AbstractA technique for forecasting daily peak load in a utility power system is presented. After embedding time series data of daily peak load into a reconstructed state space, a nonlinear mapping is constructed by a local approximation method based on the orthonormal Gram‐Schmidt bases. This method utilizes only the past load data for short‐term prediction of the daily peak load, while many conventional methods make predictions with various kinds of data such as temperature and weather. The quality of prediction by the proposed method is as good as those with other prediction methods. Moreover, the results of short‐term prediction by this method are satisfactory even with data as small as 250 p