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The introduction of a deregulated power system market and development of smart-‐metering technologies in Sweden, bring new opportunities for fully exploiting its power system efficiency and reliability, such as price-‐based demand response (DR) programs at a large scale for household, commercial and industrial users. The deployments of these DR programs require, however, very accurate demand forecasting models. The traditional approach of obtaining the total energy use and peak demand does not offer the required detailed information. This article reviews several methodologies for forecasting electricity consumption from a bottom-‐up perspective in order to define the required parameters and structure for obtaining an energy model. This model will finally include energy usage data, behavioural parameters obtained from a survey conducted with 5 000 end-‐users in different Swedish distribution system operators’ areas, and physical conditions for the facilities (internal/external temperatures and insulation materials). This information is provided from previous research studies performed at Mälardalen University and Swedish electric utilities companies.

The obtained model should be able to adjust its parameters dynamically in order to simulate several demand-‐response scenarios based on four different strategies: time of use pricing, use of curtailable/interruptible rates, imposition of penalties for usage beyond predetermined levels, and real time pricing.