VARMA model parameterization using MLLE approach for intraday wind power forecasting application

Abstract

AbstractAccurate intraday wind power forecasting (WPF) is required by system operators to mitigate challenges arising from small‐scale grid integration of intermittent wind power. This low‐level integration of wind energy into the system urges quality deterministic forecast. Multivariate statistical models considering spatiotemporal correlations are commonly used for such WPF. However, prudent parameterization is a necessity to improve deterministic forecasts even in smaller datasets. This article proposes a computationally efficient vector autoregressive moving average (VARMA) based intraday WPF model that uses the novel maximum log‐likelihood estimation (MLLE) approach to estimate the best‐fit model parameters. MLLE approach reduces the spatiotemporal fitting error by approximately 15% with computational time deduction by more than 25%. Furthermore, an advanced accuracy in parameter estimation (AIPE) technique supports the MLLE approach to obtain the optimal training window length for computing distribution parameters with narrow confidence intervals. Proposed centralized forecast model is implemented on a group of Wind Farms (WFs) in SE Australia. Pre‐processing of wind power generation data is ensured using the Yeo–Johnson transformation and stationarity concept. Proposed model is accurate and computationally efficient as compared to benchmark models in terms of accuracy and computational time. An appreciated forecast quality is achieved using the proposed model having only 0.20 MW and 0.29 MW mean normalized RMSE for 3 and 10 WFs studies, respectively. Possible applicability of the proposed model includes an overall improvement in intraday decision‐making of wind energy producers and system operators.