2022
DOI: 10.3389/fenrg.2022.852520
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A Learning-to-Rank-Based Investment Portfolio Optimization Framework for Smart Grid Planning

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Cited by 4 publications
(2 citation statements)
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“…Power grid infrastructure planning schemes are usually affected by various investment risks in the construction duration, such as financial risks (Ammar and Eling, 2015;Zhao et al, 2022), power demand changes (Li et al, 2022;Yang et al, 2022), and extreme climates (Cao et al, 2022). Due to these investment risks, the actual completion date of power grid infrastructure projects may be delayed, and thus the electricity demand for load growth and renewable energy integration cannot be well satisfied (Dang et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…Power grid infrastructure planning schemes are usually affected by various investment risks in the construction duration, such as financial risks (Ammar and Eling, 2015;Zhao et al, 2022), power demand changes (Li et al, 2022;Yang et al, 2022), and extreme climates (Cao et al, 2022). Due to these investment risks, the actual completion date of power grid infrastructure projects may be delayed, and thus the electricity demand for load growth and renewable energy integration cannot be well satisfied (Dang et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…FastAP was proposed in 2019, which has low complexity and is suitable for stochastic gradient descent [21]. A learning-to-rank-based investment portfolio optimization framework was proposed in [22] for smart grid planning, and a machine learning-driven deduction prediction methodology in [23] was proposed for power grid planning.…”
Section: Introductionmentioning
confidence: 99%