Short-term wind power prediction with harmony search algorithm: Belen region

Yıl 2022, Cilt: 6 Sayı: 3, 251 - 255, 20.07.2022

Esra Saraç Eşsiz

https://doi.org/10.31127/tuje.970959

Öz

Wind power is the fastest-growing technology among alternative energy production sources. Reliable forecasting of short-term wind power plays a critical role in the acquisition of most of the generated energy. In this study, short-term wind power forecast is performed using radial-based artificial neural networks, forecast error and cost to be minimized with the harmony search algorithm. Experimented results show that, we can predict wind power with fewer features and less error by using harmony search algorithm. A %7 percent improvement in RMSE rate has been achieved with the proposed method for short-term wind power prediction.

Anahtar Kelimeler

Renewable Energy, Wind Power, Artificial neural networks, Feature selection, Short-term forecast

Kaynakça

• Ahmed S, Khalid M & Akram U (2017). A method for short-term wind speed time series forecasting using Support Vector Machine Regression Model. In 2017 6th International Conference on Clean Electrical Power (ICCEP) (pp. 190-195). IEEE.
• Alberdi R, Fernandez E, Albizu I, Mazón A J, Bedialauneta M T & Sagastabeitia K J (2017). Wind speed forecasting in overhead lines for system operation. In 2017 IEEE Manchester PowerTech (pp. 1-5). IEEE.
• Baby C M, Verma K & Kumar R (2017). Short term wind speed forecasting and wind power estimation: A case study of Rajasthan. In 2017 International Conference on Computer, Communications and Electronics (Comptelix) (pp. 275-280). IEEE.
• Bae H R, Tsuji T, Oyama T & Uchida K (2017). Frequency control in power system based on balancing market considering wind power forecasting error. In 2017 6th International Conference on Clean Electrical Power (ICCEP) (pp. 376-383). IEEE.
• Buhmann M D (2003). Radial basis functions: theory and implementations (Vol. 12). Cambridge university press.
• Cui M, Zhang J, Wang Q, Krishnan V & Hodge B M (2017). A data-driven methodology for probabilistic wind power ramp forecasting. IEEE Transactions on Smart Grid, 10(2), 1326-1338.
• Dowell J & Pinson P (2015). Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Transactions on Smart Grid, 7(2), 763-770.
• Eseye A T, Zhang J, Zheng D, Ma H & Jingfu G (2017). Short-term wind power forecasting using a double-stage hierarchical hybrid GA-ANN approach. In 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA) (pp. 552-556). IEEE.
• Exizidis L, Kazempour J, Pinson P, De Grève Z & Vallée F (2017). Impact of public aggregate wind forecasts on electricity market outcomes. IEEE Transactions on Sustainable Energy, 8(4), 1394-1405.
• Guangyu X, Shaoping S & Jietao S (2017). Wind speed forecast for the stratospheric airship by incremental extreme learning machine. In 2017 36th Chinese Control Conference (CCC) (pp. 4088-4092). IEEE.
• Kanna B & Singh S N (2016). Long term wind power forecast using adaptive wavelet neural network. In 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) (pp. 671-676). IEEE.
• Khodayar M, Kaynak O & Khodayar M E (2017). Rough deep neural architecture for short-term wind speed forecasting. IEEE Transactions on Industrial Informatics, 13(6), 2770-2779.
• Li H, Eseye A T, Zhang J & Zheng D (2017). A double-stage hierarchical hybrid PSO-ANFIS model for short-term wind power forecasting. In 2017 Ninth Annual IEEE Green Technologies Conference (GreenTech) (pp. 342-349). IEEE.
• Meng K, Yang H, Dong Z Y, Guo W, Wen F & Xu Z (2015). Flexible operational planning framework considering multiple wind energy forecasting service providers. IEEE Transactions on Sustainable Energy, 7(2), 708-717.
• Paixão J L, Rigodanzo J, Sausen J P, Hammarstron J R, Abaide A R, Canha L N & Santos M M (2017). Wind generation forecasting of short and very short duration using Neuro-Fuzzy Networks: A case study. In 2017 International Conference on Modern Power Systems (MPS) (pp. 1-6). IEEE.
• Quan H, Srinivasan D & Khosravi A (2013). Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE transactions on neural networks and learning systems, 25(2), 303-315.
• Shi J, Gong Y, Liu X & Zhu X (2016). Model optimization for very-short-term wind power forecasting using Hilbert-Huang Transform. In 2016 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE) (pp. 239-243). IEEE.
• Wang Z, Wang W, Liu C, Wang Z & Hou Y (2017). Probabilistic forecast for multiple wind farms based on regular vine copulas. IEEE Transactions on Power Systems, 33(1), 578-589.
• Xu Q, He D, Zhang N, Kang C, Xia Q, Bai J & Huang J (2015). A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining. IEEE Transactions on sustainable energy, 6(4), 1283-1291.
• Yang L, He M, Zhang J & Vittal V (2015). Support-vector-machine-enhanced markov model for short-term wind power forecast. IEEE Transactions on Sustainable Energy, 6(3), 791-799.
• Zhou L, Li F & Tong X (2016a). Active network management considering wind and load forecasting error. IEEE Transactions on Smart Grid, 8(6), 2694-2701.
• Zhou Y, Yan Z & Li N (2016b). A novel state of charge feedback strategy in wind power smoothing based on short-term forecast and scenario analysis. IEEE Transactions on Sustainable Energy, 8(2), 870-879.
• Zhuo W & Savkin A V (2017). Wind power dispatch based on wind forecasting, electricity price and battery lifetime estimation. In 2017 36th Chinese Control Conference (CCC) (pp. 2915-2920). IEEE.
• Woo Z, Hoon J, Loganathan G V (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION. 2001;76(2):60-68. doi:10.1177/003754970107600201