Power Consumption Short Term Forecast Using Signal Auto Regressive Method

Short term forecast of power consumption plays a critical role in optimal exploiting of power system. Economic performance and reliability of a network significantly depends to load forecast's accuracy. This forecast is used for load management and planning for unit commitment (UC) and it has especial complexity due to influence of multiple nonlinear relations between daily periodic variations and load consumption changes. 30-60 minutes forecasts are employed extensively in power distribution network. This study aims to perform short term forecasts by using linear auto regressive (AR) modelling. In this paper, collected data from a 63 kV distribution station is modelled by a aforementioned method. It is demonstrated that AR method can model data properly using auto correlation functions residual error and sum of squares criteria. Since data is non-stationary, performance of auto regressive integrated moving average (ARIMA) is investigated and optimal rank of model and the best data length to perform modelling are presented. Regarding to forecasts, appropriate model's rank for AR and ARIMA are 20 and (1,1,0) respectively. In AR model forecast error is ±10% which is equal to 38.1 and in ARIMA method forecast mse is and equals to 0.7277 and mse value for neural network method is 0.952 that totally ARIMA method shows better performance than the other.