Diversity Factor Prediction for Distribution Feeders with Interpretable Machine Learning Algorithms

The maximum diversified demand is an important factor to consider when utilities design new distribution systems. To estimate the maximum diversified demand, engineers need to make an estimate of the diversity factor (DF). In practice, electricity utility companies usually estimate the DF using DF tables, in which the DF changes with the number of customers. However, besides the number of customers, DF also depends on many other factors, such as customer type, weather, demographics, and socioeconomic conditions. Ignoring these factors, DF tables have limited accuracy. In addition, engineers cannot interpret or understand how various factors affect the DF. In this paper, by leveraging supervised machine learning algorithms, we build comprehensive DF prediction models that take a variety of factors into account. These models show high prediction accuracy and interpretabilty when applied to real-world distribution feeders. Using the interpretation method called SHapley Additive exPlanations, we quantify the importance of different features in determining DFs. Finally, we offer more insights into how various factors affect DFs.