A hybrid model based on STL with simple exponential smoothing and ARMA for wind forecast in a Brazilian nuclear power plant site

In the event of a nuclear accident with release of radioactive effluents into the environment, the Environmental Control System (ECS) at the Central Nuclear Almirante ' Alvaro Alberto (CNAAA) nuclear complex estimates the movement of the radioactive plume produced and provides forecast for one and two hours ahead. The prediction is based on the current accident and atmospheric data collected from meteorological stations spread across the site. In this way, the ECS estimates the spatial dose and dose rates distribution at ground level. The one- and two-hour forecast made by the ECS use the naive method, which repeats the last known value. Its results are satisfactory if the next value to be collected does not differ substantially from the last known value. However, observing real data, acquired in loco, this fact does not occur in practice, which can make the forecast considerably erroneous. The purpose of this work is to investigate new approaches to improve ECS forecasting method, making it more accurate in real situations. To this end, the Seasonal and Trend decomposition using Loess (STL) was used together with the Simple Exponential Smoothing (SES) method and the Autoregressive Moving Average (ARMA) model to forecast weather parameters at the Angra site. The STL method was chosen since it has a robust mathematical decomposition method, capable of satisfactorily dealing with time series that have a high frequency of acquisition, the so-called intraday series. This model uses the Loess method, which is a smoothing technique based on locally adjusted regressions. The SES method and ARMA model are the techniques that most adequately fit the trend and remainder components generated by the STL decomposition, respectively. In the scope of this work, the method is applied to forecast wind speed measurements made at one of the meteorological towers used by the ECS. Using the hybrid model developed, an increase the of 101.39% in the Mean Absolute Percentage Error (MAPE), was achieved when compared to the ECS forecast method.