A Non-intrusive Heuristic for Energy Messaging Intervention Modelled using a Novel Agent-based Approach

In response to the increased energy consumption in residential buildings, various efforts have been devoted to increase occupant awareness using energy feedback systems. However, it was shown that feedback provided by these systems is not enough to inform occupant actions to reduce energy consumption. Another approach is to control energy consumption using automated energy management systems. The automatic control of appliances takes-out the occupant sense of control, which is proved to be uncomfortable in many cases. This paper proposes an energy messaging intervention that keeps the control for occupants whilst supporting them with actionable messages. The messages inform occupants about energy waste incidents happening in their house in real-time, which enables occupants to take actions to reduce their consumption. Besides, a heuristic is defined to make the intervention non-intrusive by controlling the rate and time of the messages sent to occupants. The proposed intervention is evaluated in a novel layered agentbased model. The first layer of the model generates detailed energy consumption and realistic occupant activities. The second layer is designed to simulate the peer pressure effect on the energy consumption behaviour of the individuals. The third layer is a customisable layer that simulates energy interventions. The implemented intervention in this paper is the proposed non-intrusive messaging intervention. A number of scenarios are presented in the experiments to show how the model can be used to evaluate the proposed intervention and achieve energy efficiency targets

Taking a Passivhaus certified retrofit system onto scaled-up zero carbon trajectory

The paper describes collaboration a between industryand academia in enhancing a Passivhaus certifiedsystem for retrofit and putting it onto a zero carbontrajectory. The system was initially developed for onsite stick construction, using fixed insulationthickness and under the current UK climate. Thecollaboration with the university has contributed to aproduct development that is adaptable to differentbuildings and future climates, achieved by multiobjective optimisation. This process considers carbonemissions and comfort as functions to be optimised,and applies a number of design variables, takingdiscrete values within specified ranges of thesevariables, and producing numerous combinations fora single design. Dynamic simulations are conductedover these combinations, producing a solution spacethat is subsequently searched by a genetic algorithmfor optimum solutions. A resultant chart gives arange of trade-off solutions that enable the designteam to enhance retrofit system and make it zerocarbon ready. In addition to the design optimisation,the scaling up of this system is facilitated by onsite3D laser scanning, which enables a transition to anoffsite solution developed in flying factories. Thepaper reports on a practical application of this workto designing a retrofit for two semi-detached house

FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks

Federated Learning (FL) is an innovative area of machine learning that enables different clients to collaboratively generate a shared model while preserving their data privacy. In a typical FL setting, a central model is updated by aggregating the clients’ parameters of the respective artificial neural network. The aggregated parameters are then sent back to the clients. However, two main challenges are associated with the central aggregation approach. Firstly, most state-of-the-art strategies are not optimised to operate in the presence of certain types of non-iid (not independent and identically distributed) applications and datasets. Secondly, federated learning is vulnerable to various privacy and security concerns related to model inversion attacks, which can be used to access sensitive information from the training data. To address these issues, we propose a novel federated learning strategy FedNets based on ensemble learning. Instead of sharing the parameters of the clients over the network to update a single global model, our approach allows clients to have ensembles of diverse-lightweight models and collaborate by sharing ensemble members. FedNets utilises graph embedding theory to reduce the complexity of running Deep Neural Networks (DNNs) on resource-limited devices. Each Deep Neural Network (DNN) is treated as a graph, from which respective graph embeddings are generated and clustered to determine which part of the DNN should be shared with other clients. Our approach outperforms state-of-the-art FL algorithms such as Federated Averaging (Fed-Avg) and Adaptive Federated Optimisation (Fed-Yogi) in terms of accuracy; on the Federated CIFAR100 dataset (non-iid), FedNets demonstrates a remarkable 63% and 92% improvement in accuracy, respectively. Furthermore, FedNets does not compromise the client’s privacy, as it is safeguarded by the design of the method

An Analysis Approach for Context-Aware Energy Feedback Systems

Several energy systems have been developed and studied to help occupants reduce energy usage by providing feedback about their consumption. But recently, a major challenge has emerged about how to enable users to make informed energy efficiency decisions based on consumption feedback. This is because existing systems only present abstract consumption data that are not related to the surrounding energy consumption context. This paper proposes a novel energy data analysis approach which leverages context-awareness to support users to take actions that improve energy efficiency. The approach consists of two stages: multidimensional analysis followed by case-based reasoning. The anticipated output of the analysis approach will be understandable and actionable feedback that helps occupants control their energy consumption

Defining Context for Home Electricity Feedback Systems

Existing electricity feedback systems provide home occupants with real-time consumption data to enable them to control their consumption. However, these systems provide abstract consumption data that is not related to the occupants surrounding. Although there are some attempts to enrich consumption data with some context information, the presented feedback is not enough to inform decisions of how to conserve electricity. Therefore, this paper provides a rich definition of electricity consumption context, which can be used to provide sensible feedback to users. The obtained context elements can be categorized into three context types: User Context, Appliances Context, and Environment Context. Finally, different implications for the application of a context-aware feedback system are presented showing how the obtained context definition could be used to provide understandable feedback