Open AccessModeling and Forecasting of Wind Power Output of Urban Regional Energy Internet Based on Deep Learning
Author(s) -
Geping Weng,
Chuanxun Pei,
Jiaorong Ren,
Hao Jiang,
Jia Xu,
Wanhua Zheng,
Yuan Liu,
Tian Gao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1732/1/012190
Subject(s) - wind power , the internet , volatility (finance) , wind power forecasting , energy (signal processing) , renewable energy , meteorology , environmental science , environmental economics , computer science , power (physics) , electric power system , econometrics , engineering , economics , geography , electrical engineering , world wide web , statistics , physics , mathematics , quantum mechanics
Clean energy will account for a lot in the future energy structure with the emergence of Urban Regional Energy Internet. However, because of the intermittent and volatility of clean energy like wind energy, its main role in energy supply has restricted. Therefore, predicting wind power accurately is of great significance in the safe operation of the power system. In response to the above problems, this paper proposed a time series forecasting model of wind power based on BiLSTM, and analyzes the actual wind data in Urban Regional Energy Internet.