The ESHER project aims to enable smart services for homes in relation to energy use and guide users in opportunities for demand response actions through the application of integrated visualisation and feedback techniques. The project will collect data from smart meters, appliances and sensors in 20 homes and 1 small business, and build advanced machine learning algorithms to predict and disaggregate energy use. Fixed and variable loads will then be identified to understand what can be part of demand response actions.
Suggestions for demand response actions could be to turn appliances on/off at certain times or switch to a more efficient source of supply to save money. The user will be given guidance via online dashboards that can be accessed by smartphone apps or using a tablet. Different forms of guidance, frequency and incentives will also be tested. Where smart devices are installed, the possibility of automatically carrying out demand response actions will be investigated.
Finally, the potential for Time of Use tariffs will also be explored by use of an innovative energy trading business model and the concept of a high street retail market. This will be developed and virtually tested using the data from the demonstration sites to examine the potential for a 2-way dynamic market, enabling home owners to choose which supplier they wish to purchase their energy from at different times of the day. The results from the project will form recommendations for a wider deployment of smart micro grid applications in Ireland.
IES will use their Digital Twin technology to create models of each of the test buildings and analyse performance and behaviour over time before any interventions or actions are taken. This will then be integrated with the Home Management System to be developed by TUD. Different actions that we want the end users to take with respect to the optimisation of their buildings will then be displayed on dashboards that utilise augmented intelligence, i.e. enabling the end user to take actions themselves but with information driven from intelligent systems.