Multifactor and Multiscale Method for Power Load Forecasting

In the era of big data, various factors (particularly meteorological factors) have been considered in power load prediction, and the result shows a clear discrepancy in timescales. To capture the complicated multiscale relationship between power load and related factors, a novel multifactor and multiscale method is proposed for power load forecasting. Three primary steps are implemented: (1) multifactor analysis to select predictive factors via statistical tests; (2) multiscale analysis to extract scale-aligned components via multivariate empirical mode decomposition; and (3) power load prediction, including individual prediction at each timescale and ensemble prediction across different timescales. The empirical study focuses on the power load of Nanyang and indicates that the proposed multifactor and multiscale learning paradigms statistically outperform their corresponding original techniques (without multifactor and multiscale analysis) and semi-improved variants (with either multifactor or multiscale analysis) in terms of prediction accuracy.