Please use this identifier to cite or link to this item: https://doi.org/10.1049/iet-rpg.2019.1227
Title: Solar irradiance resource and forecasting: a comprehensive review
Authors: Kumar, Dhivya Sampath 
Yagli, Gokhan Mert 
Kashyap, Monika
Srinivasan, Dipti 
Keywords: Science & Technology
Technology
Green & Sustainable Science & Technology
Energy & Fuels
Engineering, Electrical & Electronic
Science & Technology - Other Topics
Engineering
solar power stations
wireless sensor networks
sunlight
solar power
atmospheric techniques
power grids
weather forecasting
photovoltaic power systems
reviews
numerical weather prediction
sensor-network
power grid
photovoltaic energy
energy sources
solar energy
power supply
solar forecasting
power demand
solar irradiance resource
cloud-image based methodologies
GLOBAL HORIZONTAL IRRADIANCE
NEURAL-NETWORK
MONITORING NETWORK
POWER PRODUCTION
CLOUD DETECTION
SKY-IMAGER
RADIATION
SATELLITE
MODEL
PERFORMANCE
Issue Date: 27-Jul-2020
Publisher: INST ENGINEERING TECHNOLOGY-IET
Citation: Kumar, Dhivya Sampath, Yagli, Gokhan Mert, Kashyap, Monika, Srinivasan, Dipti (2020-07-27). Solar irradiance resource and forecasting: a comprehensive review. IET RENEWABLE POWER GENERATION 14 (10) : 1641-1656. ScholarBank@NUS Repository. https://doi.org/10.1049/iet-rpg.2019.1227
Abstract: With the increase in demand for energy, penetration of alternative sources of energy in the power grid has increased. Photovoltaic (PV) energy is the most common and popular form of energy sources which is widely integrated into the existing grid. As solar energy is intermittent in nature, to ensure uninterrupted and reliable power supply to the prosumers, it is essential to forecast the solar irradiance. Accurate solar forecasting is necessary to facilitate large-scale modelling and deployment of PV plants without disrupting the quality and reliability of the power grid as well as to manage the power demand and supply. There are various methods to predict the solar irradiance such as numerical weather prediction methods, satellite-based approaches, cloud-image based methodologies, data-driven methods, and sensor-network based approaches. This study gives an overall review of the different resources and methods used for forecasting solar irradiance in different time horizons and also gives an extensive review of the sensor networks that are used for determining solar irradiance. The various error metrics and accessible data sets available for the sensor networks are also discussed that can be used for validation purposes.
Source Title: IET RENEWABLE POWER GENERATION
URI: https://scholarbank.nus.edu.sg/handle/10635/234277
ISSN: 1752-1416
1752-1424
DOI: 10.1049/iet-rpg.2019.1227
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