A Meta-learning based Distribution System Load Forecasting Model Selection Framework
This work addresses the challenge of model selection for load forecasting in distribution systems, which is incremental as it applies existing meta-learning techniques to a specific domain.
The paper tackles the problem of selecting the best load forecasting model for distribution systems by proposing a meta-learning framework that automatically recommends models based on user needs and forecasting accuracy, with simulation results showing satisfactory performance in both seen and unseen tasks.
This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. The framework includes the following processes: feature extraction, candidate model labeling, offline training, and online model recommendation. Using user load forecasting needs as input features, multiple meta-learners are used to rank the available load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism weights recommendations from each meta-leaner to make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.