###### Abstract  
Site-specific weather forecasts are essential for 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, and time-series methods, among 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 boosted decision tree\", supported by the XGBoost package (v.1.7.3) in the Python language. 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 test results show a significant improvement compared with gridded values from bias-corrected NWP models. The improvement from XGBoost (0.1-0.6 \({}^{\circ}\)C, 4-27% improvement in temperature) is found to be comparable with non-ML methods reported in the 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. weather forecast; gradient boosting decision tree; machine learning; XGBoost; NWP post-processing; SHAP 2024 14  
In addition to conventional statistical methods, various machine learning (ML) models have also been experimented with for the task of site-specific weather forecasting. Site-specific weather forecasts are optimised for a specific individual weather-observing station. The term 'local' weather forecast may refer to a site-specific forecast or to a small 'local' geographic area such as a suburb. Some of the existing studies use time series of site observation data as the only input to generate forecasts for a single time step or multiple time steps. For example, XGBoost models have been applied to predict solar irradiance [6]; Long Short-Term Memory (LSTM) and its variations such as transductive LSTM [7] and convolutional LSTM [8] have been used for temperature forecasts. These forecast models take site measurements of multiple relevant variables as input and formulate a multi-variate training dataset. Apart from purely observation data-driven models, ML can also be used as a post-processing approach for NWP--one which generates better local forecasts than NWP grid values. These models ingest both past NWP forecast and observation data as input, so the trained model can identify the systematic bias within the NWP model for the sites and generate a more accurate forecast once the latest NWP data becomes available [9; 10; 11]. Among the various ML models tested in the literature, XGBoost [12] stands out for its computational efficiency, robustness and accuracy over alternative models. Previous studies have shown that XGBoost delivers better accuracy on tabular data, a data form often encountered in site-based forecasting. [PERSON] et al. [13] argues that the better performance of tree-based models (XGBoost, Random Forest, etc.) as compared to deep neural networks may be attributed to their robustness towards uninformative features and the fact that XGBoost does not suffer from the inductive bias toward smooth solutions as is the case with deep neural networks.