Evaluation of wind energy investment with artificial neural networks

Hasan Huseyin Yildirim; Mehmet Yavuz

doi:10.11121/ijocta.01.2019.00780

Authors

Hasan Huseyin Yildirim Balikesir University http://orcid.org/0000-0002-5840-8418
Mehmet Yavuz http://orcid.org/0000-0002-3966-6518

DOI:

https://doi.org/10.11121/ijocta.01.2019.00780

Keywords:

Energy, Wind Energy, Forecasting, Energy Investment Valuation, Artificial Neural Networks

Abstract

Countries aiming for sustainability in economic growth and development ensure the reliability of energy supplies. For countries to provide their energy needs uninterruptedly, it is important for domestic and renewable energy sources to be utilised. For this reason, the supply of reliable and sustainable energy has become an important issue that concerns and occupies mankind. Of the renewable energy sources, wind energy is a clean, reliable and inexhaustible source of energy with low operating costs. Turkey is a rich nation in terms of wind energy potential. Forecasting of investment efficiency is an important issue before and during the investment period in wind energy investment process because of high investment costs. It is aimed to forecast the wind energy products monthly with multilayer neural network approach in this study. For this aim a feed forward back propagation neural network model has been established. As a set of data, wind speed values 48 months (January 2012-December 2015) have been used. The training data set occurs from 36 monthly wind speed values (January 2012-December 2014) and the test data set occurs from other values (January-December 2015). Analysis findings show that the trained Artificial Neural Networks (ANNs) have the ability of accurate prediction for the samples that are not used at training phase. The prediction errors for the wind energy plantation values are ranged between 0.00494-0.015035. Also the overall mean prediction error for this prediction is calculated as 0.004818 (0.48%). In general, we can say that ANNs be able to estimate the aspect of wind energy plant productions.

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Author Biography

Mehmet Yavuz

Necmettin Erbakan University, Faculty of Science, Department of Mathematics-Computer, KONYA

References

http://www.enerji.gov.tr [Accessed 11 October 2018].

Develi, A. & Kaynak, S. (2012). Energy Economics, Frankfurt am Main, ISBN 3631633335, DEU:Peter Lang AG.

Turkish Wind Energy Association, Turkish Wind Statistic Report 2018 July, https://www.tureb.com. tr/yayinlar/turkiye-ruzgar-enerjisi-istatistik-raporu-temmuz-2018.

Ministry of Energy and Natural Resources (2014). 2015-2019 Strategy Plan, http://sp.enerji.gov.tr/ ETKB_2015_2019_Stratejik_Plani.pdf, 26.

Bahgat, G. (2006). Europe’s energy security: challanges and opportunities, International Affairs, 82(5), 961-975.

Kalogirou, S.A., Neocleous, C.C., & Schizas, C.N. (1996). Artificial neural networks in modelling the heat-up response of a solar steam generation plant, Proceedings of the International Conference EANN, 1-4.

Kalogirou, S.A., Neocleous, C.C., & Schizas, C.N. (1997). Artificial neural networks for the estimation of the performance of a parabolic trough collector steam generation system, Proceedings of the International Conference EANN, 227-232.

Kemmoku, Y., Orita, S., Nakagawa, S., & Sakakibara, T. (1999). Daily insolation forecasting using a multi-stage neural network, Solar Energy, 66(3), 193-199.

Kajl, S., Roberge, M.A., Lamarche, L., & Malinowski, P. (1997). Evaluation of building energy consumption based on fuzzy logic and neural networks applications, Proceedings of the International Conference CLIMA 2000, 264-274.

Datta, D., Tassou, S.A., & Marriott, D. (2000). Application of neural networks for the prediction of the energy consumption in a supermarket, Proceedings of the International Conference CLIMA 2000, 98-107.

Dorvlo, A.S., Jervase, J.A., & Al-Lawati, A. (2002). Solar radiation estimation using artificial neural networks, Applied Energy, 71(4), 307-319.

Aydinalp, M., Ugursal, V.I., & Fung, A.S. (2002). Modeling of the Appliance, Lighting, and Space-Cooling Energy Consumptions in the Residential Sector Using Neural Networks, Applied Energy, 71(2), 87-110.

More, A., & Deo M. (2003). Forecasting Wind with Neural Networks, Marine Structures, 16(1), 35-49.

Öztopal, A. (2006). Artificial Neural Network Approach to Spatial Estimation of Wind Velocity Data, Energy Conversion and Management, 47(4), 395-406.

Ermis, K., Midilli, A., Dincer, I., & Rosen, M.A. (2007). Artificial neural network analysis of world green energy use. Energy Policy, 35(3), 1731-1743.

Amrouche, B., & Le Pivert, X. (2014). Artificial neural network based daily local forecasting for global solar radiation. Applied Energy, 130, 333-341.

Velázquez, S., Carta, J. A., & Matías, J. M. (2011). Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study. Renewable and Sustainable Energy Reviews, 15(3), 1556-1566.

Zhao, P., Wang, J., Xia, J., Dai, Y., Sheng, Y., & Yue, J. (2012). Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China. Renewable Energy, 43, 234-241.

Bigdeli, N., Afshar, K., Gazafroudi, A.S., & Ramandi, M.Y. (2013). A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada. Renewable and sustainable energy reviews, 27, 20-29.

Castellani, F., Burlando, M., Taghizadeh, S., Astolfi, D., & Piccioni, E. (2014). Wind energy forecast in complex sites with a hybrid neural network and CFD based method. Energy Procedia, 45, 188-197.

Qin, S., Liu, F., Wang, J., & Song, Y. (2015). Interval forecasts of a novelty hybrid model for wind speeds. Energy Reports, 1, 8-16.

Wang, J., Qin, S., Zhou, Q., & Jiang, H. (2015). Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China. Renewable Energy, 76, 91-101.

Ata, R. (2015). Artificial Neural Networks Applications in Wind Energy Systems: A Review, Renewable and Sustainable Energy Reviews, 49, 534-562.

Sakarya, S., Yavuz, M., Karaoglan, A.D., & Özdemir, N. (2015). Stock market index prediction with neural network during financial crises: a review on BIST-100. Financial Risk and Management Reviews, 1(2), 53-67.

Yavuz, M., Sakarya, S., & Özdemir, N. (2015). Yapay Sinir Ağları ile Risk-Getiri Tahmini ve Portföy Analizi, Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(4), 87-107.