To forecast the demand response of a residential customer, forecasted load profiles must first be calculated and the demand response produced by residential energy management devices determined. Finding an efficient method to select an optimal forecasted load profile to assess the demand response of these devices is a fundamental issue currently overlooked. Poorly forecasted load profiles can affect customer compensation for demand reduction, estimation of retail profits and allocation and reconciliation processes for electricity markets. This research resolved to develop an economic model for selecting an optimal forecasted load profile from which the demand response of residential energy management devices can be determined. Using 2 years of half-hourly electricity consumption data for 5379 households obtained from the “Low Carbon” London project (UK), a novel economic model was created to assess the economic advantage of nine different forecast methods. Results suggest that when creating forecasted load profiles to measure demand response of these devices the sociodemographic of customers, tariff type and the operational objectives of the device must be taken into account to maximize the economic benefit of demand response initiatives for the customer and retailer.