This study proposes a model for the prediction of smart household load demand influenced by a dynamic pricing demand response (DR) program. Price-based DR programs have a considerable impact on household demand pattern due to the expected choice of customers or their home energy management systems (HEMSs) to use more energy in low price periods in order to reduce their electricity procurement cost. Many studies in the literature have dealt with power prediction, but the authors are prior in the field attempting to include the impact of different DR strategies on load demand prediction of smart households. The proposed methodology is expected to be valuable for utilities, retailers, aggregators, etc., in order to evaluate the success of their price-based DR strategies and predict adverse effects such as power peaks in normally off-peak periods and stress of infrastructure.