An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Cloning and functional analysis of the Sry-related HMG box gene, Sox18

The Sox gene family (Sry like HMG box gene) is characterised by a conserved DNA sequence encoding a domain of approximately 80 amino acids which is responsible for sequence specific DNA binding. We initially published the identification and partial cDNA sequence of murine Sox18, a new member of this gene family, isolated from a cardiac cDNA library. This sequence allowed us to classify Sox18 into the F sub-group of Sox proteins, along with Sox7 and Sox17. Recently, we demonstrated that mutations in the Sox18 activation domain underlie cardiovascular and hair follicle defects in the mouse mutation, ragged (Ra) (Pennisi et al., 2000. Mutations in Sox18 underlie cardiovascular and hair follicle defecs in ragged mice. Nat. Genet. 24, 434-437). Ra homozygotes lack vibrissae and coat hairs, have generalised oedema and an accumulation of chyle in the peritoneum. Here we have investigated the genomic sequences encoding Sox18. Screening of a mouse genomic phage library identified four overlapping clones, we sequenced a 3.25 kb XbaI fragment that defined the entire coding region and approximately 1.5 kb of 5' flanking sequences. This identified (i) an additional 91 amino acids upstream of the previously designated methionine start codon in the original cDNA, and (ii);ln intron encoded within the HMG box/DNA binding domain in exactly the same position as that found in the Sox5, -13 and -17 genes. The Sox18 gene encodes a protein of 468 aa. We present evidence that suggests HAF-2, the human HMG-box activating factor-2 protein, is the orthologue of murine Sox18. HAF-2 has been implicated in the regulation of the Human IgH enhancer in a B cell context. Random mutagenesis coupled with GAL4 hybrid analysis in the activation domain between amino acids 252 and 346, of Sox18, implicated the phosphorylation motif, SARS, and the region between amino acid residues 313 and 346 as critical components of Sox18 mediated transactivation. Finally, we examined the expression of Sox18 in multiple adult mouse tissues using RT-PCR. Low-moderate expression was observed in spleen, stomach, kidney, intestine, skeletal muscle and heart. Very abundant expression was detected in lung tissue. (C) 2001 Elsevier Science B.V. All rights reserved.

Infusing the use of seasonal climate forecasting into crop management practice in North East Australia using discussion support software

Seasonal climate forecasting offers potential for improving management of crop production risks in the cropping systems of NE Australia. But how is this capability best connected to management practice? Over the past decade, we have pursued participative systems approaches involving simulation-aided discussion with advisers and decision-makers. This has led to the development of discussion support software as a key vehicle for facilitating infusion of forecasting capability into practice. In this paper, we set out the basis of our approach, its implementation and preliminary evaluation. We outline the development of the discussion support software Whopper Cropper, which was designed for, and in close consultation with, public and private advisers. Whopper Cropper consists of a database of simulation output and a graphical user interface to generate analyses of risks associated with crop management options. The charts produced provide conversation pieces for advisers to use with their farmer clients in relation to the significant decisions they face. An example application, detail of the software development process and an initial survey of user needs are presented. We suggest that discussion support software is about moving beyond traditional notions of supply-driven decision support systems. Discussion support software is largely demand-driven and can compliment participatory action research programs by providing cost-effective general delivery of simulation-aided discussions about relevant management actions. The critical role of farm management advisers and dialogue among key players is highlighted. We argue that the discussion support concept, as exemplified by the software tool Whopper Cropper and the group processes surrounding it, provides an effective means to infuse innovations, like seasonal climate forecasting, into farming practice. Crown Copyright (C) 2002 Published by Elsevier Science Ltd. All rights reserved.

The VCAM-1 gene that encodes the vascular cell adhesion molecule is a target of the Sry-related high mobility group box gene, Sox18

VCAM-1 (vascular cell adhesion molecule-1) and Sox18 are involved in vascular development. VCAM-1 is an important adhesion molecule that is expressed on endothelial cells and has a critical role in endothelial activation, inflammation, lymphatic pathophysiology, and atherogenesis. The Sry-related high mobility group box factor Sox18 has previously been implicated in endothelial pathologies. Mutations in human and mouse Sox18 leads to hypotrichosis and lymphedema. Furthermore, both Sox18 and VCAM-1 have very similar spatio-temporal patterns of expression, which is suggestive of crosstalk. We use biochemical techniques, cell culture systems, and the ragged opossum (RaOP) mouse model with a naturally occurring mutation in Sox18 to demonstrate that VCAM-1 is an important target of Sox18. Transfection, site-specific mutagenesis, and gel shift analyses demonstrated that Sox18 directly targeted and trans-activated VCAM-1 expression. Importantly, the naturally occurring Sox18 mutant attenuates the expression and activation of VCAM-1 in vitro. Furthermore, in vivo quantitation of VCAM-1 mRNA levels in wild type and RaOP mice demonstrates that RaOP animals show a dramatic and significant reduction in VCAM-1 mRNA expression in lung, skin, and skeletal muscle. Our observation that the VCAM-1 gene is an important target of SOX18 provides the first molecular insights into the vascular abnormalities in the mouse mutant ragged and the human hypotrichosis-lymphedematelangiectasia disorder.

Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal

In face of the current economic and financial environment, predicting corporate bankruptcy is arguably a phenomenon of increasing interest to investors, creditors, borrowing firms, and governments alike. Within the strand of literature focused on bankruptcy forecasting we can find diverse types of research employing a wide variety of techniques, but only a few researchers have used survival analysis for the examination of this issue. We propose a model for the prediction of corporate bankruptcy based on survival analysis, a technique which stands on its own merits. In this research, the hazard rate is the probability of ‘‘bankruptcy’’ as of time t, conditional upon having survived until time t. Many hazard models are applied in a context where the running of time naturally affects the hazard rate. The model employed in this paper uses the time of survival or the hazard risk as dependent variable, considering the unsuccessful companies as censured observations.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Volatility forecasting with range models: An evaluation of new alternatives to the CARR model

The aim of this paper is to analyze the forecasting ability of the CARR model proposed by Chou (2005) using the S&P 500. We extend the data sample, allowing for the analysis of different stock market circumstances and propose the use of various range estimators in order to analyze their forecasting performance. Our results show that there are two range-based models that outperform the forecasting ability of the GARCH model. The Parkinson model is better for upward trends and volatilities which are higher and lower than the mean while the CARR model is better for downward trends and mean volatilities.

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.