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The incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather conditions. The dependence of demand on weather conditions may change with time during a day. Therefore, the time stamped weather information is essential. In this paper, a multi-layer moving window approach is proposed to incorporate the significant weather variables, which are selected using Pearson and Spearman correlation techniques. The multi-layer moving window approach allows the layers to adjust their size to accommodate the weather variables based on their significance, which creates more flexibility and adaptability thereby improving the overall performance of the proposed approach. Furthermore, a recursive model is developed to forecast the demand in multi-step ahead. An electricity demand data for the state of New South Wales, Australia are acquired from the Australian Energy Market Operator and the associated res ....

Australian Energy Market Operator , New South Wales , Autoregressive Ar Model , Road Forecasting , Ulti Layer Moving Window , Pearson Correlation , Spearman Correlation ,