Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Linking environmental uncertainty to non-financial performance measures and performance : a research note

Several previous research studies have reported mixed results concerning the direct association between non-financial performance measures and  performance. The presence of environmental uncertainty on this relationship has not been established. This paper makes a contribution to this area by proposing that it is in conditions of environmental uncertainty that non-financial measures are most useful in improving organizational performance. It analyses empirical data from a sample of New Zealand manufacturing organizations to test the hypothesis that non-financial measures of performance would lead to improved organizational performance under conditions of increased environmental uncertainty. Multiple regression analysis of the data suggests that performance should be a declining function of the size of the ‘mismatch’ between an organization's environment and use of the different combinations of non-financial performance measures. Further, the paper concludes that prior mixed results may be attributed to the omission of environmental uncertainty.

The knowledge economy and innovation: certain uncertainty and the risk economy

The knowledge economy is a dominant force in today's world, and innovation policy and national systems of innovation are central to it. In this article, we draw on different sociological and economic theories of risk to engage critically with innovation policy and national systems of innovation. Beck's understanding of a risk society, Schumpeter's innovation thesis, and Perez's techno-economic paradigm are used to consider the risk economy, and the broader risk implications of knowledge economy policies and their associated innovation systems. Derrida's theory of haunting provides the methodological framework for our discussion. We use his notion of “hauntology” to conceptualize the risk economy as a ghost that haunts knowledge economy policies and systems. The spectral risk economy draws attention to the inherent instability of the knowledge economy, and challenges the certainty of its economic dogma by offering an alternative perspective. The risk economy problematizes knowledge economy policies and systems by revealing the uncertain and “undecidable” future of social, political and cultural hazards ignored in the interest of commercial gain.

Calibration and reliability of a school food checklist: a new tool for assessing school food and beverage consumption

There is a pressing need in Australia and other countries to develop systems for monitoring secular trends in childhood obesity and related behavioural and environmental determinants. Energy from foods and beverages consumed at school is an accessible indicator of children’s eating patterns and we have developed a school food checklist (SFC) to measure this. The SFC records the number of serves and source (home, canteen, vending machine) of 20 food and beverage categories. This study aims to assess the accuracy and to calibrate the SFC by comparing it to a weighed record (WR) and to evaluate inter-recorder reliability. Participants were 910 primary school children aged 5 to 12 years from a rural township in Victoria, Australia. WR were collected from a nonrandom sub-sample of 106 and a second sub-sample (n=46) had intake measured twice using the SFC to assess inter-recorder reliability. Mean energy values were 2992 kJ ± 924 and 3008 kJ ± 952 for the SFC and WR respectively and the correlation coefficient was strong (Pearson r = 0.77). The mean difference between the WR and SFC methods was 15 kJ (95% CI, -107 kJ to 138 kJ) and the limits of agreement (+2 standard deviations) were ± 1270 kJ. The SFC overestimated the energy/serve of breads and fruit drinks and under-estimated energy/serve from fat spreads, biscuits/crackers, muesli/fruit bars and fruit. Inter-recorder reliability was good (kappa 0.51). The SFC was designed to measure energy from food and beverages in schools. It has good accuracy and reliability and the revised version should further improve accuracy of the instrument.

Interrogating the discourse of 'social literacies' in an era of uncertainty

Originally, the term 'social literacies' was used to suggest the skills, knowledge and processes for addressing multicultural teaching and learning (Kalantzis and Cope, 1983). The meaning of the phrase has since evolved to encompass widely different concepts, including for example, social 'competencies', and/or citizenship education (eg., Arthur & Davison, 2000). Clearly the discourse around 'social literacies' is shifting in response to changing educational policies, both nationally and internationally.

In this paper, we examine how constructs of 'social literacies' have been and might be deployed. Building from a review of the policy, program and theoretical literature, we pose questions concerning how 'social literacies' might be used to interrogate and rework relations, especially those of gender and culture. Questions to be considered include: will the concept of 'social literacies' enable us to better understand the processes of identity and community formations in this era of uncertainty? Which knowledges and skills are identified in the literature and positioned as critical in establishing 'productive' social relations/literacies? Additionally, we begin to theorise the degree to which such constructions of 'social literacies' might enhance and/or limit quality learning at the tertiary levels of teacher education.

A calibration for surface analysis on aluminium alloys by RF-Glow-Discharge Optical Emission Spectrometry

Glow-Discharge Optical Emission Spectrometry (GD-OES) is a powerful technique for the rapid analysis of elements in a solid surface as a function of depth. DC-GD-OES allows depth profiling on electrically conductive surfaces only, and has proven to be difficult for the analysis of insulating layers, such as oxides. However, the technique of radio-frequency (RF) GD-OES has the advantage of being able to depth profile through multiple layers, both conducting and insulating. In this work, a LECO GDS- 850A spectrometer was calibrated for aluminium, oxygen, and other elements, with the RF source installed. A quantitative depth profile for a sample of tempered aluminium alloy 7475 is presented and compared with earlier work[1,2].

Educational organizations and gender in times of uncertainty

In this chapter I identify and elaborate, from a feminist perspective, upon the theoretical shifts and key concepts that inform sociological analyses of gender and educational organizations. Gender inequalities are embedded in the multi-dimensional structure of relationships between women and men, which, as the modern sociology of gender shows, operates at every level of experience, from economic arrangements, culture and the state to interpersonal relationships and individual emotions. (Connell, 2005: 1801) Even naming this a sociology of gender and organizations is problematic. Many sociologists consider gender as a key sociological concept, but not necessarily from a feminist perspective. Feminism is a multidisciplinary, transnational movement that 'focuses on the relationship between social movements, political action and social inequalities' (Arnot, 2002: 3) and on the everyday experiences of women and girls and how they translate into social and structural 'ruling relations' (Smith, 1988). Feminism takes on multiple trajectories and imperatives in different cultural contexts, although with familial resemblances, most particularly the shared objective of equality for women and girls. Education as a primary institution of individual and collective mobility and social change, but also social and economic reproduction, has long been a focus of feminist theory and activism. So a feminist sociology needs to address this complexity of feminist sociological 'encounters' with gender and organizations.

Creative redemption : uncertainty in poetic creativity

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Exposure estimation, uncertainty and variability in occupational hygiene retrospective assessment

This thesis reports on a quantitative exposure assessment and on an analysis of the attributes of the data used in the estimations, in particular distinguishing between its uncertainty and variability. A retrospective assessment of exposure to benzene was carried out for a case control study of leukaemia in the Australian petroleum industry. The study used the mean of personal task-based measurements (Base Estimates) in a deterministic algorithm and applied factors to model back to places, times etc for which no exposure measurements were available. Mean daily exposures were estimated, on an individual subject basis, by summing the task-based exposures. These mean exposures were multiplied by the years spent on each job to provide exposure estimates in ppm-years. These were summed to provide a Cumulative Estimate for each subject. Validation was completed for the model and key inputs. Exposures were low, most jobs were below TWA of 5 ppm benzene. Exposures in terminals were generally higher than at refineries. Cumulative Estimates ranged from 0.005 to 50.9 ppm-years, with 84 percent less than 10 ppm-years. Exposure probability distributions were developed for tanker drivers using Monte Carlo simulation of the exposure estimation algorithm. The outcome was a lognormal distribution of exposure for each driver. These provide the basis for alternative risk assessment metrics e.g. the frequency of short but intense exposures which provided only a minimal contribution to the long-term average exposure but may increase risk of leukaemia. The effect of different inputs to the model were examined and their significance assessed using Monte Carlo simulation. The Base Estimates were the most important determinant of exposure in the model. The sources of variability in the measured data were examined, including the effect of having censored data and the between and within-worker variability. The sources of uncertainty in the exposure estimates were analysed and consequential improvements in exposure assessment identified. Monte Carlo sampling was also used to examine the uncertainties and variability associated with the tanker drivers' exposure assessment, to derive an estimate of the range and to put confidence intervals on the daily mean exposures. The identified uncertainty was less than the variability associated with the estimates. The traditional approach to exposure estimation typically derives only point estimates of mean exposure. The approach developed here allows a range of exposure estimates to be made and provides a more flexible and improved basis for risk assessment.

Breast cancer : the embodied transfer of uncertainty and risk

The thesis highlights how women reconstruct themselves after mammography, following a positive diagnosis of cancer, and post mastectomy. It juxtaposes women's experiences of breast cancer with doctors' perceptions of their role in treating patients, allowing an understanding of how risk and uncertainty are transferred between the private and public spheres.

Risk and the regulation of youth(ful) identities in an age of manufactured uncertainty

Examines the benefits and dangers of allowing experts to intervene in the lives of youth, and their families, on the basis of assessing them as being at-risk.  Highlights the dangers of expert promises to prevent risk by intervening in people's lives on the basis of what is perceived to be normal or good.

Technology uncertainty and market collusion

In this paper, we propose technology uncertainty as a new factor relevant to market collusion. We analyze an infinitely repeated quantity game where, for each firm, the marginal productivity of the input employed in the production process is affected by an unobservable shock. Each firm faces technology uncertainty, measured by the variance of the shocks, in every period. We show that, under both grim trigger strategies and optimal punishments, technology uncertainty enhances cartel stability, suggesting that, in industries characterized by technology uncertainty, the actions of the antitrust authorities should be intensified. We also show that collusion is less likely when technology shocks are highly correlated, implying that regulators interested in deterring collusion should promote the formation of industrial clusters.