论文标题
基于元学习的分配系统负载预测模型选择框架
A Meta-learning based Distribution System Load Forecasting Model Selection Framework
论文作者
论文摘要
本文介绍了基于元学习的自动分配系统负载预测模型选择框架。该框架包括以下过程：功能提取，候选模型标签，离线培训和在线模型建议。使用用户负载预测需求作为输入功能，使用多个元学习者根据其预测精度对可用的负载预测模型进行排名。然后，一个评分投票机构加权每个元素的建议，以提出最终建议。在不同的负载聚合级别设置了具有不同时间和技术要求的异质负载预测任务，以训练，验证和测试所提出的框架的性能。仿真结果表明，在可见和看不见的预测任务中，基于元学习的方法的性能都是令人满意的。
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.
