Considering the high consumption rates of the non-renewable energy sources as well as their adverse climatic impacts, renewable energy has become a widespread topic of discussion. Among the available renewable resources, solar and wind are the highest contributors. However, the high influence of atmospheric parameters and higher cost involved in energy production prevent the widespread use of renewable energy among common public. The location identification for optimum energy production is also a crucial step for setting up future energy plants. In this regard, here we propose a novel strategy to compare prediction results in terms of loss made by traditional convolutional neural network (CNN) with that of spiking neural network (SNN). Though the SNNs are popularly used for vision related tasks, here we evaluate their efficacy in analyzing time series data of solar irradiance and wind speed. In summary, we provide a comprehensive discussion on SNN and their significance on energy forecasting.