Short term load forecasting using a self-supervised adaptive neural network

We developed a self-supervised adaptive neural network to perform short term load forecasts (STLF) for a large power system covering a wide service area with several heavy load centers. We used the self-supervised network to extract correlational features from temperature and load data. In using data from the calendar year 1993 as a test case, we found a 0.90 percent error for hour-ahead forecasting and 1.92 percent error for day-ahead forecasting. These levels of error compare favorably with those obtained by other techniques. The algorithm ran in a couple of minutes on a PC containing an Intel Pentium -120 MHz CPU. Since the algorithm included searching the historical database, training the network, and actually performing the forecasts, this approach provides a real-time, portable, and adaptable STLF.