Solar Transposition Modeling via Deep Neural Networks With Sky Images

作者：Benjamin G. Pierce, Jennifer L. Braid, Joshua S. Stein, Jim Augustyn, Daniel Riley · 发表于：IEEE Journal of Photovoltaics · 年份：2021 · DOI：10.1109/jphotov.2021.3120508 · 被引用次数：11 · 研究领域：Solar Radiation and Photovoltaics、Photovoltaic System Optimization Techniques、Optical Wireless Communication Technologies

This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180$^{\circ }$arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs anN-dimensional vector, whereNis the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. These profiles may be used to perform near-term prediction of angular irradiance, w...