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In smart grid, precise and accurate electricity load forecasting is one of the most challenging tasks. It is due to the high volatile, non-stationary and non-linear behavior of electricity load data. In this paper, a Deep Convolution Neural Network (DCNN) model is proposed to forecast the electricity load for each day of the week of Victoria (Australia). To forecast the electricity load for one day of the week, we analyzed the electricity load data consumed on the same day for the previous three months. To show the usefulness of our proposed scheme, comparison is made with the state of the art forecasting models namely recurrent neural network, extreme learning machine, CNN and auto regressive integrated moving average. Results show that the proposed DCNN has the lowest mean absolute percentage error, mean absolute error and root mean square error of 2.1%, 138.771 and 116.417.