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There is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular techniques is clustering, but depending on the algorithm the resolution of the data can have an important influence on the resulting clusters. This paper shows how temporal resolution of power demand profiles affects the quality of the clustering process, the consistency of cluster membership (profiles exhibiting similar behavior), and the efficiency of the clustering process. This work uses both raw data from household consumption data and synthetic profiles. The motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and switching, fault and fraud detection, demand-side management, and energy efficiency measures. The key criterion for mining very large data sets is how little information needs to be used to get a reliable result, while maintaining privacy and security.

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The UK has adopted legal obligations concerning climate change which will place increased stresses on the current 'traditional' model of centralised generation. This will include the stimulation of large volumes of intermittent generation, more distributed generation and larger and more variable loads at grid extremities, potentially including large volumes of electric vehicles and heat pumps. Smarter grids have been mooted as a major potential contributor to the decarbonisation of electricity, through facilitation of reduced losses, greater system efficiency, enhanced flexibility to allow the system to deal with intermittent sources and a number of other benefits. This article considers the different policy elements of what will be required for energy delivery in the UK to become smarter, the challenges this presents, the extent to which these are currently under consideration and some of the changes that might be needed in the future.

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Reducing energy use in tenanted commercial property requires greater understanding of 'buildings as communities'. Tenanted commercial properties represent: (1) the divergent communities that share specific buildings and (2) the organisational communities represented by multi-site landlord and tenant companies. In any particular tenanted space the opportunity for environmental change is mediated (hindered or enabled) through the lease. This discussion draws on theoretical and practical understandings of (i) the socio-legal relationships of landlords, tenants and their advisors; (ii) the real performance of engineering building services strategies to improve energy efficiency; (iii) how organisational cultures affect the ability of the sector to engage with energy efficiency strategies; and (iv) the financial and economic basis of the relationship between owners and occupiers. The transformational complexity stems from: (i) the variety of commercial building stock; (ii) the number of stakeholders (solicitors, investors, developers, agents, owners, tenants and facilities managers); (iii) the fragmentation within the communities of practice; and (iv) leasehold structures and language. An agenda is proposed for truly interdisciplinary research that brings together both the physical and social sciences of energy use in buildings so that technological solutions are made effective by an understanding of the way that buildings are used and communities behave.

Investigating the energy use of an economy in a resource-constrained world requires an understanding of the relationships of its economic, social, and energy-use elements. We introduce a novel whole-economy analytical framework which harmonises multiple national accounting procedures. The economic elements align with the international system of national accounts. In a modular fashion, our framework curates and maintains disparate accounts (economic stocks and flows, energy use, employment, transport) in parallel, but retains each of their unique measurement unit and accounting requirements. We present the UK as a case study to demonstrate how the data organisation and conditioning procedures are generic and will allow model development for other countries. The framework is capable of exploiting time-series ratios between different measurement units to give key functional relationships that vary gradually over time, are robust and thus useful for analysing national policy complexities such as decarbonisation, employment, investment and balance of payments. We use novel Sankey diagrams to visualise snapshots of the whole system. The framework is neither an exclusively economic, physical, nor social model. It upholds the integrity of each world-view through retaining their unique time-series datasets. As this framework is agnostic to the way in which a nation organises its economy, it has the potential to reduce tension between competing models and philosophies of economic development, environmental refurbishment, and climate change mitigation.

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