Effects Of Random Changes Research Articles

Ultra‐short‐term PV power prediction model based on HP‐OVMD and enhanced emotional neural network

Accurate photovoltaic (PV) power prediction plays an increasingly crucial role to maintain the safety and reliability of power grid operation. However, the fluctuation and non-stationarity of PV power make it a challenging task to optimize the accurate results. This paper presents a novel prediction model which is the combination of Hodrick–Prescott (HP) filter, optimized variational mode decomposition (OVMD), and enhanced emotional neural network (EENN). It overcomes the adverse effects of random changes under highly volatile weather conditions. First, the trend component and fluctuation component of PV power are screened through HP filter as the pre-step to alleviate the non-linearity impact of PV power data. Then, OVMD is used to decompose the residual PV power time series into a series of relatively stationary intrinsic modes. Finally, the EENN model optimized by the grey wolf optimization (GWO) is established to predict each subseries, and the prediction results of each subseries are reconstructed to obtain the final predicted results. The numerical results based on actual PV power data show that the prediction accuracy of the proposed model is significantly improved compared with the contrast models, and the proposed model achieves the best accuracy against the OVMD-GWO-EENN, VMD-GWO-EENN, and GWO-EENN models.

Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder

Accurate wind power prediction can improve the safety and reliability of power grid operation. In this study, a novel deep learning network stacked by independent recurrent autoencoder (IRAE) is designed according to the characteristics of ultra-short-term wind power data, hereafter called SIRAE (staked independently recurrent autoencoder). This approach accommodates a sheer volume of data in the smart energy era and overcomes the effects of random changes in the natural environment. First, the original sequence is decomposed into sub-sequences through variational mode decomposition techniques. Second, each IRAE extracts the structural features and deep connections of the data through unsupervised pre-training and finds an appropriate initial state. Finally, all IRAEs are stacked into SIRAE and the parameters of each layer for SIRAE are adjusted through supervised training. The results of two comparative experiments show that SIRAE model outperforms the existing popular models. In the extended application, compared with the persistence model, SIRAE shows 18.46%, 31.16%, 9.06% and 34.24% improvements in terms of root mean square error for March, June, September, and December. Therefore, SIRAE is an appropriate tool for ultra-short-term wind power prediction on account of its effective and stable forecasting performance.

Special Issue Editorial: Emergent Collective Behavior: From Fish Schools to Bacterial Colonies

Active systems, that is, systems driven internally by self-propelled individual units, are ubiquitous in nature. They span awide range of length and time scales, from schools of fish and flocks of birds to suspensions of motile bacteria and assemblages of subcellular extracts, and exhibit rich emergent dynamics, typically at a scale much larger than the scale of the individual unit. Their emergent behavior is the topic of extensive research, spread across several scientific disciplines, and using diverse theoretical, computational and experimental tools. The goal of this special issue is to offer select samples of research articles that demonstrate the diversity and versatility of this exciting field. These articles are not intended to provide a comprehensive review of this exciting area of research or to focus on a specific approach and model system. The model systems considered here range from fish swimming to propulsion of microscopic organisms. The coordinated motion in fish schools is addressed in two papers. The paper of Mwaffo, Anderson and Porfiri studies computationally the effects of random changes in the individuals’ orientation on the overall coordination of fish schools in the context of two rule-based models. The first is based on the classical Vicsek model, where each individual averages the orientation of its geographically proximal neighbors, and the second is a metric-free vectorial network model, where neighbors are selected randomly, independent of the group’s geometric configuration. They find that, in both

A Study of Reactive Power Requirements of Traditional and Restructured Power System

Due to importance of requirements of reactive power in a power system, by employing probabilistic load flow with Monte Carlo method we can gain the exact value of requirement reactive power of system in a traditional and a restructured power system. In this paper the main object is the examination of effect of random changes in load, generation and price on all requirement reactive power of system as well as the amount of reactive power of each bus. Effective parameters in reactive power distribution are determined and an effective probable model for each of parameters is presented. The simulation results are used by network management for reactive power and voltage control service contracts. The results of analysis in two environments (traditional and restructured power system) are shown. The Monte Carlo method and MATLAB software is used, and studies are done on 30 buses IEEE system that is modeled by MATPOWER software.

A Friday 13th risk assessment of failure of ultraviolet irradiation for potable water in turbulent flow

Ultraviolet (UV) irradiation for potable water is an important alternative to widespread disinfection methods such as chlorine. Failure of UV irradiation to reduce levels of viable contaminants can lead to enduring health effects, with or without fatalities. Here a new risk assessment of failure of UV irradiation for potable water in turbulent flow in a series annular-reactor is presented using the Friday 13th (Fr 13) methodology of Davey and co-workers (Food Control 29(1), 248–254, 2013). The aim was to demonstrate the effects of random changes in UV parameters on plant failure. Failure is defined as unexpected levels of survival of Escherichia coli, a species of faecal bacteria widely used as an indicator for health risk. The assessment is based on a unit-operations model of UV irradiation together with extensive experimental data of Ye (2007). A failure factor (p) is defined in terms of the design reduction and actual reduction in viable E. coli contaminants. UV irradiation is simulated using a refined (Latin Hypercube) Monte Carlo (r-MC) sampling. Illustrative results show 16% of apparent successful operations, over the long term, can fail to achieve the design reduction in viable E. coli of 10−4.35 due to random effects. The analysis is shown to be an advance on current risk assessments because it produces all possible practical UV outcomes. Implications of Fr 13 methodology for practical re-design and targeted physical changes to UV plant for improved reliability and safety is discussed.

Association between reflux and symptoms during ambulatory reflux monitoring: pros and cons of existing methods

Ambulatory reflux monitoring provides information on the quantity of gastroesophageal reflux and, if symptoms are registered during the study, provides insights into the relation between symptoms and reflux. Several symptom-reflux association analysis indices have been developed to express the relationship between onset of symptom and reflux in a numerical way. One of these indices is the binomial symptom index, introduced by Ghillebert et al. in 1990. The binomial symptom index can be used to study the relationship between symptoms and acid reflux. In this issue of Neurogastroenterology and Motility Barriga-Rivera et al. describe the use of Monte Carlo simulations to investigate the effect of random changes in reflux rates and symptom occurrences on binomial symptom index outcome. Their analysis shows that this index can be used to measure the strength of reflux symptom association. Performing symptom-reflux association analysis is important as the outcome has been shown to be reproducible and can be used as a predictor to response to acid-suppressive treatment. Despite some limitations, use of symptom-reflux association analysis is a valuable clinical tool that can provide unique insights into the cause of a patient's symptoms.

Parameters affecting reproducibility in capillary electrophoresis.

It is well known that poor quantitative reproducibility substantially limits the practical implementation of capillary electrophoresis (CE) separations in chemical analysis. The principal sources of variance in observed peak areas are irreproducible flow rate, which influences on-column detector response, and inconsistent injection volume or amount. An overview of studies by researchers to address the reproducibility issue will be presented. In addition, current efforts in our laboratory to assess sources of quantitative variance for separations of dansylated amino acids using an automated CE system are presented and related when appropriate to the body of existing knowledge on this important topic. A comparison of different injection methods (hydrostatic vs. electrokinetic) and approaches (e.g., high vs. low pressure), the effect of random changes in electroosmotic flow (EOF) due to air bubbles in the CE capillary, and choice of certain peak integration parameters in terms of peak area reproducibility are presented. Under optimum conditions relative standard deviation (RSD) values in raw peak area are typically 2.0%. With nonoptimum conditions (e.g., with air bubbles in capillary), RSD values can substantially degrade. However, normalizing with retention times, internal standards, or observed electrophoretic current produces RSD values in a range of 1.4-2.3%.

Ecotypic adaptation in Austrocedrus chilensis in cuticular hydrocarbon composition

An homologous series of hydrocarbons ranging from C21 to C37 (was detected in foliar cuticular waxes of Austrocedrus chilensis (D. Don) Pic.‐Ser. & Bizz., by gas chromatography. Qualitatively, the chromatograms were similar for all individuals sampled from 29 natural populations in Chile and Argentina, showing C33 and C35 alkanes to be dominant. Quantitatively, the chromatograms varied significantly among populations. Multivariate analyses showed that arid zone populations from mediterranean Chile and the Patagonian steppe were different from one another and were distinct from mesic populations close to the Valdivian rainforest vegetation zone. Mediterranean Chilean populations were characterized by higher concentrations of the longer carbon‐chain alkanes, but retained equal amounts of shorter‐chain homologues as the mesic populations. A greater variation in chain lengths and higher concentrations of longer carbon chains in the mediterranean populations is consistent with a model for the adaptation to reduced cuticular permeability. Partial Mantel matrix tests revealed significant climatic and inter‐population distance effects with taxonomic distances based on multivariate and univariate hydrocarbon data. Annual rainfall was overall the most significant factor, particularly in regressions with the shorter‐chain hydrocarbons. Annual mean temperature was most significant for the longer‐chain hydrocarbons. This suggests, on the one hand, ecogenic adaptation to both temperature and precipitation of cuticular hydrocarbon composition, and, on the other hand, a weaker, but important effect of gene flow in determining hydrocarbon composition in this species. The northernmost populations at San Felipe and San Gabriel were the most distinctive. This could result from the effects of random changes in allele frequencies and/or to founder effects in isolated and small populations. These populations show some East‐Andean affinities presumably due to historic migration patterns.

The Hotelling model under uncertainty: A note

The classical Hotelling model was traditionally applied to a static world with perfect certainty. The present note, on the contrary, examines the effects of random changes in the parameters of the Hotelling model on the optimal decisions regarding the depletion of an exhaustible resource. Under the assumption of risk neutrality of the mining firm, we have found that precautionary motives work not only with respect to the randomness of the reserves, which were shown in previous studies, but also with respect to all other parameters that are involved in the determination of the equilibrium path of prices.

THE EFFECT OF VARYING THE INSPIRATORY FLOW RATE ON THE VENTILATION OF INFANTS WITH RESPIRATORY DISTRESS SYNDROME (RDS)

The effect of random changes in inspiratory flow rate was evaluated in 15 infants with RDS on assisted ventilation with a time-cycled continual flow ventilator. Other ventilator parameters were not altered. Ten infants were on intermittent mandatory ventilation (IMV); five were controlled under curare paralysis. At a flow of 12 liters/minute delivered tidal volumes and peak tracheal pressures were higher in all but one infant and respiratory rates were increased in those breathing spontaneously. In spite of this, PaCO2 was elevated and VA decreased in 12 of 15 patients. No changes in cardiac output or right to left shunting were observed. The increase in VD at the higher inspiratory flow rate may be related to increased volume of the anatomic dead space due to over-distention of the proximal airway.

Anisotropy in Ionospheric Diffraction and its Effect on Drift Measurement

An attempt was made by the author a few years ago to combine the geometrical approach of Putter and the statistical approach of Briggs and Spencer to the problem of ionosphere drift measurement. The combination gave rise to a method of wind measurement which was nearly as simple as that of Putter and yet vigorous enough to eliminate the effect of random changes in the ground diffraction pattern. However, the method could not be proved valid when the ground pattern was statistically anisotropic. The present paper develops a method involving the use of five statistical parameters and a few simple algebraic formulae, which takes into account the effects of random motion as well as anisotropy. The intuitive clarity of the geometrical picture is, however, unfortunately lost.