Wind energy as one of the most promising energy alternatives brings a set of serious challenges in the operation of power systems because of the uncertain nature of wind speed. To address this problem, it is essential to establish a framework to forecast a comprehensive form of information about the wind speed. To this end, an ensemble residual regression deep network is designed to understand fully time-variant and spatial features from the historical data including wind speed and corresponding meteorological data. Then, to enhance the accuracy, a modified error-based loss function is proposed. Consequently, to provide a comprehensive form of information, a modified kernel density estimator is proposed to extract a set of probability density functions (PDFs) with a high level of accuracy and reliability. The simulation results and a comparative analysis on an actual dataset in London, U.K. demonstrate the high capability of the proposed probabilistic wind speed approach.