Hybrid model based on wavelet transform and ARIMA for short-term electricity price forecasting

In order to forecast the next 24 hour's electricity price,this paper proposed a hybrid model based on wavelet transform and autoregressive integrated moving average( ARIMA) for short-term electricity price forecasting. According to whether considering the influence of demands,it used ARIMA to forecast the time series depcomposed by wavelet transform. It proposed an electricity price anomalies detect and process algorithm to handle the condition where price changed drastically. The numerial example based on the historical data of the Australian national electricity market,New South Wales,in the year 2012,shows that the hybrid model,not considering the influence of demands,got a more precise result than the ARIMA model; electricity price anomalies detect and process algorithm can process the electricity price anomalies precisely and improve the predict accuracy.