Load forecasting is the basis not only of power system stable and safe operation, but also of power demand side intelligent electricity management. Short-term correlation analysis can be used to mine the electricity consumption of a period of time. The analysis of similar electricity consumption can improve the effect of load forecasting. Therefore, this paper proposes a load forecasting method based on short-time correlation clustering. First, a method is proposed to analyze the correlation matrix of electricity sequences, and then eliminate the effects of noise information of the correlation matrix. Then, clustering to identify groups of loads with similar load consumption behavior based on fuzzy C-means clustering algorithm, after each load is assigned to a special cluster, we sum the load data in the group to obtain the partial system load. And then forecast this partial system load at each group based on artificial neural network. The partial system load forecasts are summed to obtain total system load forecast. Finally, the case studies with instance data verify the load clustering based on short-term correlation analysis for power sequences can improve load forecasting..
