This paper proposes and compares feature construction and calibration methods for clustering daily electricity load curves. Such load curves describe electricity demand over a period of time. A rich body of the literature has studied clustering of load curves, usually using temporal features. This limits the potential to discover new knowledge, which may not be best represented as models consisting of all time points on load curves. This paper presents three new methods to construct features: 1) conditional filters on time-resolution-based features; 2) calibration and normalization; and 3) using profile errors. These new features extend the potential of clustering load curves. Moreover, smart metering is now generating high-resolution time series, and so the dimensionality reduction offered by these features is welcome. The clustering results using the proposed new features are compared with clusterings obtained from temporal features, as well as clusterings with Fourier features, using household electricity consumption time series as test data. The experimental results suggest that the proposed feature construction methods offer new means for gaining insight in energy-consumption patterns.

The modelling of complex, dynamic and uncertain socioenvironmental systems requires close collaboration between research disciplines and stakeholders at all levels, for if such models are representations of aspects of reality, how can it be possible to build them without inputs from people who interact with the systems in reality? This paper reflects on findings of case study research involving stakeholders in knowledge creation through conceptual and formal model building to support upland water catchment management. The uncertainty, multiple scales and conflicting understandings of stakeholders inherent in natural resource management necessitate a strong focus on participatory processes in integrated modelling. This leads to the recognition that problems and solutions should be identified by the stakeholders themselves, emphasising the intersection and complementarity of lay and expert knowledge. Stakeholders in this context comprise varied groups such as land managers (e.g. grouse moor managers and sheep farmers), water companies and water users, tourists, residents, policy-makers and researchers. Models are used at multiple stages to help formulate problems, create and compare conceptual understanding, and explore implications. This requires the involvement of stakeholders early in the problem formulation and research process to help answer the 'right' questions and provide 'relevant' outputs. This poses a number of interesting new challenges for the organisation of research. In contrast to traditional approaches of matching model components to expertise in the research team, modelling expertise must adapt to answer the questions and priorities that emerge from stakeholder engagement.

This work presents data mining methods for discovering unusual consumption patterns and their associated descriptive models from smart electricity meter data. At present, data mining and knowledge discovery in electricity meter data suffer from three notable weaknesses: 1) insufficient focus on intelligent data analysis of subgroups (subsets) whose patterns vary significantly from aggregate patterns embodied in an entire dataset; 2) a lack of effort towards generating intuitively understandable and practically applicable knowledge for industrial practitioners to identify such subgroups; and 3) limited knowledge regarding the link between unusual consumption patterns and household consumers' socio-demographic characteristics. This paper addresses these practically important but technically challenging issues by applying subgroup discovery algorithms to a real smart electricity meter dataset. Subgroups whose patterns are unusual and whose sizes are large enough are discovered, and their descriptive and predictive models are generated. Furthermore, to enrich subgroup discovery algorithms, three new-quality measures for real-valued targets are proposed. The comparative studies empirically evaluate the effectiveness and usefulness of subgroup discovery on classification accuracy, predictive power, and computational resources. The methodologies and algorithms presented are generic, and therefore applicable to a wider range of data mining problems.