Short-term probabilistic building load forecasting based on feature integrated artificial intelligent approach
Short-term probabilistic building load forecasting based on feature integrated artificial intelligent approach
复制标题
DOI：
10.1016/j.epsr.2022.107802
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Ronghui Liu;Teng Chen;Gaiping Sun;S. Muyeen;Shunfu Lin;Yang Mi
中科院分区：
文献类型：
--
作者：
Ronghui Liu;Teng Chen;Gaiping Sun;S. Muyeen;Shunfu Lin;Yang Mi
由于各种因素的影响，导致建筑负荷的不稳定性和波动性，短期建筑负荷预测是一项艰巨的任务。提出一种基于正交最大相关系数（OMCC）特征选择和卷积门递归单元（CGRU）分位数回归的混合短期建筑负荷概率密度预测方法。首先，OMCC选择最优特征集。然后通过拟合Copula模型构造指标变量，确定风险价值（VAR）。然后，将特征选择阶段的数据作为CGRU分位数回归模型的输入，建立负荷预测模型。最后，用核密度估计拟合建筑荷载概率密度分布。使用平均绝对百分比误差（MAPE），均方根误差（RMSE），和平均Arctan绝对百分比误差（MAAPE）的预测性能进行评估。在所有三个建筑物的仿真结果验证了所提出的模型的短期建筑物级概率负荷预测任务的可靠性。
Due to various influential factors that lead to instability and volatility of the building load, short-term building load forecasting is a gruelling task. This paper proposes a hybrid short-term building load probability density forecasting method based on Orthogonal Maximum Correlation Coefficient (OMCC) feature selection and Convolutional Gated Recurrent Unit (CGRU) quantile regression. Firstly, the optimal feature set is selected by OMCC. Then Value-At-Risk (VAR) is determined from fitting Copula model to construct indicator variables. Next, the data from the feature selection stage is used as input to the quantile regression model of CGRU for building load forecasting. Finally, the building load probability density distribution is fitted by kernel density estimation. The forecasting performance is evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Arctan Absolute Percentage Error (MAAPE). Simulation results across all three buildings validate the reliability of the proposed model for the short-term building-level probabilistic load forecasting tasks.