办公建筑运行能耗的混沌时间序列复合预测

针对办公建筑已有的能耗预测方法中未能考虑到能耗数据的混沌变化特性,提出了一种基于混沌时间序列的办公建筑运行能耗预测方法.对研究对象的时间序列进行相空间重构,判断其具备混沌特性,建立混沌理论和支持向量回归的组合模型进行训练,采用Markov链消除组合模型由于参数传递产生的累积误差,得到最终预测结果.为了验证算法的有效性,以西安某办公建筑的能耗监测数据为例进行实例分析,并与非线性自回归神经网络、支持向量回归等其他预测方法进行对比.实验结果表明,经过Markov修正后的混沌时间序列组合模型预测精度显著提高,预测效果优于其他方法,且更符合办公建筑能耗的变化规律,为节能优化提供有效的数据支撑.

Chaotic time series composite prediction of office building energy consumption

The existing energy consumption prediction methods for office buildings fail to take into account the chaotic change characteristics of energy consumption data. In this paper, a method of energy consumption prediction for office buildings based on chaotic time series was proposed. The method first reconstructed the phase space of the time series of the research object, and judged that whether it had chaotic characteristics. Then the combination model of chaos theory was established and applied in vector regression for training. Finally, Markov chain was used to eliminate the cumulative errors caused by parameter transfer of the combination model, and the final prediction result was obtained. In order to verify the effectiveness of the algorithm, the energy consumption monitoring data of an office building in Xi''an was taken as an example for analysis. The proposed method was compared with other prediction methods, such as nonlinear autoregressive neural network and support vector regression. The experimental results show that the prediction accuracy of the chaotic time series combination model modified by Markov was significantly improved, the prediction result was better than those of other models and more consistent with the change law of energy consumption of office buildings, providing effective data support for energy conservation optimization.