چکیده مقاله
Solar energy forecasting is a critical component of boosting the competitiveness of solar power plants in the energy market and decreasing economic and societal dependency on fossil fuels The datasets we used in this study represent data from 2019 to 2021and are related to California Decision Tree and Extra Tree have provided high accuracies Extra Tree has shown good performance with about 4 seconds predictions Decision Tree has shown as good as Extra Tree performance with approximately 1 7 seconds predictions
کلیدواژهها
نویسندگان
Seyed Matin Malakoti
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Amir Rikhtehgar Ghiasi
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
شیوه ارجاع
Malakoti, Seyed Matin and Rikhtehgar Ghiasi, Amir,1401,Machine learning algorithms are being used to predict the output capacity of asolar farm in California in order to substitute renewable energy with fossil fuels,The 1st National Conference on Environmental Challenges: Green Industry and Mining,Tehran
ارائهشده در
مجموعه مقالات نخستین کنفرانس ملی چالش های محیط زیست: صنعت و معدن سبز28 اردیبهشت 1401 · تهران