Improved Wind Power Forecasting with ARIMA Models

摘要

The introduction of large amounts of wind power into the electricity system raises potential reliability issues for the grid due to the intermittent nature of wind power. Wind power cannot be scheduled in advance like conventional generation units and thus forecasts of the wind power that will be produced in future hours are used to schedule the amount of wind power available. Any improvements in wind power forecasting have the potential to reduce the amount of reserves necessary in systems with significant amounts of wind power, and eventually lower the cost of electricity in such systems. In this work we examine the ability of statistical time series analysis tools, namely autoregressive integrative moving average (ARIMA) models, to forecast future wind power output from historical data. A systematic approach to determine the best values for the assortment of variables associated with the models, such as training period length and model orders, has been developed and applied. The ability of the models to outperform a standard forecasting benchmark has been examined at a number of different forecast period lengths. The application of the tools to total power output of the many wind farms that may be present within the territory of a single independent system operator is studied. Finally, a case study involving wind farm data from Ontario, Canada is used to show how the improvements that these statistical techniques offer may be beneficial for the independent system operator.