An adaptive algorithm for short-term multinode load forecasting in power systems

An online adaptive escalator lattice structure for orthogonalization of multiple-channel signals is used to predict load demands among loading nodes in a power system by an autoregressive multiple-channel mode. Since the escalator outputs are white and also uncorrelated with each channel or node, the parameters of the algorithm are updated adaptively using scalar operations. Because matrix or vector operations are not required in the updating procedures, the convergence speed is insensitive to the ratio of the largest to the smallest eigenvalues of the loads´ covariance matrix. Thus the prediction filter has a faster convergence rate than common matrix-oriented gradient adaptive filters. Computer simulation shows that this algorithm has a faster convergence rate and better numerical properties in the adaptive process. This is very attractive for multinode load forecasting in a large power system, where each load model varies with time and has different statistical characteristics, or where loads are nonstationary and the ratio of eigenvalues in the load covariance matrix is large