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

Very short term irradiance forecasting using the lasso

We find an application of the lasso (least absolute shrinkage and selection operator) in sub-5-min solar irradiance forecasting using a monitoring network. Lasso is a variable shrinkage and selection method for linear regression. In addition to the sum of squares error minimization, it considers the sum of ℓ1-norms of the regression coefficients as penalty. This bias–variance trade-off very often leads to better predictions.<p></p> One second irradiance time series data are collected using a dense monitoring network in Oahu, Hawaii. As clouds propagate over the network, highly correlated lagged time series can be observed among station pairs. Lasso is used to automatically shrink and select the most appropriate lagged time series for regression. Since only lagged time series are used as predictors, the regression provides true out-of-sample forecasts. It is found that the proposed model outperforms univariate time series models and ordinary least squares regression significantly, especially when training data are few and predictors are many. Very short-term irradiance forecasting is useful in managing the variability within a central PV power plant.<p></p&gt

A feedback-driven bubble G24.136+00.436: a possible site of triggered star formation

We present a multi-wavelength study of the IR bubble G24.136+00.436. The J=1-0 observations of $^{12}$CO, $^{13}$CO and C$^{18}$O were carried out with the Purple Mountain Observatory 13.7 m telescope. Molecular gas with a velocity of 94.8 km s$^{-1}$ is found prominently in the southeast of the bubble, shaping as a shell with a total mass of $\sim2\times10^{4}$ $M_{\odot}$. It is likely assembled during the expansion of the bubble. The expanding shell consists of six dense cores. Their dense (a few of $10^{3}$ cm$^{-3}$) and massive (a few of $10^{3}$ $M_{\odot}$) characteristics coupled with the broad linewidths ($>$ 2.5 km s$^{-1}$) suggest they are promising sites of forming high-mass stars or clusters. This could be further consolidated by the detection of compact HII regions in Cores A and E. We tentatively identified and classified 63 candidate YSOs based on the \emph{Spitzer} and UKIDSS data. They are found to be dominantly distributed in regions with strong emission of molecular gas, indicative of active star formation especially in the shell. The HII region inside the bubble is mainly ionized by a $\sim$O8V star(s), of the dynamical age $\sim$1.6 Myr. The enhanced number of candidate YSOs and secondary star formation in the shell as well as time scales involved, indicate a possible scenario of triggering star formation, signified by the "collect and collapse" process.Comment: 13 pages, 10 figures, 4 tables, accepted by Ap

Roles of TGFβ and FGF signals during growth and differentiation of mouse lens epithelial cell in vitro.

Transforming growth factor β (TGFβ) and fibroblast growth factor (FGF) signaling pathways play important roles in the proliferation and differentiation of lens epithelial cells (LECs) during development. Low dosage bFGF promotes cell proliferation while high dosage induces differentiation. TGFβ signaling regulates LEC proliferation and differentiation as well, but also promotes epithelial-mesenchymal transitions that lead to cataracts. Thus far, it has been difficult to recapitulate the features of germinative LECs in vitro. Here, we have established a LEC culture protocol that uses SB431542 (SB) compound to inhibit TGFβ/Smad activation, and found that SB treatment promoted mouse LEC proliferation, maintained LECs' morphology and distinct markers including N-cadherin, c-Maf, Prox1, and αA-, αB-, and β-crystallins. In contrast, low-dosage bFGF was unable to sustain those markers and, combined with SB, altered LECs' morphology and β-crystallin expression. We further found that Matrigel substrate coatings greatly increased cell proliferation and uniquely affected β-crystallin expression. Cultured LECs retained the ability to differentiate into γ-crystallin-positive lentoids by high-dosage bFGF treatment. Thus, a suppression of TGFβ/Smad signaling in vitro is critical to maintaining characteristic features of mouse LECs, especially expression of the key transcription factors c-Maf and Prox1