Wind power ramp events ordinal prediction using minimum complexity echo state networks

Abstract

Renewable energy is the fastest growing source of energy in the last years. In Europe, wind energy is currently the energy source with the highest growing rate and the second largest production capacity, after gas energy. There are some problems that difficult the integration of wind energy into the electric network. These include wind power ramp events, which are sudden differences (increases or decreases) of wind speed in short periods of times. These wind ramps can damage the turbines in the wind farm, increasing the maintenance costs. Currently, the best way to deal with this problem is to predict wind ramps beforehand, in such way that the turbines can be stopped before their occurrence, avoiding any possible damages. In order to perform this prediction, models that take advantage of the temporal information are often used. One of the most well-known models in this sense are recurrent neural networks. In this work, we consider a type of recurrent neural networks which is known as Echo State Networks (ESNs) and has demonstrated good performance when predicting time series. Specifically, we propose to use the Minimum Complexity ESNs in order to approach a wind ramp prediction problem at three wind farms located in the Spanish geography. We compare three different network architectures, depending on how we arrange the connections of the input layer, the reservoir and the output layer. From the results, a single reservoir for wind speed with delay line reservoir and feedback connections is shown to provide the best performance.