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Electricity is the most important source of energy that is exploited nowadays; it is essential for the economic development and the social stability, and this implies the need to model systems that keeps a perfect balance between supply and demand. This task depends heavily on identifying the factors that affect power consumption and improving the precision of the forecasted model. This paper presents a novel convolutional neural network (CNN) for short-term load forecasting (STLF); studies have been conducted to identify the different factors that affect the power consumption in Algeria (North Africa), and these studies helped to determine the inputs to the model. The proposed CNN uses a two-dimensional input unlike the conventional one-dimensional input used for STLF, and the results given by the CNN were compared to other artificial intelligence methods and demonstrated good results for both: one-quarter-ahead and 24-h-ahead forecast. [ABSTRACT FROM AUTHOR]