Utilization of machine learning algorithms in timeseries data analysis is crucial to effective decision making in today's dynamic and competitive environment. One data type of growing interest is the electricity consumer load profile (LP) data. Owing to advances in the smart grid, immense amount of LP data became available to policymakers as potential to improving the electricity sector. Due to the growing size and volatile nature of LP data, development and evaluation of clustering approaches has been of high demand in recent energy research, whereas the classification techniques receive less attention. This study is the first to address the effect of LP time-series data temporal dimension on the classification performance using the most popular classification algorithms in machine learning including decision trees, support vector machines (SVM), discriminant analysis, and ensemble methods. Results indicate a decline in the classification accuracy as the temporal dimension increases. Accordingly, this study proposes a fuzzy classification heuristicbased method inspired by the genetic algorithm (GA) which proves to maintain robustness against high temporal dimensions. The results are assessed using real data from industrial consumers with 420 daily LPs and 93 weekly LPs.