Medium-term feeder load forecasting and boosting peak accuracy prediction using the PWP-XGBoost model

Medium-term feeder load forecasting plays a pivotal role in efficiently operating and planning electrical distribution systems. It provides valuable insights into future electricity demand trends, enabling utilities to make informed decisions regarding infrastructure upgrades, resource allocation, and energy management strategies. However, few studies have been done on medium-term load forecasting for the distribution network’s operational planning at the medium voltage level. Moreover, conventional load forecasting techniques mainly consider the impact of limited external factors, which is typically challenging to forecast accurately. In this work, multiple influential features have been utilized for accurate medium-term load prediction. Accurate load forecasting is paramount for efficient operation and planning in power systems. This study proposes a novel approach for medium-term feeder load forecasting that enhances peak accuracy using the Prominence-guided Weighted Peaks (PWP) in conjunction with the eXtreme Gradient Boosting (XGBoost) model. To evaluate the performance of the proposed model, we conduct experiments using real-world load data from a distribution feeder. Comparative analysis with traditional forecasting methods demonstrates the superior accuracy of the PWP-XGBoost model, particularly in predicting peak loads. Enhanced peak accuracy is crucial for utilities to effectively manage peak demand, optimize resource allocation, and ensure grid stability.