Cooling load prediction in a district heating and cooling system through simplified robust filter and multi-layered neural network

The cooling load is a heat value of the cold water used for air conditioning in a district heating and cooling system. Cooling load prediction in such a system is one of key techniques needed for its smooth and economical operation. Unfortunately, since actual cooling load data usually involves measurement noise, outliers and missing data, for several reasons, a prediction method considering the effect of the outliers and missing data is desirable. In this paper, a new prediction method using a simplified robust filter with improved numerical stability and a three-layered neural network is proposed.