基于改进决策树算法的日特征负荷预测研究

针对决策树ID3算法的缺陷,提出了属性-值对的两次信息增益优化算法,该算法是ID3的改进算法,它能克服ID3算法在选取属性进行扩展时易偏向属性值多的属性及ID3算法属性间相关性考虑较少的缺点;通过对熵阈值的设定,采用预剪枝技术,又能部分克服ID3算法对噪音敏感的不足.该算法可用以生成日特征负荷决策树预测模型.该模型结合预测日的气象、星期等信息,可进行日特征负荷的预测.采用等深直方图分析思想,可对负荷变化率数据离散化,将层次聚类和信息熵相结合,对气象数据离散化.数据预处理后,通过属性-值对的2次信息增益优化算法生成负荷预测决策树模型,在给出预测日气象及星期信息后可对特征负荷进行预测,预测结果能够满足并超过负荷预测实用化标准的要求并具有较高的预测精度.如果将日24点或96点负荷及相应影响因素数据均用该算法进行模型训练,形成24个或96个预测模型,则可进行日24点或96点负荷预测.

STUDY ON THE DAILY CHARACTERISTIC LOAD FORECASTING BASED ON THE OPTIMIZIED ALGORITHM OF DECISION TREE

To eliminate limitation of the ID3 algorithm, an optimized algorithm for two-level information gain of attribute-value pairs is presented to establish the forecasting model of daily- -characteristic-load decision tree. The algorithm has improved primitive ID3 algorithm in many aspects, it can prevent expansion biasing the attribute which has multi values. By this improved algorithm, the relationship of attributes can be considered well. Through setting threshold value sensitization of noise can be reduced. Daily characteristic load forecasting can be implemented by this model which associates day-forecasted information such as weather, week and so on. The analytic method of histogram is adopted to discretize the data of the load rate-of-change and the data of weather combined hierarchical clustering and discretization based on entropy; after the data is pre-processed, the forecasting model of load decision tree is established by the optimized algorithm for two-level information gain of attribute-value pairs and the characteristic load can be forecasted by giving the information of date-forecasted weather and week. The forecasting results meet even exceed the requirements of utility and demonstrate high-accuracy of the proposed model. If use the 24 or 96 load and its corresponding influent factors to train, 24 or 96 forecasting models will be formed. Then 24 or 96 load can be forecasted by these models.