Reactive power estimation for large-scale customers using machine learning

Reactive power plays a vital role in the stability and efficiency of electrical systems, yet it is often overlooked in favour of active power in grid management practices. Standard power factors are used which lead to inefficient grid usage at most locations and possible unnoticed reliability risks at a few exceptional spots. Here, we investigate the challenges of estimating reactive power for large-scale customers where direct measurements are limited or unavailable. Using a dataset comprising fifteen-minute resolution measurements of active and reactive power for approximately 3,000 Dutch large customers, we developed machine learning models with the aim to accurately estimate reactive power. Traditional methods, which rely on fixed power factor assumptions, often result in inefficient and conservative grid utilization. By integrating customer metadata and monthly reactive power totals, we investigated the use of different approaches, achieving a fourfold reduction in estimation error compared to standard methods. With a model using LightGBM the mean absolute error decreases 13% further. These findings highlight the potential of machine learning to improve reactive power estimation, optimize grid usage and enhance grid reliability.