu

https://doi.org/10.33462/jotaf.837446

Cited By: 7

Abstract

Solar energy is one of the renewable energy sources that has been in high demand in the last decades. With the increasing penetration of photovoltaic (PV) systems in around the world, accurate estimation of the power output of PV systems has become an important issue. Since PV systems directly convert sunlight into electrical energy, PV power output varies depending on environmental conditions. In order to deal with the periodic and non-stationary problems of PV output power, modelling methods are widely use for forecasting. The main purpose of this study is to lead an assessment of forecasting of the PV power outputs in short-time. For this purpose, data are obtained from experimental activities carried out on a real 250 kWp PV stystem, which is located in T.C Tekirdağ Namık Kemal University, Süleymanpaşa district of Tekirdağ province. All parametres are measured hourly with three times according to inclination of the panel setups (0˚, 30˚,60˚). In this sense, this study differs from the previously studies in literature, as it expands the forecasting model with considering of different panel angle. In the first stage, the significant variables for predicting PV power output are identified based on both correlation analysis and stepwise regression analysis. The findings are shown that solar radiation and angle of inclination of the panel are significant predictors of the generation of PV power. In the second stage, three different model are proposed based on Time Series Analysis (TSA) and Artificial Neural Network (ANN) approaches in order to predict power production of PV system. Furthermore, the accuracies of the models are analyzed in order to better understand the intrinsic errors caused and to evaluate its potential in energy forecasting applications. All models are compared in terms of the correlation coefficient (R), coefficient of determination (R2), mean absolute percentage error (MAPE). The results of analyses show that the ANN models have higher accuracy than the TSA model for forecasting PV power.

Keywords

Artificial neural network, Back propagation, PV power forecasting, ARIMA, Tekirdağ

Prediction of Photovoltaic Panel Power Outputs using Time Series and Artificial Neural Network Methods

Abstract

Güneş enerjisi, son yıllarda yüksek talep gören yenilenebilir enerji kaynaklarından biridir. Fotovoltaik (FV) sistemlerin dünya çapında artan yaygınlığıyla birlikte, FV sistemleri güç çıkışının doğru tahmini önemli bir konu haline gelmiştir. FV sistemleri doğrudan güneş ışığını elektrik enerjisine dönüştürdüğünden, FV güç çıkışı çevre koşullarına bağlı olarak değişkenlik gösterir. FV çıkış gücünün periyodik olma ve durağan olmama sorunlarının üstesinden gelebilmek amacı ile yapılan tahminlemelerde modelleme yöntemleri yaygın olarak kullanılmaktadır. Bu çalışmanın temel amacı, kısa süreli FV güç çıkışı tahminlerinin değerlendirilmesinde yol gösterici olmaktır. Bu amaçla toplanan veriler, Tekirdağ ili Süleymanpaşa ilçesine bağlı T.C Tekirdağ Namık Kemal Üniversitesi'nde kurulan bir 250 kWp’lık FV sistemi ile gerçekleştirilen deneysel faaliyetlerden elde edilmiştir. Tüm parametreler, saat bazında farklı panel eğim açıları (0˚, 30˚, 60˚) dikkate alınarak üçer kez ölçülmüştür. Bu anlamda, bu çalışma tahmin modelini farklı panel açılarını da dikkate alarak genişletmesi nedeniyle literatürdeki önceki çalışmalardan farklılık göstermektedir. İlk aşamada, FV güç çıktısını tahmin etmede kullanılacak anlamlı değişkenler hem korelasyon analizi hem de aşamalı regresyon analizi sonuçlarına göre belirlenmiştir. Bulgular, güneş radyasyonunun ve panel eğim açısının, FV gücü üretiminin önemli belirleyicileri olduğunu göstermiştir. İkinci aşamada, FV sisteminin güç üretimini tahmin etmek için Zaman Serisi Analizi (TSA) ve Yapay Sinir Ağı (YSA) yaklaşımlarına dayalı olarak üç farklı model önerilmiştir. Ayrıca, enerji tahmin uygulamalarında ortaya çıkan içsel hataları daha iyi anlamak ve potansiyelini değerlendirmek için modellerin doğrulukları analiz edilmiştir. Tüm modeller korelasyon katsayısı (R), belirleme katsayısı (R2), ortalama mutlak yüzde hatası (MAPE) açısından karşılaştırılmıştır. Analiz sonuçları, FV gücünü tahmin etmek için YSA modellerinin TSA modelinden daha yüksek doğruluğa sahip olduğunu göstermektedir.

Keywords

Yapay sinir ağı, Geri yayılım, ARIMA, Tekirdağ, FV güç tahmini

References

• Abdel-Nasser, M., Mahmoud, K. (2019). Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing & Applications, 31:2727–2740.
• Ahmed, U.M.K., Ampatzis, M., Nguyen, H.P., Kling, L.W. (2014). Application of time-series and Artificial Neural Network models in short term load forecasting for scheduling of storage devices. 49th International Universities Power Engineering Conference (UPEC), Cluj-Napoca, Romania. 10.1109/UPEC.2014.6934761.
• Al-Ali, R.A. (2016). Internet of things role in the renewable energy resources. Energy Procedia, 100:34-38.
• Arifin, F., Robbani, H., Annisa, T., Ma’Arof, N.N.M.I. (2019). Variations in the number of layers and the number of neurons in artificial neural networks: Case study of pattern recognition. J. Phys. Conf. Ser. 1413: 0–6.
• Dandıl, E., Gürgen, E. (2017). Prediction of photovoltaic panel power output using artificial neural networks learned by heuristic algorithms: A comparative study. International Conference on Computer Science and Engineering (UBMK), Antalya, 397-402.
• GEPA (2019). Solar Energy Potential Atlas, http://www.yegm.gov.tr/MyCalculator/, Access Date: 20.05.2020.
• Hossain, M., Mekhilef, S., Danesh, M., Olatomiwa, L., Shamshirband, S. (2017). Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. Journal of Cleaner Production, 167:395-405.
• Huang, C., Bensoussan, A., Edesess, M., Tsui, L.K. (2016). Improvement in artificial neural network-based estimation of grid connected photovoltaic power output. Renewable Energy, 97:838-848.
• İzgi, E., Öztopal, A., Yerli, B., Kaymak, M.K., Şahin, A.D. (2012). Short–mid-term solar power prediction by using artificial neural networks. Solar Energy Volume 86, Pages 725-723.
• Jumaat, A.S., Crocker, F., Wahab, A.H.M., Mohd, H., Radzi, M.H.N., Othman, F.M. (2018). Prediction of Photovoltaic (PV) Output using artificial neutral network (ANN) based on ambient factors. Journal of Physics: Conference Series. 1049.
• Kardakos, G.E., Alexiadis, C.M., Vagropoulos, I.S., Simoglou, K.C., Biskas, N.P., Bakirtzis, G.A. (2013). Application of Time Series and Artificial Neural Network Models in Short-term Forecasting of PV Power Generation. 48th International Universities' Power Engineering Conference (UPEC), Dublin, Ireland, 10.1109/UPEC.2013.6714975
• Kumar, R.K., Kalavathib, K.S. (2018). Artificial intelligence based forecast models for predicting solar power generation. Materials Today: Proceedings, 5:796–802.
• Liu, L., Liu, D., Sun, Q., Li, H., Wnnersten R. (2017). Forecasting power output of photovoltaic system using a bp network method. Energy Procedia, 142:780–786.
• Mukaram, Z.M., Yusof, F. (2017). Solar radiation forecast using hybrid SARIMA and ANN model: A case study at several locations in Peninsular Malaysia. Malaysian Journal of Fundamental and Applied Sciences Special Issue on Some Advances in Industrial and Applied Mathematics, 346-350.
• Muzaffar, B. (2016). The Development and Validation of a Scale to Measure Training Culture: The TC Scale. Journal of Culture, Society and Development, 23: 49-58.
• Netsanet, S., Zhang, J., Zheng, D., Hui, M. (2016). Input parameters selection and accuracy enhancement techniques in PV forecasting using artificial neural network. 2016 IEEE International Conference on Power and Renewable Energy (ICPRE), Shanghai, 565-569, doi: 10.1109/ICPRE.2016.7871139.
• Nitisanon, S., Hoonchareon, N. (2018). Solar power forecast with weather classification using self-organized map. IEEE Power & Energy Society General Meeting (PESGM), Chicago, IL, USA, 10.1109/PESGM.2017.8274548
• Özkişi, H., Topaloğlu, M. (2017). The Estimation of the photovoltaic cell productivity with the use of artificial neural network. International Journal of Informatics Technologies, 10(3):247-253.
• Ramsami, P., Oree, V. (2015). A hybrid method for forecasting the energy output of photovoltaic systems. Energy Conversion and Management, 95:406-413.
• Raza, Q. M., Nadarajah, M., Ekanayake, C. (2016) On recent advances in PV output power forecast. Solar Energy, 136:125-144.
• Semenkina, M., Akhmedoval, S., Semenkin, E., Ryzhikov, I. (2014) Spacecraft Solar Arrays Degradation Forecasting with Evolutionary Designed ANN-based Predictors. ICINCO - Proc. 11th Int. Conf. Informatics Control. Autom. Robot,421-428, 10.5220/0005122004210428.
• Thomas, A.J., Petridis, M., Walters, S.D., Gheytassi, S.M., Morgan, R.E. (2017) Two hidden layers are usually better than one. Engineering Applications of Neural Networks, Springer International Publishing, 279-290. https://doi.org/10.1007/978-3-319-65172-9_24.
• Vaz, R.G.A, Elsinga, B., Van Sark, W. Brito, C. M. (2016). An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands. Renewable Energy, 85:631-641.
• Vagropoulos, I. S., Chouliaras, I. G., Kardakos, G.E., Simoglou, K.C., Bakirtzis, G.A. (2016). Comparison of SARIMAX, SARIMA, Modified SARIMA and ANN-based Models for Short-Term PV Generation Forecasting. IEEE International Energy Conference (ENERGYCON), Leuven, Belgium, 10.1109/ENERGYCON.2016.7514029
• Wang, G., Su, Y., Shu, L. (2016). One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models. Renewable Energy, 96:469-478.
• Yüksel Türkboyları, E. (2018). Tekirdağ koşullarında güneş kolektörlerinden elde edilen ısı enerjisi ile sera toprağının dezenfekte edilmesi. Tekirdağ Ziraat Fakültesi Dergisi, 15(01):123-128.
• Yüksel Türkboyları, E., Yüksel, A.N. (2021). Use of solar panel system in vermicompost (worm manure) production facilities as source of energy. Journal of Tekirdag Agricultural Faculty, 18(1): 91-97.