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Most commercial buildings have been dedicated HVAC systems to meet their comfort needs. In larger commercial buildings, the control of this equipment is achieved through use of computerized control systems, which allow the flexibility of running the building under different control strategies. Most control systems vary the building temperatures using a strategy called night-setup control. Under night-setup control, the air temperatures are in the middle of the comfort range and the cooling equipment is usually active during daytime hours. During the night and weekends, a high temperature setpoint is sent out by the control system and the cooling system is typically inactive during these periods. However, recently there has been an increased emphasis on developing dynamic building control strategies, which attempt to minimize the total cooling costs of a building and reduce peak cooling demand. Accurate building modeling tools are needed for predicting the building thermal loads under different control strategies. The work described in this report involved development and evaluation of models using data from the Energy Resource Station (ERS) at the Iowa Energy Center (IEC). Models trained with data from the ERS were used to estimate peak cooling load reduction associated with different demand-limiting and precooling strategies. The IEC is a relatively lightweight structure that is representative of small commercial buildings. Demand reduction results for this building would be smaller than those possible for larger commercial buildings. The specific work tasks that provided the basis for the results described in this report are summarized as follows: A. Develop forward simulation model for the ERS building B. Develop inverse simulation model for the ERS building C. Evaluate demand-limiting control strategies

Most commercial buildings have been dedicated HVAC systems to meet their comfort needs. In larger commercial buildings, the control of this equipment is achieved through use of computerized control systems, which allow the flexibility of running the building under different control strategies. Most control systems vary the building temperatures using a strategy called night-setup control. Under night-setup control, the air temperatures are in the middle of the comfort range and the cooling equipment is usually active during daytime hours. During the night and weekends, a high temperature setpoint is sent out by the control system and the cooling system is typically inactive during these periods. However, recently there has been an increased emphasis on developing dynamic building control strategies, which attempt to minimize the total cooling costs of a building and reduce peak cooling demand. Accurate building modeling tools are needed for predicting the building thermal loads under different control strategies. The work described in this report involved development and evaluation of models using data from the Energy Resource Station (ERS) at the Iowa Energy Center (IEC). Models trained with data from the ERS were used to estimate peak cooling load reduction associated with different demand-limiting and precooling strategies. The IEC is a relatively lightweight structure that is representative of small commercial buildings. Demand reduction results for this building would be smaller than those possible for larger commercial buildings. The specific work tasks that provided the basis for the results described in this report are summarized as follows: A. Develop forward simulation model for the ERS building B. Develop inverse simulation model for the ERS building C. Evaluate demand-limiting control strategies