Mining of electricity prices in energy markets using a hybrid linear ARMA and nonlinear functional link neural network trained by evolutionary unscented H-infinity filter

摘要

This paper presents a hybrid autoregressive moving average (ARMA) and a nonlinear functional link neural network for electricity price forecasting in an Energy market. The functional neural block helps to introduce nonlinearity by expanding the input space to higher dimensional space through a basis function without using any hidden layers like MLP structure. Unlike the conventional functional link artificial neural network (FLANN), the input layer comprises the inputs and tangent hyperbolic functions of the linear combination of the inputs known as the basis functions. The proposed hybrid neural network is trained by an unscented H-infinity filter to provide an accurate forecasting of day ahead electricity prices. The noise covariance parameters of the unscented H-infinity fdter are further optimised with an adaptive differential evolution strategy. The studies on PJM, Spanish and Australian energy market markets exhibit excellent forecasting results over different seasonal horizons for one day ahead of time.