Optimal Location of Distributed Generation for Loss Minimization by Application of Machine Learning

It has been observed that DG integration has the ability to improve grid sustainability and resilience. However given the complexity of the distributed generation (DG) placement issue, a more data-driven strategy is required, especially when taking into account variables like load profiles, grid features, economic considerations, and environmental restrictions. This study contributes to enhanced grid performance in the shift toward decentralized and renewable energy sources by providing insights into the accuracy of DG placement by framing the problem as a regression challenge and assessing several machine-learning techniques. In summary, this paper highlights the potential of machine learning for recognising the change in Voltage profile and losses for improving distributed generation placement in contemporary power distribution networks.