改进NRS与ELM相结合在住宅需求预测中的应用

针对住宅需求预测受到不同方面因素的影响且具有非线性特征等问题,本文在原始邻域粗糙集(NRS)的基础上进行改进,并与极限学习机(ELM)相结合来进行预测.首先改进算法(MNRS)解决了原始NRS无法在不同条件属性之间设定最佳邻域值的问题,根据不同条件属性的邻域半径和标准差构建邻域关系矩阵;然后在输出属性重要度排序时引入Pearson相关系数,克服了条件属性之间的影响,获得最小冗余属性的约简集构成住宅需求预测指标体系;最后将构建的住宅需求指标体系输入极限学习机模型,得到准确的预测值.实验结果表明:MNRS-ELM预测模型不仅有效降低了运算复杂度,而且能够获得更高的预测精度.

Application of Improved NRS Combined with ELM in Residential Demand Prediction

Residential demand forecasting is affected by multiple factors and is non-linear. To address this issue, the study modifies the original neighborhood rough set (NRS) and then combines it with extreme learning machines (ELMs) to forecast residential demands. Specifically, the modified NRS (MNRS) algorithm constructs a neighborhood relationship matrix based on the neighborhood radii and standard deviations of different conditional attributes, thereby overcoming the failure of the original NRS algorithm to set the optimal neighborhood value for different conditional attributes. Then, the Pearson correlation coefficient is introduced into output attribute importance ranking to overcome the influence among conditional attributes, and the minimal redundant attribute-based reduction set is obtained to serve as the indicator system for residential demand forecasting. Finally, the residential demand indicator system is input into the ELM model to output an accurate forecasted value. Experimental results show that the MNRS-ELM forecasting model not only effectively reduces the operational complexity but also achieves higher prediction accuracy.