Short Term Electrical Load Combination Forecasting Model based on
Multi-dimensional Meteorological Information Spatio-Temporal Fusion and
MPA-VMD
Abstract
This paper considers the variability in the impact of multi-dimensional
meteorological information on power load in different regions. To
improve the accuracy of load forecasting in the spatial dimension, the
method of spatio-temporal fusion (SF) of multi-dimensional
meteorological information is proposed. The Copula theory is applied to
analyze the nonlinear coupling of meteorological information such as
wind speed, rainfall, temperature, and sunshine intensity from multiple
meteorological stations with the power load and to achieve
spatio-temporal fusion. In the time dimension, the core parameters of
the variational mode decomposition (VMD) are improved by the marine
predators algorithm (MPA). The weighted permutation entropy (WPE) is
used to construct the MPA-VMD fitness function for the adaptive
decomposition of the load sequence. In addition, the input sets of the
LSTM model and MPA-LSSVM model are constructed by combining each
component of the time dimension and each meteorological information of
spatial dimension to obtain the prediction results of each component.
The prediction model corresponding to each component is selected
according to the evaluation index, and reconstructed to obtain the
overall prediction results. The analysis results show that the proposed
forecasting method is better than the traditional forecasting method and
effectively improves the accuracy of power load forecasting.