scholarly journals Incorporating equivalent temperature in building cooling load prediction

2021 ◽  
Author(s):  
Zixuan Wang ◽  
Yuguo Li ◽  
Jiyun Song ◽  
Kai Wang ◽  
Pak Wai Chan
Keyword(s):  
Cooling Load ◽  
Equivalent Temperature ◽  
Load Prediction ◽  
Building Cooling

Building Cooling Load Forecasting Based on Support Vector Machines with Simulated Annealing

2010 ◽  
Vol 108-111 ◽  
pp. 1003-1008
Author(s):  
Xue Mei Li ◽  
Li Xing Ding ◽  
Jin Hu Lǔ ◽  
lan Lan Li
Keyword(s):  
Simulated Annealing ◽  
Support Vector Machines ◽  
Support Vector ◽  
Cooling Load ◽  
Load Prediction ◽  
Promising Alternative ◽  
Vector Machines ◽  
Accurate Forecast ◽  
Electricity Load ◽  
Building Cooling

Accurate forecasting of building cooling load has been one of the most important issues in the electricity industry. Recently, along with energy-saving optimal control, accurate forecast of electricity load has received increasing attention. Because of the general nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting electricity load. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. In order to improve time efficiency of prediction, a new hourly cooling load prediction model and method based on Support Vector Machine in this paper. Moreover, simulated annealing (SA) algorithms were employed to choose the parameters of a SVM model. Subsequently, examples of cooling load data from Guangzhou were used to illustrate the proposed SVM-SA model. A comparison of the performance between SVM optimized by Particle Swarm Optimization (SVM-PSO) and SVM-SA is carried out. Experiments results demonstrate that SVM-SA can achieve better accuracy and generalization than the SVM-PSO. Consequently, the SVM-SA model provides a promising alternative for forecasting building load.

The use of occupancy space electrical power demand in building cooling load prediction

2012 ◽  
Vol 55 ◽  
pp. 151-163 ◽  
Author(s):  
M.C. Leung ◽  
Norman C.F. Tse ◽  
L.L. Lai ◽  
T.T. Chow
Keyword(s):  
Electrical Power ◽  
Cooling Load ◽  
Power Demand ◽  
Load Prediction ◽  
Building Cooling

Weighted LS-SVM Method for Building Cooling Load Prediction

2010 ◽  
Vol 121-122 ◽  
pp. 606-612
Author(s):  
Xue Mei Li ◽  
Jia Shu Chen ◽  
Li Xing Ding
Keyword(s):  
Time Series ◽  
Time Window ◽  
Weighted Least Squares ◽  
Superior Performance ◽  
Support Vector ◽  
Model Parameters ◽  
Cooling Load ◽  
Load Prediction ◽  
On Line ◽  
Building Cooling

A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks, but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for building cooling load prediction. In order to improve the prediction accuracy of cooling load time series, weighted least squares support vector machine regression (WLS-SVM) method for a chaotic cooling load prediction is proposed. In this method, a sliding time window is built and data in the sliding time window are employed to reconstruct the dynamic model. Different weights are assigned to different data in the sliding time window, and the model parameters are refreshed on-line with the rolling of the time window. The results show that the method has more superior performance than other methods like LS-SVM.

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