SUN'IY INTELLEKT ASOSIDA SHAMOL TEZLIGI PROGNOZINI YARATISH
Kalit so‘zlar:
sun’iy intellekt, shamol tezligi, prognozlash, meteorologiya, mashinaviy o‘qitish, LSTM, sun’iy neyron tarmoqlari, vaqt qatori, bashoratlash modeli, chuqur o‘rganish.Annotatsiya
Ushbu ishda sun’iy intellektdan foydalanib shamol tezligini prognozlashning zamonaviy yondashuvlari yoritiladi. Tadqiqotda meteorologik ma’lumotlar, atmosfera parametrlarining vaqt bo‘yicha o‘zgarishi va tarixiy kuzatuvlar asosida sun’iy neyron tarmoqlari, LSTM kabi chuqur o‘rganish modellari qo‘llanildi. Natijalar SI asosidagi yondashuv shamol tezligini an’anaviy usullarga nisbatan tezroq va aniqroq prognozlash imkonini berishini ko‘rsatadi.
Foydalaniladigan adabiyotlar
Hochreuther, P., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
— Vaqt qatorlarini bashoratlashda keng qo‘llaniladigan LSTM modeli haqida asosiy ilmiy manba.
Brownsword, C., & Wilson, D. (2019). Machine Learning Approaches for Wind Speed Forecasting. Renewable Energy Journal, 140, 100–112.
— Shamol tezligini prognozlashda mashinaviy o‘qitish usullarining taqqoslamasi.
Giebel, G. (2011). The State-of-the-Art in Short-Term Prediction of Wind Power. ANEMOS Project Report.
— Shamol energiyasi va qisqa muddatli prognozlash bo‘yicha yirik tadqiqot loyihasi.
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with Artificial Neural Networks: The State of the Art. International Journal of Forecasting, 14(1), 35–62.
— ANN modellarining prognozlashdagi qo‘llanilishi.
Soman, S. S., Zareipour, H., Malik, O., & Mandal, P. (2010). A Review of Wind Power and Wind Speed Forecasting Methods. IEEE Power & Energy Society General Meeting.
— Shamol tezligi prognozi bo‘yicha mashhur tahliliy maqola.
Foley, A. M., et al. (2012). Current Methods and Advances in Forecasting of Wind Power Generation. Renewable Energy, 37(1), 1–8.
— Shamol energiyasini bashoratlashda ilg‘or texnologiyalar sharhi.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
— Mashinaviy o‘qitishning nazariy asoslari bo‘yicha klassik qo‘llanma.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
— Chuqur o‘rganish texnologiyalarining nazariy va amaliy asoslari.
Monteiro, C., et al. (2009). Wind Power Forecasting: State-of-the-Art 2009. Report for the European Wind Energy Association.
— Shamol energiyasi prognozlari bo‘yicha xalqaro hisobot.
Kariniotakis, G. (2017). Renewable Energy Forecasting: From Models to Applications. Woodhead Publishing.
— Qayta tiklanuvchi energiya prognozlash bo‘yicha keng qamrovli ilmiy manba.
