Deep learning for pattern recognition of photovoltaic energy generation

Abstract

With the rapid growth in computational complexities of statistical pattern recognition of photovoltaic (PV) energy measurements, the need for new data-driven models has emerged. Among machine learning frameworks, deep neural networks yield promising solutions due to their high generalization capacity, low estimation bias, and ease of implementation. In this study, first, the discriminative deep models including autoencoders, Long Short-Term Memory networks, and Rectified Linear Units are introduced as a class of mathematical models that directly estimate the future solar energy given historical measurements. In addition, Convolutional Neural Networks are explored to show the merit of spatiotemporal pattern recognition to increase PV generation prediction accuracy. Then, we explore deep sparse coding algorithms for behind-the-meter (BTM) energy disaggregation (ED) with PV generation in residential and commercial customers. We show how the combination of deep discriminative models with dictionary learning leads to a promising performance in decomposing the net electricity demand into the solar generation and demand. Finally, deep generative modeling for BTM ED with PV generation is proposed which learns powerful probability distribution functions from PV time series rather than crisp features captured by the classic discriminative deep learning. Our numerical results on real-world ED datasets of residential units show significant improvements in ED accuracy compared to the state-of-the-art sparse coding approaches.