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Electricity is critical for industrial and economic advancement, as well as a driving force for sustainable development. In turn, reducing energy consumption for sustainability and both tracking and managing energy efficiently have become critical challenges. In this research, we analyzed electricity consumption from the perspective of load profiling, which charts variations in electrical load during a specified period in order to track energy consumption of an annealing furnace in a co-operating steel forging plant. We made a preliminary proposal to use a symbolic time-series data mining framework for electricity consumption analysis. First, we adopted a piecewise aggregate approximation (PAA) approach to perform dimension reduction. Then, we refined the distance measure of the symbolic aggregate approximation (SAX) algorithm. SAX is a symbolic representation of time-series for dimensionality reduction and indexing with a lower-bounding distance measure. Our experimental results showed that the dimension reduction method known as PAA can better detect the state of the annealing furnace compared to the fixed feature point (FFP) method. In addition, the refined lower-bounding distance measure proved to be better than the traditional measure for calculating the similarity between energy load profiles. The results can help the plant conduct further normal and abnormal electricity pattern detection.