  Site-specific weather forecasts are essential to accurate prediction of power
demand and are consequently of great interest to energy operators. However,
weather forecasts from current numerical weather prediction (NWP) models lack
the fine-scale detail to capture all important characteristics of localised
real-world sites. Instead they provide weather information representing a
rectangular gridbox (usually kilometres in size). Even after post-processing
and bias correction, area-averaged information is usually not optimal for
specific sites. Prior work on site optimised forecasts has focused on linear
methods, weighted consensus averaging, time-series methods, and others. Recent
developments in machine learning (ML) have prompted increasing interest in
applying ML as a novel approach towards this problem. In this study, we
investigate the feasibility of optimising forecasts at sites by adopting the
popular machine learning model gradient boosting decision tree, supported by
the Python version of the XGBoost package. Regression trees have been trained
with historical NWP and site observations as training data, aimed at predicting
temperature and dew point at multiple site locations across Australia. We
developed a working ML framework, named 'Multi-SiteBoost' and initial testing
results show a significant improvement compared with gridded values from
bias-corrected NWP models. The improvement from XGBoost is found to be
comparable with non-ML methods reported in literature. With the insights
provided by SHapley Additive exPlanations (SHAP), this study also tests various
approaches to understand the ML predictions and increase the reliability of the
forecasts generated by ML.
