Short-term prediction of wind farm output using the recurrent quadratic volterra model

This paper presents a way to use the recurrent quadratic Volterra system to forecast the wind power output. The recurrent quadratic Volterra system is a second-order polynomial equation that uses the output data as feedback recursively. The Volterra system is extracted from the weights of the Recurrent Neural Network. During this process, three innovative techniques are used. In order to make Volterra kernels from the combination of weights, the activation function is approximated to the high-order polynomial function by using the Lagrangian interpolation. Furthermore, the memory of the Volterra system is also identified using the Partial Autocorrelation Function. After building the Volterra system, the 15 and 30-minutes ahead of wind power output is forecasted with confidence intervals at the 95% confidence level. The confidence interval is calculated using the multi-linear regression techniques. The stability of the recurrent Volterra system is also considered by the heuristic method.