The residential sector represents approximately 30% of global electricity consumption, but theunderlying drivers are still poorly understood. The drivers are many, varied, and complex,including local climate, household demographics, household behaviour, building stock and thetype and number of appliances. There is considerable variation across households and, untilrecently, often a lack of good data.This thesis draws upon a detailed household dataset from the Australian Smart Grid Smart Cityproject to build a residential demand modelling tool set. This data covers a part of greaterSydney. Two statistical models for household annual electricity demand and half-hourly peakelectricity demand were established and tested for both individual households and regionalaggregations of households. The model showed only reasonable performance in forecasting theconsumption of individual households, highlighting the influence of factors beyond thosesurveyed. However, the model demonstrated good performance for aggregated householdconsumption: 3.9% MAPE for annual electricity consumption forecast and 4.57% MAPE for peakdemand forecast. Models such as this would be highly useful for a range of stakeholders,including individual households, trying to understand the potential implications of differentchoices and utilities looking to better forecast the impact of different possible residential trends.The model would also be very helpful to grid operators seeking better reliability while avoidingaugmentation and to policy makers seeking to improve householder’s energy efficiency throughtargeted policies and programs. Based on the developed tool set, models were built to simulatevarious strategies for annual and peak demand reduction, and socio-economic evaluations werecalculated and compared between different reduction options. Results showed thatbehavioural and demand response interventions were found to provide the most cost effectivepeak reduction. The results were scaled up to the Sydney geographical region to provide realisticrecommendations for policy makers, utility operators and other stakeholders. In addition,annual demand reduction intervention using feedback systems were investigated. Resultsshowed that feedback interventions have different effectiveness on households with differentcharacteristics. The statistically significant findings directly support the fact that demandreduction intervention should be tailored to match specific household types to achieve optimumand cost effective outcomes.
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