Abstract
Cooling load is the heat value of chilled water used for air-conditioning in a district heating and cooling system. Cooling load prediction is indispensable for operating a district heating and cooling system. For several reasons, the real data sets usually involve som outliers and missing data. In this paper, we focus on cooling load prediction problems in a district heating and cooling system. For dealing with such real data sets, a simplified robust filter which improves a numerical stability problem of a robust filter is proposed for filtering. Then RBF-NARMA model, which is a nonlinear autoregressive moving-average(NARMA)model through a radial basis function network(RBFN), is presented for time series prediction. The effectiveness of the prediction method proposed in this paper is demonstrated by applying both the proposed method and the recurrent NARMA model based method proposed by J.T.Connor et al.
