A Novel Hybrid Method for Short-Term Probabilistic Load Forecasting in Distribution Networks

In recent decades, the evolution of loads and distributed energy resources has added great complexity and uncertainty to distribution networks. In such a context, a reliable characterization of the uncertainty associated with prediction is fundamental to the wide range of the newly emerging applications in the low-voltage level grid. In this regard, this paper proposes a novel method to solve the short-term probabilistic load forecasting (STPLF) problem in distribution networks in which the loads are usually too volatile to be forecasted accurately. The approach developed employs a Dirichlet process mixture model (DPMM) to handle the uncertainty of load patterns, which is inferred by a Markov chain Monte Carlo (MCMC)-based method. Thereafter, the DPMM representation of the load patterns is combined with a tree-based ensemble learning method to address the STPLF by solving a classification problem. The final result is averaged over all MCMC samples. The proposed technique is compared to selected benchmark methods at different aggregation levels using smart meter datasets collected from customers located in Ireland as well as the City of Saskatoon, SK, Canada. The results obtained demonstrate that the proposed approach outperforms the benchmark methods in STPLF at the given aggregation levels.