Electrical load forecasting is of great significance to guarantee the system stability under large disturbances, and optimize the distribution of energy resources in the smart grid. Traditional prediction models, which are mainly based on time series analyzing, have been unable to fully meet the actual needs of the power system, due to their non-negligible prediction errors. To improve the forecasting precision, we successfully transform the numerical prediction problem into an image processing task, and, based on that, utilize the state-of-the-art deep learning methods, which have been widely used in computer image area, to perform the electrical load forecasting. A novel deep learning based short-term forecasting (DLSF) method is proposed in the paper. Our method can perform accurate clustering on the input data using a deep Convolutional Neural Network (CNN) model. And ultimately, another neural network with three hiddenlayers is used to predict the electric load, considering various external influencing factors, e.g. temperature, humidity, wind speed, etc. Experimental results demonstrate that the proposed DLSF method performs well in both accuracy and efficiency.