Large-scale deployment of effective and suitable demand-side management technologies such as residential energy management systems can bring significant technical, social, and economic advantages for households, and retailers. The current limitation of the wide-scale utilization of such technologies is mainly due to the lack of proper tools to simulate their operation and estimate the overall benefits that can be gain after initial installation. The aim of this research was to develop a generalised model of a residential energy management system that uses household aggregated load profile data and that optimally selects a baseline to model the behaviour of these devices. An autoregressive integrated moving average technique was used to develop the model and then modified to consider the forecast reductions produced by these devices. The thesis has presented a sample full factorial analysis results that demonstrate the impact of tariff structures, sociodemographic profile of customers, and the choice of operational objectives of these devices. The thesis has also developed a technique for maximizing social welfare and evaluate the customers’ optimal baseline profile to quantify the performance of the employed demand response. The effectiveness of the developed technique by numerical analysis through various case studies has been demonstrated.