Big Data Analysis for PV Applications

With increasing photovoltaic (PV) installations, large amounts of time series data from utility-scale PV systems such as meteorological data and string level measurements are collected [1, 2]. Due to fluctuations in irradiance and temperature, PV data is highly stochastic. Spatio-temporal differences with potential time-lagged correlation are also exhibited, due to the wind directions affecting cloud movements [3]. Coupling these variations with different types of PV systems in terms of power output and wiring configuration, as well as localised PV effects like partial shading and module mismatches, lengthy time series data from solar systems are highly multi-dimensional and challenging to process. In addition, these raw datasets can rarely be used directly due to the possibly high noise and irrelevant information embedded in them. Moreover, it is challenging to operate directly on the raw datasets, especially when it comes to visualizing and analyzing these data. On this point, the Pareto principle, or better-known as the 80/20 rule, commonly applies: researchers and solar engineers often spend most of their time collecting, cleaning, filtering, reducing and formatting the data. In this work, a data analytics algorithm is applied to mitigate some of the complexities and make sense of the large time series data in PV systems. Each time series is treated as an individual entity which can be characterized by a set of generic or application-specific features. This reduces the dimension of the data, i.e., from hundreds of samples in a time series to a few descriptive features. It is is also easier to visualize big time series data in the feature space, as compared to the traditional time series visualization methods, such as the spaghetti plot and horizon plot, which are informative but not very scalable. The time series data is processed to extract features through clustering and identify correspondence between specific measurements and geographical location of the PV systems. This characterisation of the time series data can be used for several PV applications, namely, (1) PV fault identification, (2) PV network design and (3) PV type pre-design for PV installation in locations with different geographical attributes

Effects of system-bath entanglement on the performance of light-harvesting systems: A quantum heat engine perspective

We explore energy transfer in a generic three-level system, which is coupled to three non-equilibrium baths. Built on the concept of quantum heat engine, our three-level model describes non-equilibrium quantum processes including light-harvesting energy transfer, nano-scale heat transfer, photo-induced isomerization, and photovoltaics in double quantum-dots. In the context of light-harvesting, the excitation energy is first pumped up by sunlight, then is transferred via two excited states which are coupled to a phonon bath, and finally decays to the ground state. The efficiency of this process is evaluated by steady state analysis via a polaron-transformed master equation; thus a wide range of the system-phonon coupling strength can be covered. We show that the coupling with the phonon bath not only modifies the steady state, resulting in population inversion, but also introduces a finite steady state coherence which optimizes the energy transfer flux and efficiency. In the strong coupling limit, the steady state coherence disappears and the efficiency approaches the heat engine limit given by Scovil and Schultz-Dubois in Phys. Rew. Lett. 2, 262 (1959).Comment: 10 pages, 8 figures, all comments are welcom

Boise State\u27s Journey to a K-12 Online Teaching Endorsement Program

The key to successful K-12 online learning may rely more on the quality of instruction than the medium used to deliver that instruction (Rice, 2012). The quality of online instruction and teachers\u27 professional development concerning teaching online remains a critical issue, as well as a challenge, in the field of K-12 online teaching (Fisk, 2011). Higher education institutions are beginning to address this issue by providing teachers with K-12 online teaching certificate or endorsement programs. Boise State University (BSU) is one such institution

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

PV Parameter Identification using Reduced I-V Data

In this paper, possibility and accuracy of using reduced I-V data in PV parameter identification are discussed. Based on the linear identification method proposed in [1], six I-V points are used instead of the whole I-V curve to identify the PV parameters. The maximum power point (MPP) is then estimated using the identified I-V and P-V characteristics. Validation is done by using different sets of six points on the I-V curve. Experiment results show that the accurate curve fitting (with low RMSE and MPE) and good estimation of MPP can be achieved

Building an Effective Online Thermodynamics Course for Undergraduate Engineering Students

Online learning does not appear to be the common option when approaching some core engineering courses. However, the growing need for online engineering courses necessitates the development of online courses that can allow for the flexibility and convenience these distance learning experiences can offer, which also can help broaden the participation in engineering education. Thermodynamics is among the most difficult engineering subjects to teach, 1 , 2 especially online, where instructors are unable to demonstrate the overwhelming number of equations and applications as they would in face-to-face lectures. 3 , 4. This paper describes the design and development of an undergraduate online thermodynamics class. It also reports the students’ learning experience with thermodynamics in an online environment, students’ feedback on the online course, and students’ responses as to what worked in this particular online course

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

The Validation of an Instrument for Evaluating the Effectiveness of Professional Development Program on Teaching Online

Attending professional development (PD) on teaching online is becoming popular for teachers in today’s K-12 online education. Due to the unique characteristics of the online instructional environments, surveys become the most feasible approach to evaluate the effectiveness of PD programs. However, there is no validated, open-access instrument available to satisfy the needs. Purpose of this study is to conduct construct validity, content validity, concurrent validity, and reliability tests on an open-access instrument for K–12 PD for online teaching. With the exception of a few items that have minor issues on content and construct validity, results show that the survey is, in general, a valid and reliable instrument. Suggestions and potential applications of the instrument are also discussed

Students’ Online Interaction Styles: Can They Change?

Past studies indicate that students demonstrate different online interaction styles, which consist of the ways or habits students acquire knowledge from computer-mediated discussions (Sutton, 2001). Such interaction styles include the active interaction style (Beaudion, 2002), the vicarious interaction style (Sutton, 2001), and the mixed or balanced-interaction style. The purpose of this exploratory study was to further investigate whether students’ online interaction styles changed during a course utilizing asynchronous computer-mediated discussions; and if so how and why they changed. Results indicate that such changes did take place as 44% of participants adjusted to more active learning styles as the courses progressed. This study has implications for the design of online learning environments, instructor’s role in online courses, and educational tools to facilitate students in adapting to more active interaction styles in computer-mediated learning environments

Duality Symmetry of Quantum Electrodynamics

The duality symmetry between electricity and magnetism hidden in classical Maxwell equations suggests the existence of dual charges, which have usually been interpreted as magnetic charges and have not been observed in experiments. In quantum electrodynamics (QED), both the electric and magnetic fields have been unified into one gauge field $A_{\mu}$, which makes this symmetry inconspicuous. Here, we recheck the duality symmetry of QED by introducing a dual gauge field. Within the framework of gauge-field theory, we show that the electric-magnetic duality symmetry cannot give any new conservation law. By checking charge-charge interaction and specifically the quantum Lorentz force equation, we find that the dual charges are electric charges, not magnetic charges. More importantly, we show that true magnetic charges are not compatible with the gauge-field theory of QED, because the interaction between a magnetic charge and an electric charge can not be mediated by gauge photons.Comment: 5 pages with 4 appendixes, 1 figur