The authors of this work present a method that mines big media data streams from large Social Media Networks in order to discover novel correlations between objects appearing in images and electricity utilization patterns. The hypothesis of this work is that there exist correlations between what users take pictures of, and electricity utilization patterns. This work employs a Convolutional Neural Network to detect objects in 578,232 images gathered from over 15,000,000 tweets sent in the San Diego area. These objects were considered in the context of concurrent power use, on a monthly and hourly basis. The results reveal both positive and negative correlations between power use and specific objects, such as lamps (.053 hourly), dogs (−.011 hourly), horses (.422 monthly) and motorcycles (−.415, monthly).