论文标题
本田智能房屋的房屋电力消耗的预测方法比较
Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home
论文作者
论文摘要
建筑物的电力消耗构成了该市能源消耗的主要部分。电力消耗预测可以开发房屋能源管理系统，从而导致未来的可持续性房屋设计和总能源消耗的减少。建筑物中的能源性能受环境温度，湿度和各种电气设备等许多因素的影响。因此，多元预测方法是首选而不是单变量。选择了本田智能家庭数据集，以比较三种方法，以最大程度地减少预测错误，MAE和RMSE：人工神经网络，支持向量回归和基于模糊规则的基于模糊规则的系统，以通过在不同时间计算的多变量数据集上为每种方法构造许多模型。比较表明，SVR比替代方案是一种优越的方法。
The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector Regression, and Fuzzy Rule-Based Systems for Regression by constructing many models for each method on a multivariate data set in different time terms. The comparison shows that SVR is a superior method over the alternatives.
