Short-Term Load Forecasting in Smart Distribution Networks: A Systematic Review of Data Analytics, Clustering Techniques, and Forecasting Models

The operation and management of Smart Distribution Networks (SDNs) heavily relies on the Short-Term Load Forecasting (STLF) function in the context of Demand-Side Management (DSM), Distributed Energy Resource (DER) integration, and grid reliability. With the wide use of smart meters and Advanced Metering Infrastructure (AMI), it is now possible to obtain high-resolution electricity consumption data, which has opened up new opportunities for data-driven forecasting at the transformer, feeder, substation, and consumer levels. Forecasting in SDNs, however, still faces difficulties because of the variability of the loads, the heterogeneity of the data, the distributed nature of the energy resources, and the stochastic behavior of consumers. This paper provides a systematic literature review of clustering-based and data-driven STLF methodologies for Smart Distribution Systems (SDNs). According to PRISMA guidelines, publications from 2018 to March 2026 were retrieved from Google Scholar, Scopus, and Web of Science and analyzed in terms of clustering techniques, forecasting methods, data sources, and fields of application. The focus is on K-Means, K-Medoids, Hierarchical Clustering, Dynamic Time Warping (DTW), and DBSCAN, along with statistical, Machine Learning (ML), Deep Learning (DL), Federated Learning (FL), and Edge-computing forecasting frameworks. The review indicates that the clustering-based preprocessing is highly useful in the field of characterizing and forecasting load profiles. In the reviewed studies, DL models, especially LSTM, GRU and CNN models, were repeatedly found to perform better than statistical and ML baseline architectures in their respective experimental scenarios, while FL and edge-computing methods show promise for real-time and privacy-preserving forecasting. Lastly, the review presents existing problems and future research directions on how to create scalable, intelligent, and privacy-preserving forecasting systems in SDNs.