Abstract: Forecasting algorithms are a valuable mechanism to aid in the prediction of future prices. Although various black-box modelling techniques have been applied to variations of this problem, we focus on the use of transparent models to enable understanding and interpretation of the developed model. We utilize a Nonlinear AutoRegressive Moving Average model with eXogenous input(NARMAX) for electricity price forecasting using multiple input factors. Energy data from a 14-week period in 2017 were analyzed to determine whether a NARMAX model could accurately predict day-ahead electricity prices and to check which input factors in the model were most significant. The model considered the closely correlated lags and included 13 input factors. There were two models developed in order to determine which variables played an important role in predicting future prices. Experimental results indicate that previous price, demand, gas, coal, and nuclear are the most significant factors that influence electricity prices. Gas was the highest weighted factor for both developed models. Previous price yielded the biggest Error Reduction Ratio(ERR), but when not included in the model, demand generated the biggest ERR value. To summarize a NARMAX model with an input regression lag of one and previous price included generates the best day-ahead forecast of electricity prices.