Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

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.

Applying a Systems Approach to the Analysis of the Benefits of Conversion to Organic Production

Although the current level of organic production in industrialised countries amounts to little more than 1-2 percent, it is recognised that one of the major issues shaping agricultural output over the next several decades will be the demand for organic produce (Dixon et al. 2001). In Australia, the issues of healthy food and environmental concern contribute to increasing demand and market volumes for organic produce. However, in Indonesia, using more economical inputs for organic production is a supply-side factor driving organic production. For individual growers and processors, conversion from conventional to organic agriculture is often a challenging step, entailing a thorough revision of established practices and heightened market insecurity. This paper examines the potential for a systems approach to the analysis of the conversion process, to yield insights for household and community decisions. A framework for applying farming systems research to investigate the benefits of organic production in both Australia and Indonesia is discussed. The framework incorporates scope for farmer participation, crucial to the understanding of farming systems; analysis of production; and relationships to resources, technologies, markets, services, policies and institutions in their local cultural context. A systems approach offers the potential to internalise the external effects that may be constraining decisions to convert to organic production, and for the design of decision-making tools to assist households and the community. Systems models can guide policy design and serve as a mechanism for predicting the impact of changes to the policy and market environments. The increasing emphasis of farming systems research on community and environment in recent years is in keeping with the proposed application to organic production, processing and marketing issues. The approach will also facilitate the analysis of critical aspects of the Australian production, marketing and policy environment, and the investigation of these same features in an Indonesian context.