Short-term Photovoltaic Power Forecasting Using Deep Convolutional Networks

Abstract

Uncertain factors such as weather conditions make the photovoltaic power forecasting challenging. Therefore, some advanced deep learning (DL) techniques have been introduced into the field. However, the exploitation of historical data by existing DL-based models is usually limited to one-dimensional power arrays, and the information describing power varies over time is also ignored. Differently, this work proposes a model based on a deep convolutional network (DCN), which attempts to improve forecast performance by combining feature learning and multi-dimensional arrays. To match the DCN paradigm along with digging the power information indepth, we use the historical daily data to build a power tensor. We train and test the proposed model using data sets from the real world, Self-evaluation and comparison with several state-of-the-art models demonstrate the superiority of our proposal.