CAJ | 학술논문

概率预测有别于期望值预测,能够提供被预测量的概率分布信息。文中提出一种基于分量稀疏贝叶斯学习的风电场短期输出功率概率预测方法。该方法采用分量预测方式,应用离散正交小波变换Mallat算法将风电场输出功率分解为体现输出功率变化主趋势的趋势分量和平稳度较好的扰动分量。利用风速与风电场输出功率趋势上较强的相关性,结合趋势分量的自相关性对趋势分量进行预测;同时,根据扰动分量近似平稳的特点,利用其自身的自相关性对扰动分量进行预测。文中基于稀疏贝叶斯学习理论构建预测模型,实现对趋势分量、扰动分量以及原风电场输出功率的概率预测,并通过构建多学习机实现风电场输出功率的多步预测。算例分析部分通过对某处风电场7 200次的连续预测,验证了所提出方法的有效性。
개솔예측유별우기망치예측,능구제공피예측량적개솔분포신식。문중제출일충기우분량희소패협사학습적풍전장단기수출공솔개솔예측방법。해방법채용분량예측방식,응용리산정교소파변환Mallat산법장풍전장수출공솔분해위체현수출공솔변화주추세적추세분량화평은도교호적우동분량。이용풍속여풍전장수출공솔추세상교강적상관성,결합추세분량적자상관성대추세분량진행예측;동시,근거우동분량근사평은적특점,이용기자신적자상관성대우동분량진행예측。문중기우희소패협사학습이론구건예측모형,실현대추세분량、우동분량이급원풍전장수출공솔적개솔예측,병통과구건다학습궤실현풍전장수출공솔적다보예측。산례분석부분통과대모처풍전장7 200차적련속예측,험증료소제출방법적유효성。
Probabilistic forecast is different from expectation forecast by the capability of forecasting the distribution of random variables. Based on the componential sparse Bayesian learning, this paper proposes a novel method to forecast the short-term wind farm generation. With this method, a time series of wind farm generation is decomposed into trend component and disturbance components by discrete wavelet decomposition Mallat algorithm. The trend component is then forecasted according to its strong correlation with wind speed and its self-correlation property,, while the disturbance components, which are more stationary, are forecasted according to their self-correlation property. A sparse Bayesian learning method is used to establish the forecasting model to give probabilistic forecasts to trend component, disturbance components, and as well as the total wind farm generation. Several learning machines are set up to fulfill a multi-step probabilistic forecast. Case study shows the effectiveness of the proposed method by continuous 7 200 times forecasting tests for a given actual wind farm.