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Demand planning of a nearly zero energy building in a PV/grid-connected system.

• Authors : Fiorotti, Rodrigo^1,2 (AUTHOR) ; Yahyaoui, Imene^1,3 (AUTHOR) ; Rocha, Helder R.O.² (AUTHOR)

Subjects: *Building-integrated photovoltaic systems; *Photovoltaic power generation; *Energy industries

• Source: Renewable Energy Focus. Jun2023, Vol. 45, p220-233. 14p.

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A High‐Proportion Household Photovoltaic Optimal Configuration Method Based on Integrated–Distributed Energy Storage System.

• Authors : Zhang, Xinyuan (AUTHOR); Ma, Gang (AUTHOR); Pang, Yuqi (AUTHOR)

Subjects: ENERGY storage; PHOTOVOLTAIC power generation; PARTICLE swarm optimizationJAPAN

• Source: IEEJ Transactions on Electrical & Electronic Engineering. Mar2022, Vol. 17 Issue 3, p335-343. 9p.

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Applied Science & Technology Full Text (H.W. Wilson)

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Applied Science & Technology Full Text (H.W. Wilson)

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Model‐agnostic online forecasting for PV power output.

• Authors : Lee, HyunYong¹ ; Lee, Jun‐Gi¹; Kim, Nac‐Woo¹

Subjects: *Photovoltaic power generation; *Solar energy; *Energy management

• Source: IET Renewable Power Generation (Wiley-Blackwell). Nov2021, Vol. 15 Issue 15, p3539-3551. 13p.

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Applied Science & Technology Full Text (H.W. Wilson)

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Applied Science & Technology Full Text (H.W. Wilson)

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Forecasting Photovoltaic Power Generation via an IoT Network Using Nonlinear Autoregressive Neural Network.

• Authors : Rogier, John Kevin; Mohamudally, Nawaz

Subjects: PHOTOVOLTAIC power generation; POWER resources; SMART meters

• Source: Procedia Computer Science; 2020, Vol. 171, p643-650, 8p

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Research on Energy Management in Hydrogen–Electric Coupled Microgrids Based on Deep Reinforcement Learning.

• Authors : Shi, Tao; Zhou, Hangyu; Shi, Tianyu

Subjects: ARTIFICIAL neural networks; DEEP reinforcement learning; REINFORCEMENT learning

• Source: Electronics (2079-9292); Sep2024, Vol. 13 Issue 17, p3389, 18p

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Applied Science & Technology Full Text (H.W. Wilson)

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