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

Cooling load is a heat value of cold water used for air conditioning in a district heating and cooling system. Cooling load prediction in a district heating and cooling system is one of the key techniques for smooth and economical operation. In this article, cooling load prediction in such a district heating and cooling system is considered. Unfortunately, since actual cooling load data usually involve measurement noises, outliers, and missing data for several reasons, a prediction method considering the effect of the outliers and missing data is desirable. In this article, a new prediction method using a simplified robust filter to improve a numerical stability problem of a robust filter and a three-layered neural network, is proposed. Applications of the proposed method and some other methods to actual cooling load data in a district heating and cooling system involving outliers and missing data show the usefulness of the proposed method.