The recent interest in the smart grid vision and the technological advancement in the communication and control infrastructure enable several smart applications at different levels of the power grid structure, while specific importance is given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the impact of price-based DR strategies on smart household load pattern variations is assessed. The household load datasets are acquired using model of a smart household performing optimal appliance scheduling considering an hourly varying price tariff scheme. Then, an approach based on artificial neural networks (ANN) and wavelet transform (WT) is employed for the forecasting of the response of residential loads to different price signals. From the literature perspective, the contribution of this study is the consideration of the DR effect on load pattern forecasting, being a useful tool for market participants such as aggregators in pool-based market structures, or for load serving entities to investigate potential change requirements in existing DR strategies, and effectively plan new ones.