Photovoltaic and solar power forecasting for smart grid energy management

By Can Wan, Jian Zhao, Yonghua Song, Zhao Xu, Jin Lin, Zechun Hu. 2015

555 citations recorded by the source corpus.

Due to the challenge of climate and energy crisis, renewable energy generation including solar generation has experienced significant growth. Increasingly high penetration level of photovoltaic (PV) generation arises in smart grid. Solar power is intermittent and variable, as the solar source at the ground level is highly dependent on cloud cover variability, atmospheric aerosol levels, and other atmosphere parameters. The inherent variability of large-scale solar generation introduces significant challenges to smart grid energy management. Accurate forecasting of solar power/irradiance is critical to secure economic operation of the smart grid. This paper provides a comprehensive review of the theoretical forecasting methodologies for both solar resource and PV power. Applications of solar forecasting in energy management of smart grid are also investigated in detail.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/ee6728ba0cedd2f4759dfcf6c1d459798fafcc5d

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In Artificial Intelligence

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Citations in the corpus

Review of photovoltaic power forecasting (2016, background): “Other more recent reviews are only focused on a specific aspect of forecasting, such as ensemble forecasting (Ren et al., 2015) or different forecasting techniques (Wan et al., 2015).”
Tackling Climate Change with Machine Learning (2019, methodology): “For instance, since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting (§7) and in hybrid physics-plus-ML modeling techniques [33–35].”
Accurate photovoltaic power forecasting models using deep LSTM-RNN (2017, methodology): “These methods can be classiﬁed into four categories: (1) statistical methods, (2) artiﬁcial intelligence methods, (3) physical methods, and (4) hybrid methods [1, 51].”
Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast (2020, methodology): “Statistical models use historical data of PV power generation, whereas physical models utilize satellite imagery sources [2].”