London Hydro

A federated identity is a single identity that enables users to access multiple services across a network of business parties. Such identities are subject to various threats and attacks and face diverse challenges including identity leaks, centralised management, auditing limitations, and long breach investigation processes. This paper proposes a framework aimed at automating and decentralising the generation and auditing of a robust and secured blockchain-based federated identity in a marketplace. Business parties participating in the marketplace form the nodes of a distributed blockchain network and participate in the creation of federated identities. Users of this network can access services provided by any one of the participating parties using a single federated identity. All transactions are fully audited in the blockchain, meaning that participating parties can monitor access to their service and users can trace the use of their identities. The proposed framework has been evaluated using two blockchain technologies (Ethereum and Hyperledger Fabric) to measure its performance in public and permissioned blockchain environments.

Climate change and environmental concerns are instigating widespread changes in the modern electricity sector due to energy policy initiatives and advances in sustainable technologies. With the deluge of information resulting from the ubiquitous communication and computational capabilities present in all aspects of our society, system operators and consumers have elevated situational awareness and are able to make informed context-based decisions. We capitalize on this information-centric nature of the advanced metering infrastructure (AMI) in the power grid to enable non-intrusive load monitoring for individual consumers with high accuracy. We propose a novel ensemble based deep learning model to disaggregate smart meter readings and identify the operation of individual appliances. We show through comprehensive practical and comparative studies the superior performance of the proposed model.

Benchmarking makes it possible to identify low-performing buildings, establishes a baseline for measuring performance improvements, enables setting of energy conservation targets, and encourages energy savings by creating a competitive environment. Statistical approaches evaluate building energy efficiency by comparing measured energy consumption to other similar buildings typically using annual measurements. However, it is important to consider different time periods in benchmarking because of differences in their consumption patterns. For example, an office can be efficient during the night, but inefficient during operating hours due to occupants’ wasteful behavior. Moreover, benchmarking studies often use a single regression model for different building categories. Selecting the regression model based on actual data would ensure that the model fits the data well. Consequently, this paper proposes Energy Slices, an energy benchmarking approach with time slicing for existing buildings. Time slicing enables separation of time periods with different consumption patterns. The regression model suited for the specific scenario is selected using cross validation, which ensures that the model performs well on previously unseen data. The evaluation is carried out on a case study involving two sports arenas; event energy efficiency is benchmarked to identify low-performing events. The case study demonstrates the Energy Slice procedure and shows the importance of model selection.

The capabilities of lag reshaping for the generalised spectral estimation technique, in order to realise desirable effective windows, is illustrated for several combinations of temporal and lag weightings. Also the variance of the spectral estimate is presented and computed for some windows with very good sidelobe behaviour. It is found that, with proper overlap, if the length of the temporal weighting is somewhat larger than the length of the lag weighting, the variance is at a near minimum relative to any technique which realises the same frequency resolution with the given finite data record length.