Comparison of short-term weather forecasting models for model predictive control.(Report): An article from: HVAC & R Research
Book Details
Author(s)Anthony R. Florita, Gregor P. Henze
ISBN / ASINB003S0WYIA
ISBN-13978B003S0WYI9
MarketplaceGermany 🇩🇪
Description
This digital document is an article from HVAC & R Research, published by American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. on September 1, 2009. The length of the article is 8480 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.
From the author: Model predictive control applied to commercial buildings requires short-term weather forecasts to optimally adjust setpoints in a supervisory control environment. Review of the literature reveals that many researchers are convinced that nonlinear forecasting models based on neural networks (NNs) provide superior performance over traditional time series analysis. This paper seeks to identify the complexity required for short-term weather forecasting in the context of a model predictive control environment. Moving average models with various enhancements and (NN) models are used to predict weather variables seasonally in numerous geographic locations. Their performance is statistically assessed using coefficient-of-variation and mean bias error values. When used in a cyclical two-stage model predictive control process of policy planning followed by execution, the results show that even the most complicated nonlinear autoregressive neural network with exogenous input does not appear to warrant the additional efforts in forecasting model development and training in comparison to the simpler MA models.
Citation Details
Title: Comparison of short-term weather forecasting models for model predictive control.(Report)
Author: Anthony R. Florita
Publication:HVAC & R Research (Magazine/Journal)
Date: September 1, 2009
Publisher: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.
Volume: 15 Issue: 5 Page: 835(19)
Article Type: Report
Distributed by Gale, a part of Cengage Learning
From the author: Model predictive control applied to commercial buildings requires short-term weather forecasts to optimally adjust setpoints in a supervisory control environment. Review of the literature reveals that many researchers are convinced that nonlinear forecasting models based on neural networks (NNs) provide superior performance over traditional time series analysis. This paper seeks to identify the complexity required for short-term weather forecasting in the context of a model predictive control environment. Moving average models with various enhancements and (NN) models are used to predict weather variables seasonally in numerous geographic locations. Their performance is statistically assessed using coefficient-of-variation and mean bias error values. When used in a cyclical two-stage model predictive control process of policy planning followed by execution, the results show that even the most complicated nonlinear autoregressive neural network with exogenous input does not appear to warrant the additional efforts in forecasting model development and training in comparison to the simpler MA models.
Citation Details
Title: Comparison of short-term weather forecasting models for model predictive control.(Report)
Author: Anthony R. Florita
Publication:HVAC & R Research (Magazine/Journal)
Date: September 1, 2009
Publisher: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.
Volume: 15 Issue: 5 Page: 835(19)
Article Type: Report
Distributed by Gale, a part of Cengage Learning
