Multi-type load forecasting model based on random forest and density clustering with the influence of noise and load patterns

Load forecasting (LF) models are essential for various smart grid applications, and their accuracy heavily relies on the quality of input load data and load types. Previous LF studies have ignored noise loads due to tampering, transmission failures, etc., and have not considered the fusion of different types of loads, both of which have an impact on load forecasting accuracy. To address these issues, this study introduces a novel multi-type load forecasting model named MLF-RFDC, based on random forest and density clustering, that enjoys three-fold ideas: (1) it treats load data from each electrical activity as an independent data matrix, capturing variation patterns unique to each load type; (2) it identifies and corrects noisy entries in each data matrix using a low-rank clustering approach, highlighting noises as outliers and restoring them through latent factor analysis; and (3) it combines noise-free data matrices from all load types to enhance LF accuracy from an ensemble perspective. Extensive experiments conducted on ten benchmark datasets and three real-world load datasets demonstrate that our proposed algorithm outperforms 11 state-of-the-art models. Specifically, the performance results are remarkable: (1) the anomaly data detection accuracy is enhanced by up to 15.66%; (2) the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) for anomaly data recovery show significant improvements of tens of times; and (3) the MAE, RMSE, MAPE, and R-squared ( R2 ) for load forecasting are the most favorable.