Publication Date:
2023-01-09
Description:
This work presents an innovative short to mid-term forecasting
model that analyzes nonlinear complex spatial and temporal
dynamics in energy networks under demand and supply balance constraints
using Network Nonlinear Time Series (TS) and Mathematical
Programming (MP) approach. We address three challenges simultaneously,
namely, the adjacency matrix is unknown; the total amount in the
network has to be balanced; dependence is unnecessarily linear. We use
a nonparametric approach to handle the nonlinearity and estimate the
adjacency matrix under the sparsity assumption. The estimation is conducted
with the Mathematical Optimisation method. We illustrate the
accuracy and effectiveness of the model on the example of the natural gas
transmission network of one of the largest transmission system operators
(TSOs) in Germany, Open Grid Europe. The obtained results show that,
especially for shorter forecasting horizons, proposed method outperforms
all considered benchmark models, improving the avarage nMAPE for
5.1% and average RMSE for 79.6% compared to the second-best model.
The model is capable to capture the nonlinear dependencies in the complex
spatial-temporal network dynamics and benefits from both sparsity
assumption and the demand and supply balance constraint.
Language:
English
Type:
article
,
doc-type:article
