基于多元非线性回归法的商场空调负荷预测
摘要：
提出了一种基于多元非线性回归的商场空调负荷预测快速建模方法，采用实际用能系数描述不同时刻商场空调的负荷特性，建立了空调负荷预测模型，在提高预测建模速度的同时很大程度上避免了由于训练样本不完备而导致的预测精度波动。利用广州某商场空调负荷的实测数据进行了仿真实验，得到空调负荷与其影响因素之间的多元非线性拟合方程。揭示各种因素对空调负荷的影响规律，仿真结果验证了该方法的有效性和可行性。
Abstract：
Proposes a cooling load prediction modeling method based on multivariate nonlinear regression for shopping malls. Describes the cooling load characteristics of the shopping malls at different time using actual energy consumption coefficient. Establishes a prediction model and increases the prediction modeling speed while avoids the fluctuation of predicted values accuracy due to incomplete training samples. Carries out a simulation experiment with the measured cooling load data of a shopping mall in Guangzhou. Obtains a multivariate nonlinear fitting equation between cooling load and its influencing factors. Reveals the influence of various factors on cooling load, and the simulation results demonstrate the effectiveness and feasibility of this method.
Keywords:shopping mall, air conditioning, load prediction, energy consumption coefficient, multivariate nonlinear regression, BP neural network, support vector regression