A new state estimation method for power cooling integrated energy system

Integrated Electric-Cold System(IECS) has received wide attention because it can realize cascade utilization of energy and improve the efficiency of energy utilization. To obtain high-precision IECS data, you need to implement an IECS Real-time and accurate state awareness, i.e. the need to build state estimates for IECS. A knowledge-guided deep neural network(DNN) state estimation method is presented for IECS with large amounts of historical data. The IECS measurement-state equation is used to estimate the cooling load, which provides the cooling load parameters for the state estimation of the cooling system. Facing the massive historical data of IECS, the knowledge method is applied to guide the data drive, and the relevant historical data is filtered to train the DNN model for real-time state estimation of each node. The accuracy and efficiency of the proposed state estimation algorithm are verified by an example analysis in an IECS composed of an air conditioning system.