Analyzing big time series data in solar engineering using features and PCA

In solar engineering, we encounter big time series data such as the satellite-derived irradiance data and string-level measurements from a utility-scale photovoltaic (PV) system. While storing and hosting big data are certainly possible using today’s data storage technology, it is challenging to effectively and efficiently visualize and analyze the data. We consider a data analytics algorithm to mitigate some of these challenges in this work. The algorithm computes a set of generic and/or application-specific features to characterize the time series, and subsequently uses principal component analysis to project these features onto a two-dimensional space. As each time series can be represented by features, it can be treated as a single data point in the feature space, allowing many operations to become more amenable. Three applications are discussed within the overall framework, namely (1) the PV system type identification, (2) monitoring network design, and (3) anomalous string detection. The proposed framework can be easily translated to many other solar engineer applications

Statistical modeling, parameter estimation and measurement planning for PV degradation

Photovoltaics (PV) degradation is a key consideration during PV performance evaluation. Accurately predicting power delivery over the course of lifetime of PV is vital to manufacturers and system owners. With many systems exceeding 20 years of operation worldwide, degradation rates have been reported abundantly in the recent years. PV degradation is a complex function of a variety of factors, including but not limited to climate, manufacturer, technology and installation skill. As a result, it is difficult to determine degradation rate by analytical modeling; it has to be measured. As one set of degradation measurements based on a single sample cannot represent the population nor be used to estimate the true degradation of a particular PV technology, repeated measures through multiple samples are essential. In this chapter, linear mixed effects model (LMM) is introduced to analyze longitudinal degradation data. The framework herein introduced aims to address three issues: 1) how to model the difference in degradation observed in PV modules/systems of a same technology that are installed at a shared location; 2) how to estimate the degradation rate and quantiles based on the data; and 3) how to effectively and efficiently plan degradation measurements

Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some challenges, e.g., deficiency of labeled data, inadequate utilization of data information and so on. In this paper, a complex-valued generative adversarial network (GAN) is proposed for the first time to address these issues. The complex number form of model complies with the physical mechanism of PolSAR data and in favor of utilizing and retaining amplitude and phase information of PolSAR data. GAN architecture and semi-supervised learning are combined to handle deficiency of labeled data. GAN expands training data and semi-supervised learning is used to train network with generated, labeled and unlabeled data. Experimental results on two benchmark data sets show that our model outperforms existing state-of-the-art models, especially for conditions with fewer labeled data

Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations

The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a large-scale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. Our model significantly advances the state-of-the-art results on step classification (+2.8% / +3.3% on COIN / EPIC-Kitchens) and step forecasting (+7.4% on COIN). Moreover, our model attains promising results in zero-shot inference for step classification and forecasting, as well as in predicting diverse and plausible steps for incomplete procedures. Our code is available at https://github.com/facebookresearch/ProcedureVRL.Comment: Accepted to CVPR 202