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Ice storage has been widely used in modern buildings, because of the prominent energy saving effect for air conditioning systems. However, sampling periods of energy management and control system (EMCS) are long in some projects. This leads to data deficiencies at the time of making and melting ice, and influents the output of prediction model ulteriorly. This paper analyzes the engineering data of current percentage and chilled water temperature with the data variation trend in the actual project, and designs an improved prediction model based on the E-Elman neural network. The result shows that the improved model has enhanced the facticity of predicted outputs significantly, and solves the problems about data mutation and local optimum. Meanwhile, the improved model has superior performance.