Rethinking electrical water heaters

South Africa is, at the time of writing, in the midst of an energy crisis as the national utility is unable to meet the nation’s energy demands. Electrical water heaters (EWHs) remain one of the main contributors to residential energy consumption in South Africa and other countries where they are used. Although educational material has been published to create awareness of energy saving actions for EWHs, it is unclear if users understand the content and efficiently control their EWHs. Additionally, insufficient feedback of usage data makes it difficult for consumers to understand their consumption patterns and make informed decisions regarding their future water and electricity use. This work presents a mobile based eco-feedback system for the energy and water consumption data of residential EWHs. The system consists of several components: an EWH model; an event detection algorithm; and an Android mobile application. The physics based EWH model was developed in order to accurately simulate the energy input and output of an EWH for various control settings, usage profiles and orientations (i.e. vertical and horizontal). The accuracy of the model is validated against six datasets, four comprising 900 hours with multiple usage events and two with only standing losses. The results show that measured energy usage is modelled with an estimation error of less than 2% and 7% for schedule control and thermostat control respectively. As well as being accurate, the presented model has a low computational complexity, taking only 100 milliseconds to complete a 10 day simulation on a standard desktop machine, making it ideal for use in mobile devices. A novel and non-invasive hardware solution and matching algorithm were developed to support the identification and classification of warm water usage events without the use of invasive and expensive water metering technologies. The algorithm was tested using 49 days of data which included 127 usage events and was found to accurately detect usage events with an accuracy of 91%. Additionally, the algorithm was able to detect very small usage events (0.5 litres was detected successfully). However, the estimated duration of events is within 2 minutes accurate 79% of the time. Additionally, the outlet temperature and water meter data were used as inputs to the EWH model for estimating the energy consumption under various control settings. The outlet temperature data was used to estimate both the total volume of warm water consumed and the energy input for the EWH with an error of less than 10% for 3 of the 4 datasets considered. An Android mobile application was then created to allow consumers to remotely monitor and control their EWH from their mobile device. The EWH model was implemented as part of the functionality of the mobile application to provide a user with instantaneous feedback on the impact of changes in control settings and usage profiles. For example, this functionality in the mobile application allows users to determine how switching their EWH off intermittently will affect their energy consumption. Additionally, the event detection algorithm was utilised by the mobile application to establish usage profiles and provide recommended schedules for users, based on their consumption data. Finally, a ii Stellenbosch University https://scholar.sun.ac.za