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Wind Energy Prediction Using Long Short Term Memory (LSTM)  
Yazarlar
Dr. Öğr. Üyesi Merdin DANIŞMAZ Dr. Öğr. Üyesi Merdin DANIŞMAZ
Kırşehir Ahi Evran Üniversitesi, Türkiye
Özet
Researchers, financial backers, and policymakers have understood the benefit of giving close ideal forecasts of environmentally friendly power. Therefore, current examinations show progressions in systems for giving more exact energy expectations. ARIMA troupe with ANN performed better for short and super short terms a few hours to come. SVR, Kalman channels, and their groups, then again, have exhibited great execution for medium-term wind speed expectations. As of late, brain organizations, explicitly repetitive brain organizations (RNN), have announced tremendous outcome in time series expectations, especially for medium and long haul estimating. The goal of this work is to assess the accuracy difference between precise weather data collected directly at the PV station and interpolated weather data. Data errors were recorded as a result of sensor malfunctions and were cleared using winsorization ...
Anahtar Kelimeler
Makale Türü Özgün Makale
Makale Alt Türü Uluslararası alan indekslerindeki dergilerde yayımlanan tam makale
Dergi Adı Solid State Technology
Dergi Tarandığı Indeksler
Makale Dili İngilizce
Basım Tarihi 12-2022
Cilt No 65
Sayı 1
Sayfalar 342 / 358
BM Sürdürülebilir Kalkınma Amaçları
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