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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 data sets are acquired using model of a smart household performing optimal appliance scheduling considering an hourlyvarying price tariff scheme. Then, an approach based on artificial neural network (ANN) and wavelet transform (WT) is employed for the forecasting of the response of residential loads to different price signals. From the literature perspective this study the contribution of this study is the consideration of the DR effect on load pattern forecasting, being a very 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, or to effectively plan new ones.Index Terms-Demand response, home energy management, electric vehicles, smart household, load forecasting, artificial neural networks, wavelet transform. NOMENCLATUREThe main nomenclature used throughout the paper is stated below. Other symbols and abbreviations are defined where they first appear. A. Indicesperiod of the day index in time units [h or min]. ). B. Parameters approximate series at level j. energy requirement of smart-appliance [kWh]. charging efficiency of the EV. charging rate of the EV [kW per time interval]. detail series at level j. discharging efficiency of the EV. discharging rate of the EV [kW per time interval]. period in which the operation of smart-appliance should be finished. maximum power that can be drawn from the grid [kW]. inelastic power demand of the household [kW]. rated power of smart-appliance [kWh]. period in which the operation of smart-appliance should be started. , initial state-of-energy of the EV [kWh]. , maximum allowed state-of-energy of the EV [kWh]. , minimum allowed state-of-energy of the EV [kWh]. arrival time of the EV. departure time of the EV. duration of operation of smart-appliance . , period at which EV should be fully charged. , period at which EV should be fully discharged, if applicable. time step duration [h]. price of energy bought from the grid [cents/kWh]. C. Variables , power of smart-appliance during period [kW]. , EV charging power [kW]. , EV discharging power [kW]. , power used to satisfy household load from the EV [kW]. power supplied by the grid [kW]. state-of-energy of the EV [kWh]. , binary variable -1 if smart-appliance is ON during period , else 0. binary variable -1 if EV is charging during period t, 0 else. , binary variable -1 if smart-appliance sta...

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Integration of renewable energy resources into the power grid is essential in achieving the envisioned sustainable energy future. Stochasticity and intermittency characteristics of renewable energies, however, present challenges for integrating these resources into the existing grid in a large scale. Reliable renewable energy integration is facilitated by accurate wind forecasts. In this paper, we propose a novel wind speed forecasting method which first utilizes Wavelet Transform (WT) for decomposition of the wind speed data into more stationary components and then uses a spatio-temporal model on each sub-series for incorporating both temporal and spatial information. The proposed spatio-temporal forecasting approach on each sub-series is based on the assumption that there usually exists an intrinsic low-dimensional structure between time series data in a collection of meteorological stations. Our approach is inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms. Based on detailed case studies, we show that the proposed approach based on exploiting the sparsity of correlations between a large set of meteorological stations and decomposing time series for higher-accuracy forecasts considerably improve the short-term forecasts compared to the temporal and spatio-temporal benchmark methods.