Advancement in the application of neural networks for short-term load forecasting

An improved neural network approach is proposed to produce short-term electric load forecasts. A new strategy, suitable for selecting the training cases for the neural network, is presented. This strategy uses a minimum distance measurement to identify the appropriate historical patterns of load and temperature readings to be used to estimate the network weights. This strategy has the advantage of circumventing the problem of holidays and drastic changes in weather patterns, which make the most recent observations unlikely candidates for training the network. In addition, an improved neural network algorithm is proposed. This algorithm includes a combination of linear and nonlinear terms which map past load and temperature inputs to the load forecast output. The new search strategy and algorithm demonstrate improved accuracy over other methods when tested using two years of utility data. In addition to reporting the summary statistics of average and standard deviation of absolute percentage error, an alternate method using a cumulative distribution plot for presenting load forecasting results is demonstrated.