A robust auto encoder-gated recurrent unit (AE-GRU) based deep learning approach for short term solar power forecasting

Abstract

The increasing presence of solar power plants shows its potency as one of the key renewable energy resource to fulfill energy needs of the community. This increasing presence can be effectively utilized by using efficient prediction of solar power plant output for stable grid operation. The availability of processors with high computational capability and access to baseline data provide edge to deep learning models for prediction of solar power plants. In this work, sequence to sequence auto-encoder (AE) and Gated Recurrent unit (GRU) based hybrid deep learning approach is developed, which further advances other recent works offered in solar power forecasting. The AE extracts the internal relationship of input solar data, minimizes the reconstruction error and taps the important feature correlation. Furthermore, GRU exploits the time dependencies of the data. For comprehensive evaluation, different deep learning models and their hybrid forms are analyzed for different prediction durations i.e. 24 h, 48 h, and 15 days prediction. The analysis compares models on three major performance indices, i.e., mean square error, mean absolute error, and R²errors with the same hyperparameters for this time series prediction. The outcome ascertains that the proposed AE-GRU based deep learning model performs better than other deep learning models.