Smart energy meters allow individual residential, commercial, and industrial energy load usage to be monitored continuously with high granularity. Accurate short-term energy forecasting is essential for improving energy efficiency, reducing blackouts, and enabling smart grid control and analytics. In this paper, we survey commonly used non-linear deep learning timeseries forecasting methods for this task including long short-term memory recurrent neural networks and nonlinear autoregressive models, nonlinear autoregressive exogenous networks that also include weather data, and for completeness, MATLAB's nonlinear input-output model that only uses weather. These models look at every combination of load sequence data and weather information to identify which factors and methods are most effective at predicting short-term residential load. In this paper, the traditional nonlinear autoregressive model predicted short term load values most accurately using only energy load information with a mean square error of 7.53E-5 and a correlation coefficient of 0.995.