Online 24-h solar power forecasting based on weather type classification using artificial neural network

By Changsong Chen, Shanxu Duan, Tao Cai, Bangyin Liu. 2011

569 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/48842c07c4aae3f5060b3d6f8f11f844a8cba4f0

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In Artificial Intelligence

In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction (2016, methodology): “The methodologies for solar power forecasting can be classified as one- or two-stage approaches (Chen et al. 2011).”
History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining (2018, background): “For instance, Chen et al. (2017) collected more than 400,000 Google Scholar profiles across various disciplines and analyzed the demography of these scholars. A co-authorship network was built to study the collaboration among authors and its resulting link to citation metrics. It was found that the ”
Solar Power Prediction Using Interval Type-2 TSK Modeling (2013, background): “Recently, a self-organized map was proposed to predict the solar power generation [10].”
Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting (2019, methodology): “also adopted SOM to classify the local weather type of 24h ahead provided by the online meteorological services [22].”
Review of photovoltaic power forecasting (2016, methodology): “Chen et al. (2011) developed an online forecasting model for 24 h ahead using a Self Organized Map (SOM) to classify weather (sunny, cloudy and rainy) in accordance to meteorological variables (solar irradiance, total cloud amount and low cloud amount). Thus, the ANN-RBFN could be better trained. Th”
Cloud-tracking methodology for intra-hour DNI forecasting (2014, background): “…use a coarse spatial resolution (several tens of km2) but recently higher resolution evaluations reaching 3 km2 (Benjamin et al., 2010; Lara-Fanego et al., 2012) and including Artificial Neural Network (ANN) model processing (Marquez and Coimbra, 2011; Chen et al., 2011) have been carried out.”
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, unknown): “[75].”
Weather Forecasting Error in Solar Energy Forecasting (2017, methodology): “[16] proposed an artificial intelligent based technique for forecasting solar power which required the past power measurement, solar irradiance forecast, humidity and temperature as inputs.”