能耗预测导向的建筑能耗异常数据识别与修复

建筑能耗异常数据处理是对建筑能耗进行准确预测的前提。为有效处理建筑能耗异常数据,利用基于机器学习的K-means和KNN(K-nearest neighbor)算法,对上海某酒店建筑2017年7月的逐时能耗数据进行了异常识别和修复。通过建立长短期记忆网络模型,利用处理后的能耗数据预测了该建筑2017年8月首周的逐时能耗数据。预测结果表明,提出的建筑能耗异常数据识别与修复方法能准确识别并修复建筑能耗异常数据,从而显著提高后续能耗预测的效果。

Abnormal Data Identifying and Repairing for Building Energy Consumption guided by Energy Consumption Prediction

Abnormal data processing is the premise of accurate prediction of building energy consumption. In order to effectively deal with the abnormal data of building energy consumption, this paper uses the K-means and KNN algorithms based on machine learning to identify and repair the hourly energy consumption data for July 2017 of a hotel building in Shanghai. By establishing a long and short term memory neural network model, this paper uses the processed energy consumption data to predict the hourly energy consumption data for the first week of August 2017. The prediction results show that the method proposed in this paper can accurately identify and repair abnormal building energy consumption data, thus significantly improving the effect of subsequent energy consumption prediction.