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

Probing dark particles indirectly at the CEPC

When dark matter candidate and its parent particles are nearly degenerate, it would be difficult to probe them at the Large Hadron Collider directly. We propose to explore their quantum loop effects at the CEPC through the golden channel process $e^+e^-\to \mu^+\mu^-$. We use a renormalizable toy model consisting of a new scalar and a fermion to describe new physics beyond the Standard Model. The new scalar and fermion are general multiplets of the $SU(2)_L\times U(1)_Y$ symmetry, and couple to the muon lepton through Yukawa interaction. We calculate their loop contributions to anomalous $\gamma\mu^+\mu^-$ and $Z\mu^+\mu^-$ couplings which can be applied to many new physics models. The prospects of their effects at the CEPC are also examined assuming a 0.002 accuracy in the cross section measurement

A linear method to extract diode model parameters of solar panels from a single I–V curve

The I-V characteristic curve is very important for solar cells/modules being a direct indicator of performance. But the reverse derivation of the diode model parameters from the I-V curve is a big challenge due to the strong nonlinear relationship between the model parameters. It seems impossible to solve such a nonlinear problem accurately using linear identification methods, which is proved wrong in this paper. By changing the viewpoint from conventional static curve fitting to dynamic system identification, the integral-based linear least square identification method is proposed to extract all diode model parameters simultaneously from a single I-V curve. No iterative searching or approximation is required in the proposed method. Examples illustrating the accuracy and effectiveness of the proposed method, as compared to the existing approaches, are presented in this paper. The possibility of real-time monitoring of model parameters versus environmental factors (irradiance and/or temperatures) is also discussed

PV panel modeling and identification

In this chapter, the modelling techniques of PV panels from I-V characteristics are discussed. At the beginning, a necessary review on the various methods are presented, where difficulties in mathematics, drawbacks in accuracy, and challenges in implementation are highlighted. Next, a novel approach based on linear system identification is demonstrated in detail. Other than the prevailing methods of using approximation (analytical methods), iterative searching (classical optimization), or soft computing (artificial intelligence), the proposed method regards the PV diode model as the equivalent output of a dynamic system, so the diode model parameters can be linked to the transfer function coefficients of the same dynamic system. In this way, the problem of solving PV model parameters is equivalently converted to system identification in control theory, which can be perfectly solved by a simple integral-based linear least square method. Graphical meanings of the proposed method are illustrated to help readers understand the underlying principles. As compared to other methods, the proposed one has the following benefits: 1) unique solution; 2) no iterative or global searching; 3) easy to implement (linear least square); 4) accuracy; 5) extendable to multi-diode models. The effectiveness of the proposed method has been verified by indoor and outdoor PV module testing results. In addition, possible applications of the proposed method are discussed like online PV monitoring and diagnostics, noncontact measurement of POA irradiance and cell temperature, fast model identification for satellite PV panels, and etc

A Robust Quantum Random Access Memory

A "bucket brigade" architecture for a quantum random memory of $N=2^n$ memory cells needs $n(n+5)/2$ times of quantum manipulation on control circuit nodes per memory call. Here we propose a scheme, in which only average $n/2$ times manipulation is required to accomplish a memory call. This scheme may significantly decrease the time spent on a memory call and the average overall error rate per memory call. A physical implementation scheme for storing an arbitrary state in a selected memory cell followed by reading it out is discussed.Comment: 5 pages, 3 figure

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

Controlling Entanglement Dynamics by Choosing Appropriate Ratio between Cavity-Fiber Coupling and Atom-Cavity Coupling

The entanglement characteristics including the so-called sudden death effect between two identical two-level atoms trapped in two separate cavities connected by an optical fiber are studied. The results show that the time evolution of entanglement is sensitive not only to the degree of entanglement of the initial state but also to the ratio between cavity-fiber coupling () and atom-cavity coupling (). This means that the entanglement dynamics can be controlled by choosing specific v and g.Comment: 14pages, 3figures, conferenc

Forecasting of global horizontal irradiance by exponential smoothing, using decompositions

Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. ETS (exponential smoothing) has received extensive attention in the recent years since the invention of its state space formulation. In this work, we combine these models with knowledge based heuristic time series decomposition methods to improve the forecasting accuracy and computational efficiency.<p></p> In particular, three decomposition methods are proposed. The first method implements an additive seasonal-trend decomposition as a preprocessing technique prior to ETS. This can reduce the state space thus improve the computational efficiency. The second method decomposes the GHI (global horizontal irradiance) time series into a direct component and a diffuse component. These two components are used as forecasting model inputs separately; and their corresponding results are recombined via the closure equation to obtain the GHI forecasts. In the third method, the time series of the cloud cover index is considered. ETS is applied to the cloud cover time series to obtain the cloud cover forecast thus the forecast GHI through polynomial regressions. The results show that the third method performs the best among three methods and all proposed methods outperform the persistence models.<p></p&gt

Non-Contact Measurement of POA Irradiance and Cell Temperature for PV Systems

This paper presents a non-contact measurement of irradiance on plane of array (POA) and cell temperature for PV systems. The idea is motivated from the diode model of PV, where POA irradiance and cell temperature are proportional to the photocurrent and modified ideality factor, respectively. Based on the recent progress of diode model identification, the photocurrent and modified ideality factor can be linearly determined from I-V characteristics, which makes it feasible to develop a non-contact measurement approach for POA irradiance and cell temperature, i.e., both of them will be derived completely from the diode mode parameter identification without the need of any sensors. The calibration of the proportional factors is done from the indoor module flash test and then applied to outdoor module testbed to show the accuracy and effectiveness of the proposed method