基于DKDE与改进mRMR特征选择的短期光伏出力预测

随着光伏发电装机容量的增长,其在能源消费中的占比不断提升,准确预测光伏发电功率对电力系统发展规划和调度运行均具有重要意义。目前,针对光伏预测特征选择的研究比较少,不合理的特征选择往往导致信息丢失,气象参数与出力间的映射关系难以有效挖掘,最终导致预测精度偏低。因此,文中提出一种基于改进互信息计算与改进最大相关最小冗余(mRMR)的光伏预测特征选择方法。针对连续随机变量相关性互信息难以直接计算的问题,基于扩散核密度估计(DKDE)理论,提出一种依据概率密度的区间划分方法并应用于变量离散化,以提高互信息对实际有限数据集的表征能力。然后,对传统mRMR的增量搜索过程进行了改进,提出一种可并行筛选多个特征子集的改进mRMR算法,并针对各特征子集分别采用XGBoost算法构建气象信息与光伏功率的预测模型。最后,通过实际光伏电站测量数据验证了所提方法的有效性和准确性。

Seasonal Short-Term Load Forecasting for Power Systems Based onModal Decomposition and Feature-FusionMulti-Algorithm Hybrid Neural NetworkModel

To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.

Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection

Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty,multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed.Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection.Firstly, the variational mode decomposition(VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy(m RMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network(BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine(LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy.