 Deep ensemble learning based probabilistic load forecasting in smart gridsYandong Yang, Weijun Hong and Shufang Li Energy, 2019, vol. 189, issue C Abstract: With the availability of fine-grained smart meter data, there has been increasing interest in using this information for efficient 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 different 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 effectiveness and superiority of the proposed framework. Keywords: deep ensemble learning; Multitask representation learning; Probabilistic load forecasting; Smart grid; Customer profiles (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:189:y:2019:i:c:s0360544219320195 DOI: 10.1016/j.energy.2019.116324 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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