论文标题
使用基于模式相似的方法对每月电力需求的整体预测
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods
论文作者
论文摘要
这项工作介绍了使用基于模式相似性的预测方法（PSFM）对每月电力需求进行的整体预测。本研究中应用的PSFM包括$ K $ - 最初的邻居模型，模糊邻域模型，内核回归模型和通用回归神经网络。 PSFM的整体部分是使用时间序列序列模式的时间序列表示。模式表示通过过滤趋势和均等差异来确保输入和输出数据统一。创建了两种类型的合奏：异质和同质。前者由不同类型的基本模型组成，而后者由单型基本模型组成。使用五种策略来控制同质方法的多样化成员。使用不同的训练数据，不同的特征子集，随机中断的输入和输出变量以及随机破坏的模型参数生成多样性。一个经验说明应用了合奏模型以及单个PSFM，以与35个欧洲国家的每月电力需求预测进行比较。
This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs). PSFMs applied in this study include $k$-nearest neighbor model, fuzzy neighborhood model, kernel regression model, and general regression neural network. An integral part of PSFMs is a time series representation using patterns of time series sequences. Pattern representation ensures the input and output data unification through filtering a trend and equalizing variance. Two types of ensembles are created: heterogeneous and homogeneous. The former consists of different type base models, while the latter consists of a single-type base model. Five strategies are used for controlling a diversity of members in a homogeneous approach. The diversity is generated using different subsets of training data, different subsets of features, randomly disrupted input and output variables, and randomly disrupted model parameters. An empirical illustration applies the ensemble models as well as individual PSFMs for comparison to the monthly electricity demand forecasting for 35 European countries.
