Sustainable energy is a significant power generation resource for a cleaner and CO2 free environment. Out of different renewable energies out there, wind energy is a rapidly growing sector and integrated into the power grid. However, uncertainty, stochastic, and non-stationary nature of meteorological features, on which wind power depends, makes it difficult to predict accurately. The efficiency of wind farms and the power grid is directly proportional to efficient wind power predictive analytics. This study describes a hybrid model named Powernet for improving the predicted accuracy in the field of wind power analytics. The improved hybrid model is a combination of Convolution 1 Dimensional (Conv-1D) and Bidirectional Long Short-Term Memory (BiLSTM) models. First, Conv-1D layers extract the spatial features of timestamped data sequentially. Then, the output generated by multiple convolution operations at the nested layers is embedded with BiLSTM to work on the temporal characteristics of wind power data. The nesting of spatial and temporal extractors generates a novel architecture, Powernet for wind power forecasting from raw data. The effectiveness of Powernet has been validated on the real-time wind power National Renewable Energy Laboratory dataset. Also, error and computational analysis have been conducted for short-term wind power forecasting with an ensemble of long short-term memory-based models. The comparative analysis demonstrates that the proposed model Powernet achieves better prediction than traditional deep learning standalone and hybrid models. Also, the statistical models are compared to show that the raw data need to be pre-processed when conventional models are applied. However, Powernet does not need the overhead of pre-processing for generating better predictions.