With continuous advancements in load-side resources such as distributed
photovoltaic systems, electric vehicles, and virtual power plants, the
low-carbon and sustainable development attributes of power systems have been
significantly enhanced. Meanwhile, the coupling intensity between sustainable
power systems and meteorological conditions has been further consolidated.
Considerable impacts are exerted by weather variations, particularly extreme
weather events, on the dispatching and operation of sustainable power systems.
Accurate load forecasting is critical for enabling sustainable power systems
operators to optimize power generation strategy, ensuring supply stability and
resilience against extreme weather-induced disruptions. However, the intrinsic
non-stationarity and volatility of extreme weather events present significant
challenges to conventional forecasting approaches. Herein, we introduce a hybrid
algorithm integrating Newton–Raphson-based optimizer (NRBO) with extreme
gradient boosting (XGBoost) to enhance short-term load predictions under such
conditions. The model uses optimally selected meteorological and load features
as inputs, while NRBO systematically tunes XGBoost’s hyper-parameters to
maximize performance. Evaluated on an Irish dataset, the proposed framework is
quantitatively compared against five baseline models, including traditional
decision trees and neural networks. The case studies show that the mean absolute
percentage error (MAPE) of the proposed model is 2.57%, which is the lowest
among these decision tree and neural network algorithms.
