Research on short-term and ultra-short-term cooling load prediction models for office buildings

2017 ◽  
Vol 154 ◽  
pp. 254-267 ◽  
Author(s):  
Yan Ding ◽  
Qiang Zhang ◽  
Tianhao Yuan
Keyword(s):  
Prediction Models ◽  
Office Buildings ◽  
Cooling Load ◽  
Load Prediction ◽  
Short Term

A Comparison of NARX and BP Neural Network in Short-Term Building Cooling Load Prediction

2014 ◽  
Vol 513-517 ◽  
pp. 1545-1548 ◽  
Author(s):  
Yan Li Xu ◽  
Hong Xun Chen ◽  
Wang Guo ◽  
Qiu Yu Zhu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Prediction Errors ◽  
Cooling Load ◽  
Climate Data ◽  
Load Prediction ◽  
Short Term ◽  
Building Cooling

A comparison of nonlinear autoregression with exogenous inputs (NARX) neural network and back-propagation (BP) neural network in short-term prediction of building cooling load is presented in this dissertation. Both predictive models have been applied in a group of commercial buildings and analysis of prediction errors has been highlighted. Training and testing data for both prediction models have been generated from DeST (Designers Simulation Toolkits) with climate data of Shanghai. The simulation results indicate that NARX method can achieve better accuracy and generalization ability than traditional method of BP neural network. This work provides a key support in smooth and optimizing control in air-conditioning system.

Development and evaluation of cooling load prediction models for a factory workshop

2019 ◽  
Vol 230 ◽  
pp. 622-633 ◽  
Author(s):  
Qiang Zhang ◽  
Zhe Tian ◽  
Yan Ding ◽  
Yakai Lu ◽  
Jide Niu
Keyword(s):  
Prediction Models ◽  
Cooling Load ◽  
Load Prediction

scholarly journals Electrical Load Prediction for Short Term using Support Vector Machine Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 111-116
Keyword(s):  
Support Vector Machine ◽  
Prediction Models ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Electrical Load ◽  
Load Prediction ◽  
Short Term ◽  
Training Time ◽  
Short Term Load Forecasting

The electrical load prediction during an interval of a week or a day plays an important role for scheduling and controlling operations of any power system. The techniques which are presently being used and are used for Short Term Load Forecasting (STLF) by utilizing various prediction models try for the performance improvement. The prediction models and their performance mainly depend upon the training data and its quality. The different forecasting approaches using Support Vector Machine (SVM) depending on several performance indices has been discussed. The accuracy of the forecasting approaches is measured by Mean Absolute Error (MAE), Root Mean Square Error (RMSE), prediction speed and training time. The approach with least RMSE reveals as the best among the SVM methods for short term load forecasting.

scholarly journals An improved ARX model for hourly cooling load prediction of office buildings in different climates

2018 ◽  
Vol 175 ◽  
pp. 03027 ◽  
Author(s):  
Chengliang Fan ◽  
Yundan Liao ◽  
Yunfei Ding
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Climatic Conditions ◽  
Office Buildings ◽  
Coefficient Of Determination ◽  
Cooling Load ◽  
Load Prediction ◽  
Piecewise Linearization ◽  
Arx Model ◽  
Analysis Methods

An attempt was made to develop an improved autoregressive with exogenous (ARX) model for office buildings cooling load prediction in five major climates of China. The cooling load prediction methods can be arranged into three categories: regression analysis, energy simulation, and artificial intelligence. Among them, the regression analysis methods using regression models are much simple and practical for real applications. However, traditional regression models are often helpless to manage multiparameter dynamic changes, making it not accurate as the other two categories. Many of the existing cooling load prediction studies use piecewise linearization to manage nonlinearity. To improve the prediction accuracy of regression analysis methods, higher order and interaction terms are included in improved ARX based on traditional ARX model. The improved ARX model consists of eight variables, with eleven coefficients accessed at a time. For applications and evaluations, an office building in major cities within each climatic zone was selected as a representation. These cities were Harbin, Beijing, Nanjing, Kunming and Guangzhou respectively. The coefficient of determination R2 is greater than 0.9 in five cities. The prediction results show that the improved ARX model can adapt to different climatic conditions, including those nonlinearity cases.

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