In this paper an investigation is presented to determine the effect of forecasting uncertainty on the cost savings performance of a predictive optimal controller for thermal energy storage (TES) systems. Specifically, this investigation analyzed four uncertainty models to predict future values for cooling loads, weather variables, and electrical rates. The uncertainty models considered are unbiased Gaussian noise, correlated Gaussian noise, unbiased uniform noise, and biased uniform noise. The results of the analysis summarized in this paper show that the predictive optimal controller is robust and does not require high levels of accuracy in predicting the cooling loads and the real-time pricing (RTP) rates.
Units: I-P
Citation: Symposium, ASHRAE Transactions, vol. 105, pt. 2, Seattle 1999
Product Details
- Published:
- 1999
- Number of Pages:
- 9
- File Size:
- 1 file , 230 KB
- Product Code(s):
- D-7431
