Short term load forecasting using wavelet augmented non-linear autoregressive neural networks: A single customer level perspective

Short term Load Forecasting (STLF) has become an important feature of the `smart grid' concept. Though there has been considerable efforts, especially in the aggregated load aspect, luck of a clear-cut recommendation on single method of forecasting due to the high irregularity of load patterns and variation of load characteristics of different consumer types has made it still a topic that needs further study. In this study, an approach composed of wavelet transform and autoregressive neural networks is devised and tested to prove an improved forecast for single commercial type customer. The method proposes breaking down the load data into an approximate and detail components through wavelet decomposition that will be treated and forecasted independently. A unique step of determining the optimal feedback delays for each target parameters through autocorrelation analysis is adopted. A cross correlation analysis is employed to decide on the usage of external input parameters and hence choose either of the NARnet or NARXnet models. The individual forecasts are later merged trough wavelet reconstruction to give the load forecast. The technique was tested using practical load and weather data. The method showed high effectiveness compared to NAR and NARX networks as well as SVM and ANFIS techniques.