Abstract
To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. Then, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model.
Highlights
Offshore wind power over the past decade remains unprecedented [1, 2]
Offshore wind power prediction is based on historical output data, Numeric Weather Prediction (NWP), and measured meteorological data, and a prediction model is established to predict the future offshore wind power output [12,13,14,15]
This paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale
Summary
Offshore wind power over the past decade remains unprecedented [1, 2]. And offshore wind power has a pivotal and mature role in renewable energy. ere are high offshore wind speed, large turbine capacity, annual operating hours of up to 4,000 hours or more, and offshore wind power efficiency compared to onshore wind power annual power generation of 20% to 40% more, with higher energy efficiency [3,4,5]. Erefore, it is necessary to predict wind speed and predict short-term wind power in practical applications [11]. Reference [16] arrested the mining and analysis of the inherent fluctuation law of wind power as the starting point and studied new methods around the utilization of wind power fluctuation law in ultra-short-term forecasting. Reference [17] proposed a new method based on the extreme learning machine and the bootstrap prediction interval formula to predict wind power in different seasons and verify its effectiveness. Reference [19] employed support vector machine (SVM) regression to predict the wind power model and effectively verified that, for different wind power weather types, the neighboring days were selected to establish the reliability based on its reliability. This paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. In the case of processing abnormal data and data normalization and differential smoothing power series, the rolling PSO-LSTM (Particle Swarm Optimization-Long Short-Term Memory) model is established for training, and the training results are utilized to predict the data. e training and prediction results using the real data of offshore wind power show that this prediction model proposed in this paper has higher prediction accuracy than traditional prediction models and can more accurately predict the second-level wind power in the four hours
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