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This paper presents a meta-learning based, automatic distribution system loadforecasting model selection framework. The framework includes the followingprocesses: feature extraction, candidate model labeling, offline training, andonline model recommendation. Using user load forecasting needs as inputfeatures, multiple meta-learners are used to rank the available load forecastmodels based on their forecasting accuracy. Then, a scoring-voting mechanismweights recommendations from each meta-leaner to make the finalrecommendations. Heterogeneous load forecasting tasks with different temporaland technical requirements at different load aggregation levels are set up totrain, validate, and test the performance of the proposed framework. Simulationresults demonstrate that the performance of the meta-learning based approach issatisfactory in both seen and unseen forecasting tasks.

A Meta-learning based Distribution System Load Forecasting Model Selection Framework