Machine learning techniques for predicting the production capacity of a windfarm based on daily wind speed data

چکیده مقاله

Among renewable energy sources, wind energy is a substantial and suitable source with the capacity to provide electricity continuously and sustainably However, wind energy has some obstacles, including high initial investment prices, the fixed nature of wind turbines, and the difficulty in locating wind efficient energy zones Long term wind power forecasting was accomplished in this work by utilizing two machine learning algorithms based on daily wind speed data We suggested a system for forecasting wind power values based on machine learning algorithms The findings indicated that machine learning techniques might be used to anticipate long term wind power values based on past wind speed data Furthermore, the results demonstrated that machine learning based models could be applied to not model trained sites This research revealed that machine learning techniques might be effectively used before constructing wind turbines in an unknown geographical region

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

Malakoti, Seyed Matin and Rikhtehgar Ghiasi, Amir,1401,Machine learning techniques for predicting the production capacity of a windfarm based on daily wind speed data,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