Thermal storage systems were originally designed to shift on-peak cooling production to off-peak cooling production to reduce on-peak electricity demand. Recently, however, the reduction of both on- and off-peak demands is becoming an exceedingly important issue. Reduction of on- and off-peak demands can also extend the life span and defer or eliminate the replacement of power transformers due to potential shortage of building power capacity caused by anticipated equipment load increases. Next day daily average electricity demand is a critical set point to operate chillers and associated pumps at the appropriate time. For this paper, a mathematical analysis of the annual daily average cooling of a building was conducted, and three real-time building load forecasting models were developed: a first-order autoregressive model, a random walk model, and a linear regression model. A comparison of results shows that the random walk model provides the best forecast. A complete control algorithm integrated with forecast model for a chiller plant including chillers, thermal storage system and pumping systems was developed to verify the feasibility of applying this algorithm in the building automation system. Application results are introduced in this paper as well.

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