An overview of ORCHID’s research in the energy domain.
An overview of ORCHID’s research in the energy domain.
One key challenge associated with the anticipated proliferation of electric vehicles (EVs) is that the current infrastructure for en-route charging is highly limited. This is exacerbated by the short range of EVs and the long time it takes to fully re-charge them. In this work, we propose a novel EV navigation algorithm that utilises the limited charging infrastructure more efficiently by communicating with other EVs and routing the driver to charging stations with the lowest predicted congestion.
Electric vehicles (EVs) are increasingly seen as a key technology for tackling climate change. However, the current UK electricity distribution infrastructure is not designed to deal with the significant additional loads that widespread home charging of EVs will create. To address this, we propose novel auction-like mechanisms that schedule the charging of EVs within the infrastructure constraints and according to the requirements of their owners. This video contains a brief overview of our work on these mechanisms.
MyJoulo is part of a research project investigating ways to help households reduce their heating bills. The Joulo logger measures the temperature at a home’s thermostat over the course of a week. From this data, a mathematical model of how the home responds to the heating system, including how quickly it heats up, cools down, and what thermostat set-point is being used is built. This model can then be used to help households to reduce their bills by making changes to how they use the heating system. Find out more at : https://www.myjoulo.com/
This video provides a brief overview on some of the research directions that we are taking in the area of “Coalition Formation”, which allows a group of agents to come together, coordinate their actions and achieve an outcome which is greater than any outcome that could be achieved by them acting individually [1,4]. Some of those directions involve looking at how to efficiently handle any constraints that may exist , and how to efficiently distribute the computations among the agents .
We are currently running a user study for agentSwitch in Nottingham. As part of this study, we are showing participants a 3 part video that describes how present and potential future of the domestic electricity market, after which the participants are asked to discuss, within a focus group, the issues raised in the videos that concern them most.
Motivated by the need to better manage energy demand in the home, in this work we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used.
To validate our design, we deployed FigureEnergy “in the wild” — 12 participants installed meters in their homes and used the system for a period of two weeks. The results detailed in the paper below indicate that the approach is overall successful: by engaging with the data users discover new information about it, even more than in their prior experience of using other electricity displays.
Domestic homes account for 25% of the UK’s total CO2 emissions, and the majority of this goes toward space and water heating. We have developed an intelligent home heating agent that models and predicts heating costs and provides live feedback to the home owner.
Non-intrusive appliance load monitoring is the process of disaggregating a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. We evaluate the accuracy of our approach using the REDD data set, and show the disaggregation performance when using our training approach is comparable to when sub-metered training data is used. We also present a deployment of our system as a live application and demonstrate the potential for personalised energy saving feedback.
We have designed heating control algorithms for the future smart homes that will be instrumented with sensors throughout and will be aware of the environmental conditions as well as the human activities within them. These control algorithms will help users balance the trade-offs between energy cost, comfort, and carbon emissions through novel, interactive interfaces that will help them make sense of their energy use.