【摘要】 供热预测对实现供热节能有重要意义,然而由于供热系统具有非线性、复杂性和大时滞性等特点,预测存在一定难度,为了解决供热预测中的时延建模、求解和温度预测问题,基于供热系统的传热物理规律建立优化模型,提出最小二乘意义上的时延求解剪枝算法,并进行温度预测。将供热系统的时延考虑在内的模型符合热传递的物理规律,剪枝降低了时延求解的计算复杂度。通过仿真实验对比与分析证明,与梯度下降最小二乘回归、支持向量机、最小二乘支持向量机和BP神经网络等模型相比,本模型具有较低的平均相对误差和均方误差,且模型简单,计算便捷。更多还原

【Abstract】 Heating prediction is essential for energy saving. However, it is difficult to some extent because the central heating system(CHS) is nonlinear with large-scaled delay and other characteristics.Based on physical laws of the CHS, a novel model combining pruning algorithm with the least mean square is proposed to solve the problem of delay and predict the temperature. The model with the delay taking into consideration conforms to the law of thermal transmission. The pruning algorithm lowers the computational complexity. Simulations using gradient descent least squared regression(LSR), support vector machine(SVM), least-squared support vector machine(LS-SVM) and BP neural network are put forward to evaluate the model and the results show that the model has low average relative error and mean squared error; and the model is simple and easy to calculate.更多还原