电网技术2026，Vol.50Issue(1)：中插148,334-344,中插149,13.DOI:10.13335/j.1000-3673.pst.2025.0365
考虑风速分段控制和功率连续演化的短期风电功率预测
Short-term Wind Power Forecasting Considering Wind Speed Piecewise Control Strategy and Power Continuous Evolution Mechanism
摘要
Abstract
The current forecasting models for wind power time series have limitations in handling irregularly sampled time series and need to pay more attention to the piecewise control characteristics of wind speed on wind power output.To address these issues,this paper proposes a short-term wind power forecasting method based on a piecewise controlled hybrid differential neural network.First,the wind speeds at discrete timesteps are converted into a continuous path using cubic spline interpolation.The hybrid differential neural network is employed to model the temporal inertia of wind power and the regulatory effects of continuous wind speed on wind power,thereby capturing its dynamic evolution process in full.Then,based on the differentiated control modes for wind power output when wind speed falls into different numerical intervals,a multilayer perceptron is employed to dynamically output continuous weighting values representing the affiliation of wind speed to distinct control intervals.Finally,the short-term power forecasts for the wind farm are output,which can simultaneously satisfy the temporal inertia of wind power and the piecewise controlling patterns of wind speed.Practical case studies demonstrate that this method exhibits significant advantages in handling irregularly sampled time series,with the proposed piecewise control hybrid differential neural network outperforming the popular RNN networks in terms of forecasting accuracy and reliability.关键词
短期风电功率预测/时间序列/神经控制微分方程/门控函数/分段控制Key words
short-term wind power forecasting/time series/neural control differential equations/gate control function/piecewise control分类
信息技术与安全科学引用本文复制引用
李丹,黄烽云,缪书唯,唐建,罗娇娇..考虑风速分段控制和功率连续演化的短期风电功率预测[J].电网技术,2026,50(1):中插148,334-344,中插149,13.基金项目
国家自然科学基金项目(51807109).Project Supported by National Natural Science Foundation of China(51807109). （51807109）
