Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

Optimum coal properties and engineering processes for CO2 geosequestration in Coal

CO2 Geosequestration is seen by many worldwide scientists and engineers as a leading prospective solution to the global warming problem arising from excessive CO2 and other greenhouse gas emissions. CO2 geosequestration in coal seams has two important strategic benefits: the process has an extremely low risk of leakage, due to the adsorbed state of the CO2 and the known reservoir context of essentially-zero leakage into which it is be injected; the second benefit arises from the valuable by-product, clean burning coalbed methane gas. This paper presents the authors’ experience, knowledge and perspective on what coal properties and engineering processes would favour implementing a demonstration or commercial CO2 storage-in-coal project, in Queensland, Australia. As such, it may be considered a template for screening studies to select the optimum coal seam reservoir, and for preliminary studies in designing the injection system and predicting production response to the technology. The paper concludes by examining the current knowledge gaps of CO2 geosequestration in coal, identifying further basic and applied research topics.

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.