Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment

This paper presents the development of an autoregressive based time varying (ARTV) model to forecast electricity demand in a short-term period. The ARTV model is developed based on an autoregressive model by allowing its coefficients to be updated at pre-set time intervals. The updated coefficients help to enhance the relationships between electricity demand and its own historical values, and accordingly improve the performance of the model. In addition, a representative data adjustment procedure including a similar-day-replacement technique and a data-shifting algorithm is proposed in this paper to cultivate the historical demand data. These techniques help cleanse the raw data by mitigating the abnormal data points when daylight saving and holiday occur. Consequently, the robustness of the model is significantly enhanced, and accordingly the overall forecasting accuracy of the model is considerably improved. A case study has been reported in this paper by acquiring the relevant data for the state of New South Wales, Australia. The results show that the proposed model outperforms conventional seasonal autoregressive and neural network models in short term electricity demand forecasting.