Electrical load forecasting is still a challenging open problem due to the complex and variable influences (e.g., weather and time). Although, with the recent development of IoT and smart meter technology, people have obtained the ability to record relevant information on a large scale, traditional methods struggle in analyzing such complicated relationships for their limited abilities in handling nonlinear data. In the article, we introduce an IoTbased deep learning system to automatically extract features from the captured data, and ultimately, give an accurate estimation of future load value. One significant advantage of our method is the specially designed two-step forecasting scheme, which significantly improves the forecasting precision. Also, the proposed method is able to quantitatively analyze the influences of some major factors, which is of great guiding significance to select attribute combination and deploy onboard sensors for smart grids with vast areas, variable climates, and social conventions. Simulations demonstrate that our method outperforms some existing approaches, and can be well applied in various situations.