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Deep ensemble learning based probabilistic load forecasting in smart grids
پیش بینی بار احتمالی مبتنی بر یادگیری گروه عمیق در شبکه های هوشمند-2019 With the availability of fine-grained smart meter data, there has been increasing interest in using this information for ecient and
reliable energy management. In particular, accurate probabilistic load forecasting for individual consumers is critical in determining
the uncertainties in future demand with the goal of improving smart grid reliability. Compared with the aggregate loads, individual
load profiles exhibit higher irregularity and volatility and thus less predictable. To address these challenges, a novel deep ensemble
learning based probabilistic load forecasting framework is proposed to quantify the load uncertainties of individual customers.
This framework employs the profiles of dierent customer groups integrated into the understanding of the task. Specifically,
customers are clustered into separate groups based on their profiles and multitask representation learning is employed on these
groups simultaneously. This leads to a better feature learning across groups. Case studies conducted on an open access dataset from
Ireland demonstrate the eectiveness and superiority of the proposed framework Keywords: Deep ensemble learning | multitask representation learning | probabilistic load forecasting | smart grid | customer profiles |
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