Short-term Wind Speed Forecasting Using Multi-Source Multivariate RNN-LSTMs

With the increasing percentage penetration of wind energy, accurate Wind Speed/Power Forecasting (WSF/WPF) has become a crucial tool for power trading and electricity grid operations. The physical and statistical methods used to forecast mostly rely on meteorological information, site specific data and historical data from wind farms. Most of the existing works are limited to utilize single input data source and a prediction variable. However, some gap in using multiple data sources and multiple prediction variables as input has been identified to substantiate and enhance forecast accuracy of these models. In this paper, a multi-source and multivariate surveillance approach has been conducted using time series methods and machine learning algorithms. Firstly, the proposed model uses multiple sources to understand the deciding prediction variables. Secondly, an analogy among time series Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Networks (RNNs) with Long Short-term Memory units (LSTMs) is used. Although, the prediction models using single data source are economical for forecast providers and wind power producers considering multiple sources can certainly improve the forecasting accuracy thus reducing wind curtailments and imbalance penalties. A comparative approach has been made using data sources obtained for Gulf of Khambhat, a south to north penetration of the Arabian Sea on the western shelf of India. The obtained results show that the forecasting accuracy can be improved by considering multiple sources of input data.