Performance Evaluation of Deep Recurrent Neural Networks Architectures: Application to PV Power Forecasting

Abstract

Smart grid systems require an accurate energy prediction from renewable sources to ensure high sustainability and power quality. For PV plants, a precise estimation of the generated PV power is crucial for the reduction of the production/demand unbalance. This essential need comes from the high variability of weather parameters during the PV electricity generation. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent neural networks proved their high efficiency in forecasting applications. Thus, this paper proposes a comprehensive evaluation of the LSTM and GRU techniques for PV power estimation in the medium/long horizon. The evaluation is based on a fair assessment of the aforementioned architectures for one week and more than three months (98 days) periods.