Rethinking electrical water heaters

[摘要] ENGLISH ABSTRACT: South Africa is, at the time of writing, in the midst of an energy crisis as the nationalutility 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 andother countries where they are used. Although educational material has been publishedto create awareness of energy saving actions for EWHs, it is unclear if users understandthe content and efficiently control their EWHs. Additionally, insufficient feedback ofusage data makes it difficult for consumers to understand their consumption patternsand make informed decisions regarding their future water and electricity use. This workpresents a mobile based eco-feedback system for the energy and water consumption dataof residential EWHs. The system consists of several components: an EWH model; anevent detection algorithm; and an Android mobile application.The physics based EWH model was developed in order to accurately simulate theenergy 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 sixdatasets, four comprising 900 hours with multiple usage events and two with only standinglosses. The results show that measured energy usage is modelled with an estimationerror of less than 2% and 7% for schedule control and thermostat control respectively. Aswell as being accurate, the presented model has a low computational complexity, takingonly 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 developedto support the identification and classification of warm water usage events without the useof invasive and expensive water metering technologies. The algorithm was tested using 49days of data which included 127 usage events and was found to accurately detect usageevents with an accuracy of 91%. Additionally, the algorithm was able to detect very smallusage events (0.5 litres was detected successfully). However, the estimated duration ofevents is within 2 minutes accurate 79% of the time. Additionally, the outlet temperatureand water meter data were used as inputs to the EWH model for estimating the energyconsumption under various control settings. The outlet temperature data was used toestimate both the total volume of warm water consumed and the energy input for theEWH 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 monitorand control their EWH from their mobile device. The EWH model was implementedas part of the functionality of the mobile application to provide a user with instantaneousfeedback 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 theirEWH off intermittently will affect their energy consumption. Additionally, the event detectionalgorithm was utilised by the mobile application to establish usage profiles andprovide recommended schedules for users, based on their consumption data. Finally, a usability study was conducted in order to evaluate the ease with which users are able toutilise the mobile application and to improve on any areas of difficulty that may exist.Several areas of difficulty were determined and these results were used to implement variouschanges to improve the application by making it more user friendly. The results ofthe study indicate that the system is user friendly and that participants had a positiveoverall experience with the mobile application.

[发布机构] Stellenbosch University