Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

A Comparison of the Flow Structures and Losses Within Vaned and Vaneless Stators for Radial Turbines

This paper details the numerical analysis of different vaned and vaneless radial inflow turbine stators. Selected results are presented from a test program carried out to determine performance differences between the radial turbines with vaned stators and vaneless volutes under the same operating conditions. A commercial computational fluid dynamics code was used to develop numerical models of each of the turbine configurations, which were validated using the experimental results. From the numerical models, areas of loss generation in the different stators were identified and compared, and the stator losses were quantified. Predictions showed the vaneless turbine stators to incur lower losses than the corresponding vaned stator at matching operating conditions, in line with the trends in measured performance.. Flow conditions at rotor inlet were studied and validated with internal static pressure measurements so as to judge the levels of circumferential nonuniformity for each stator design. In each case, the vaneless volutes were found to deliver a higher level of uniformity in the rotor inlet pressure field. [DOI: 10.1115/1.2988493]

Exoplanets or Dynamic Atmospheres? The Radial Velocity and Line Shape Variations of 51 Pegasi and tau Bootis

The stars 51 Pegasi and tau Bootis show radial velocity variations that have been interpreted as resulting from companions with roughly Jovian mass and orbital periods of a few days. Gray and Gray & Hatzes reported that the radial velocity signal of 51 Peg is synchronous with variations in the shape of the line lambda 6253 Fe I; thus, they argue that the velocity signal arises not from a companion of planetary mass but from dynamic processes in the atmosphere of the star, possibly nonradial pulsations. Here we seek confirming evidence for line shape or strength variations in both 51 Peg and tau Boo, using R = 50,000 observations taken with the Advanced Fiber Optic Echelle. Because of our relatively low spectral resolution, we compare our observations with Gray's line bisector data by fitting observed line profiles to an expansion in terms of orthogonal (Hermite) functions. To obtain an accurate comparison, we model the emergent line profiles from rotating and pulsating stars, taking the instrumental point-spread function into account. We describe this modeling process in detail. We find no evidence for line profile or strength variations at the radial velocity period in either 51 Peg or in tau Boo. For 51 Peg, our upper limit for line shape variations with 4.23 day periodicity is small enough to exclude with 10 sigma confidence the bisector curvature signal reported by Gray & Hatzes; the bisector span and relative line depth signals reported by Gray are also not seen, but in this case with marginal (2 sigma ) confidence. We cannot, however, exclude pulsations as the source of 51 Peg's radial velocity variation because our models imply that line shape variations associated with pulsations should be much smaller than those computed by Gray & Hatzes; these smaller signals are below the detection limits both for Gray & Hatzes's data and for our own. tau Boo's large radial velocity amplitude and v sin i make it easier to test for pulsations in this star. Again we find no evidence for periodic line shape changes, at a level that rules out pulsations as the source of the radial velocity variability. We conclude that the planet hypothesis remains the most likely explanation for the existing data.

Stellar jitter from variable gravitational redshift: implications for radial velocity confirmation of habitable exoplanets

A variation of gravitational redshift, arising from stellar radius fluctuations, will introduce astrophysical noise into radial velocity measurements by shifting the centroid of the observed spectral lines. Shifting the centroid does not necessarily introduce line asymmetries. This is fundamentally different from other types of stellar jitter so far identified, which do result from line asymmetries. Furthermore, only a very small change in stellar radius, ˜0.01 per cent, is necessary to generate a gravitational redshift variation large enough to mask or mimic an Earth-twin. We explore possible mechanisms for stellar radius fluctuations in low-mass stars. Convective inhibition due to varying magnetic field strengths and the Wilson depression of starspots are both found to induce substantial gravitational redshift variations. Finally, we investigate a possible method for monitoring/correcting this newly identified potential source of jitter and comment on its impact for future exoplanet searches.

Non-radial pulsation, rotation and outburst in the Be star omega Orionis from the MuSiCoS 1998 campaign

omega Ori (HD37490, HR1934) is a Be star known to have presented variations. In order to investigate the nature and origin of its short-term and mid-term variability, a study is performed of several spectral lines (Halpha, Hdelta, HeI 4471, 4713, 4921, 5876, 6678, CII 4267, 6578, 6583, Mg II 4481, Si III 4553 and Si II 6347), based on 249 high signal-to-noise high-resolution spectra taken with 8 telescopes over 22 consecutive nights during the MuSiCoS (Multi SIte COntinuous Spectroscopy) campaign in November-December 1998. The stellar parameters are revisited and the projected rotational velocity (v sin i = 179 km s(-1)) is redetermined using several methods. With the MuSiCoS 98 dataset, a time series analysis of line-profile variations (LPVs) is performed using the Restricted Local Cleanest (RLC) algorithm and a least squares method. The behaviour of the velocity of the centroid of the lines, the equivalent widths and the apparent vsini for several lines, as well as Violet and Red components of photospheric lines affected by emission (red He i lines, Si II 6347, CII 6578, 6583) are analyzed. The non-radial pulsation (NRP) model is examined using phase diagrams and the Fourier-Doppler Imaging (FDI) method. The LPVs are consistent with a NRP mode with l = 2 or 3, \m\ = 2 with frequency 1.03 cd(-1). It is shown that an emission line outburst occurred in the middle of the campaign. Two scenarios are proposed to explain the behaviour of a dense cloud, temporarily orbiting around the star with a frequency 0.46 c d(-1), in relation to the outburst.

Estimating Stellar Radial Velocity Variability from Kepler and GALEX: Implications for the radial velocity confirmation of exoplanets.

We cross match the GALEX and Kepler surveys to create a unique dataset with both ultraviolet (UV) measurements and highly precise photometric variability measurements in the visible light spectrum. As stellar activity is driven by magnetic field modulations, we have used UV emission from the magnetically heated gas in the stellar atmosphere to serve as our proxy for the more well-known stellar activity indicator, R' HK . The R' HK approximations were in turn used to estimate the level of astrophysical noise expected in radial velocity (RV) measurements and these were then searched for correlations with photometric variability. We find significant scatter in our attempts to estimate RV noise for magnetically active stars, which we attribute to variations in the phase and strength of the stellar magnetic cycle that drives the activity of these targets. However, for stars we deem to be magnetically quiet, we do find a clear correlation between photometric variability and estimated levels of RV noise (with variability up to ~10 m s–1). We conclude that for these quiet stars, we can use photometric measurements as a proxy to estimate the RV noise expected. As a result, the procedure outlined in this paper may help select targets best-suited for RV follow-up necessary for planet confirmation.

The management of radial scars of the breast - does core biopsy help?

Purpose: To compare the effectiveness of fine needle aspiration cytology (FNAC) with core biopsy (CB) in the pre-operative diagnosis of radial scar (RS) of the breast.

Patients and methods: A retrospective analysis was made of all radial scars diagnosed on surgical histology over an 8-year period. Comparison was made between the results of different preoperative needle biopsy techniques and surgical histology findings.

Results: Forty of 47 patients with a preoperative radiological diagnosis of radial scar were included in this analysis. Thirty-eight patients had impalpable lesions diagnosed on mammography and two presented with a palpable lump. FNAC (n=17) was inadequate in 47% of patients, missed two co-existing carcinomas found in this group, and gave a false positive or suspicious result for malignancy in 4 patients. CB (n=23) suggested a RS in 15 patients, but only diagnosed 4 out of 7 co-existing carcinomas found in this group.

Conclusion: CB is more accurate than FNAC in the diagnosis of RS. However, these data demonstrate that CB may offer little to assist in the management of patients with RS. In summary, this paper advocates the use of CB in any lesion with a radiological suspicion of carcinoma and diagnostic excision of all lesions thought to be typical of RS on mammography.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

A 1-D radial vaneless diffuser model accounting for the effects of aerodynamic blockage

The radial vaneless diffuser, though comparatively simple in terms of geometry, poses a significant challenge in obtaining an accurate 1-D based performance prediction due to the swirling, unsteady and distorted nature of the flow field. Turbocharger compressors specifically, with the ever increasing focus on achieving a wide operating range, have been recognised to operate with significant regions of spanwise separated flow, particularly at off-design conditions.

Using a combination of single passage Computational Fluid Dynamics (CFD) simulations and extensive gas stand test data for three geometries, the current study aims to evaluate the onset and impact of spanwise aerodynamic blockage in radial vaneless diffusers, and how the extent of the blocked region throughout the diffuser varies with both geometry and operating condition. Having analysed the governing performance parameters and flow phenomena, a novel 1-D modelling method is presented and compared to an existing baseline method as well as test data to quantify the improvement in prediction accuracy achieved.