the Belady conference room (sensors 452 and 453) from February 12 to April 10, 2007;

-   EnergyPlus weather data for Elmira, NY starting March 1 (typical meteorological year) and a three-week warm-up period;

-   no un-modeled disturbances;

-   one-hour time step;

-   system capacity of 8.0kW.

The thermal model is the single-zone building RC network discussed previously. To emphasize the benefit of prediction, we have chosen the weather period to just saturate the control output in typical winter conditions. (These conditions emphasize the need to predict more than one hour out; we could have chosen January and increased the system capacity slightly for the same result.)

Choosing $\lambda$\[sub:Choosing\]
----------------------------------

Before we run the simulation, we need to choose the forgetting factor. Without forgetting ($\lambda=1.0$), consistent occupancy patterns allow predictions to asymptotically approach $\Gamma=0$ and $\Gamma=1$, but the ever-lengthening effective history length hinders adaptation and leads to very large prediction error. At the other extreme, high forgetting ($\lambda\ll1.0$) gives a model easily distracted by irregularities that consistently predicts occupancy near $\Gamma=0.5$, which again leads to high prediction error. Intuition suggests that a minimum prediction error should exist between these limits, and indeed this is the case. Figure \[fig:Influence-of-forgetting\] shows the relationship 