Abstract
Successful integration of renewable energy sources like wind power into smart grids largely depends on accurate prediction of power from these intermittent sources. Production of wind power cannot be controlled as the wind speed can vary based on weather conditions. Accurate prediction of wind power can assist smart grid that intelligently decides on the usage of alternative power sources based on demand forecast. Time series wind speed data are normally used for wind power prediction. In this paper, we have investigated the usage of a set of secondary features obtained using deep learning for wind power prediction. Deep learning is a special form on neural network that is capable of capturing the structural properties of time series data in terms of a set of numeric features. More precisely, we have designed a two-stage autoencoder (a particular type of deep learning) and incorporated the structural features into a prediction framework. Using the structural features, we have achieved as high as 12.63% better prediction accuracy than traditionally used statistical features.
Highlights
Renewable energy sources like wind are becoming integral part of modern power systems
Stage 1 stacked autoencoder (SAE) features perform better than stage 2 SAE features on 68 out of 70 stations using both linear regression (LR) and SVM
This suggests that stage 1 SAE features capture the structure of the underlying time series better than stage 2 SAE features
Summary
Renewable energy sources like wind are becoming integral part of modern power systems. As reported in IRENA (2017), renewable energy accounts for around 22% of global power generation. This share is expected to double in the 15 years. This is due to the rapid growth of variable renewable energy from sources like wind and solar photovoltaic (IRENA 2017). Smart grid engineering is the key for a beneficial use of widespread energy resources. This fusion of smart grid and renewable energy enables the efficient use of such sources
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