This paper discusses the modeling method of time series with neural network. In order to improve the adaptability of direct multi-step prediction models, this paper proposes a method of combining the temporal differen...This paper discusses the modeling method of time series with neural network. In order to improve the adaptability of direct multi-step prediction models, this paper proposes a method of combining the temporal differences methods with back-propagation algorithm for updating the parameters continuously on the basis of recent data. This method can make the neural network model fit the recent characteristic of the time series as close as possible, therefore improves the prediction accuracy. We built models and made predictions for the sunspot series. The prediction results of adaptive modeling method are better than that of non-adaptive modeling methods.展开更多
A 3-D forecasting model for oil-spill is developed using the finite difference method to numerically solve the shallow water equation. The instantaneous flow distribution in the studied area is calculated and the traj...A 3-D forecasting model for oil-spill is developed using the finite difference method to numerically solve the shallow water equation. The instantaneous flow distribution in the studied area is calculated and the trajectory of the oil-slick centroid is predicted by means of Lagrange’s method. The computed results agree with the observed data well, this shows that this 3-D forecasting model has high accuracy.展开更多
基于系统辨识理论,提出一种新的预测冰蓄冷中央空调平均负荷的外源自回归滑动平均(ARMAX)模型辨识方法.首先根据空调负荷历史数据构建次日平均负荷的ARMAX模型;然后基于不同的室外最高温度建立自适应ARMAX温度区间模型,该模型由几组参...基于系统辨识理论,提出一种新的预测冰蓄冷中央空调平均负荷的外源自回归滑动平均(ARMAX)模型辨识方法.首先根据空调负荷历史数据构建次日平均负荷的ARMAX模型;然后基于不同的室外最高温度建立自适应ARMAX温度区间模型,该模型由几组参数时变的子模型组成,子模型参数由在线回归算法辨识.测试数据表明,所提出的ARMAX温度区间模型与传统的ARMAX模型相比具有较高的预测精度,而且室外最高温度差异越大,效果越明显,可用于冰蓄冷中央空调的优化控制.
Abstract：
A novel ARMAX model identification method for average load forecasting of ice-storage central air conditioning is introduced based on system identification theory. Firstly an ARMAX model for the next day average load is obtained according to the historical data,then an adaptive ARMAX model is proposed based on different outdoor maximum temperature, the temperature interval based ARMAX model is composed of several time-varyiag sub-models whose parameters are identified using an on-line regression algorithm. As a result, the proposed model has good quality in terms of prediction precision in comparison with traditional ARMAX model, besides, the more outdoor maximum temperature difference, the better prediction precision, it can be applied to the optimal control of ice-storage central air conditioning.展开更多
文摘This paper discusses the modeling method of time series with neural network. In order to improve the adaptability of direct multi-step prediction models, this paper proposes a method of combining the temporal differences methods with back-propagation algorithm for updating the parameters continuously on the basis of recent data. This method can make the neural network model fit the recent characteristic of the time series as close as possible, therefore improves the prediction accuracy. We built models and made predictions for the sunspot series. The prediction results of adaptive modeling method are better than that of non-adaptive modeling methods.
文摘A 3-D forecasting model for oil-spill is developed using the finite difference method to numerically solve the shallow water equation. The instantaneous flow distribution in the studied area is calculated and the trajectory of the oil-slick centroid is predicted by means of Lagrange’s method. The computed results agree with the observed data well, this shows that this 3-D forecasting model has high accuracy.
文摘基于系统辨识理论,提出一种新的预测冰蓄冷中央空调平均负荷的外源自回归滑动平均(ARMAX)模型辨识方法.首先根据空调负荷历史数据构建次日平均负荷的ARMAX模型;然后基于不同的室外最高温度建立自适应ARMAX温度区间模型,该模型由几组参数时变的子模型组成,子模型参数由在线回归算法辨识.测试数据表明,所提出的ARMAX温度区间模型与传统的ARMAX模型相比具有较高的预测精度,而且室外最高温度差异越大,效果越明显,可用于冰蓄冷中央空调的优化控制.
Abstract：
A novel ARMAX model identification method for average load forecasting of ice-storage central air conditioning is introduced based on system identification theory. Firstly an ARMAX model for the next day average load is obtained according to the historical data,then an adaptive ARMAX model is proposed based on different outdoor maximum temperature, the temperature interval based ARMAX model is composed of several time-varyiag sub-models whose parameters are identified using an on-line regression algorithm. As a result, the proposed model has good quality in terms of prediction precision in comparison with traditional ARMAX model, besides, the more outdoor maximum temperature difference, the better prediction precision, it can be applied to the optimal control of ice-storage central air conditioning.
