A regime-dependent artificial neural network technique for short-range solar irradiance forecasting (2022)

Abstract

Solar power can provide substantial power supply to the grid; however, it is also a highly variable energy source due to changes in weather conditions, i.e. clouds, that can cause rapid changes in solar power output. Independent systems operators (ISOs) and regional transmission organizations (RTOs) monitor the demand load and direct power generation from utilities, define operating limits and create contingency plans to balance the load with the available power generation resources. ISOs, RTOs, and utilities will require solar irradiance forecasts to effectively and efficiently balance the energy grid as the penetration of solar power increases. This study presents a cloud regime-dependent short-range solar irradiance forecasting system to provide 15-min average clearness index forecasts for 15-min, 60-min, 120-min and 180-min lead-times. A k-means algorithm identifies the cloud regime based on surface weather observations and irradiance observations. Then, Artificial Neural Networks (ANNs) are trained to predict the clearness index. This regime-dependent system makes a more accurate deterministic forecast than a global ANN or clearness index persistence and produces more accurate predictions of expected irradiance variability than assuming climatological average variability.

Original languageEnglish (US)
Pages (from-to)351-359
Number of pages9
JournalRenewable Energy
Volume89
DOIs
StatePublished - Apr 1 2016

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment

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This output contributes to the following UN Sustainable Development Goals (SDGs)

  • A regime-dependent artificial neural network technique for short-range solar irradiance forecasting (1)

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McCandless, T. C., Haupt, S. E. (2016). A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. Renewable Energy, 89, 351-359. https://doi.org/10.1016/j.renene.2015.12.030

McCandless, T. C. ; Haupt, S. E. ; Young, G. S. / A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. In: Renewable Energy. 2016 ; Vol. 89. pp. 351-359.

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author = "McCandless, {T. C.} and Haupt, {S. E.} and Young, {G. S.}",

note = "Funding Information: This material is based upon work supported by the U.S. Department of Energy under SunCast Award Number [DE-EE0006016] and the National Center for Atmospheric Research . We gratefully acknowledge all of the collaborators on the SunCast project for insightful discussions and ideas, including Seth Linden, Sheldon Drobot, Jared Lee, Julia Pearson and Tara Jensen. This project would not have been possible without the data from the Sacramento Municipal Utility District and the help from Thomas Brummet at NCAR for the data quality control and processing. Publisher Copyright: {\textcopyright} 2015 Elsevier Ltd.",

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McCandless, TC, Haupt, SE 2016, 'A regime-dependent artificial neural network technique for short-range solar irradiance forecasting', Renewable Energy, vol. 89, pp. 351-359. https://doi.org/10.1016/j.renene.2015.12.030

A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. / McCandless, T. C.; Haupt, S. E.; Young, G. S.

In: Renewable Energy, Vol. 89, 01.04.2016, p. 351-359.

Research output: Contribution to journalArticlepeer-review

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AU - Haupt, S. E.

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N1 - Funding Information:This material is based upon work supported by the U.S. Department of Energy under SunCast Award Number [DE-EE0006016] and the National Center for Atmospheric Research . We gratefully acknowledge all of the collaborators on the SunCast project for insightful discussions and ideas, including Seth Linden, Sheldon Drobot, Jared Lee, Julia Pearson and Tara Jensen. This project would not have been possible without the data from the Sacramento Municipal Utility District and the help from Thomas Brummet at NCAR for the data quality control and processing.Publisher Copyright:© 2015 Elsevier Ltd.

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PY - 2016/4/1

Y1 - 2016/4/1

N2 - Solar power can provide substantial power supply to the grid; however, it is also a highly variable energy source due to changes in weather conditions, i.e. clouds, that can cause rapid changes in solar power output. Independent systems operators (ISOs) and regional transmission organizations (RTOs) monitor the demand load and direct power generation from utilities, define operating limits and create contingency plans to balance the load with the available power generation resources. ISOs, RTOs, and utilities will require solar irradiance forecasts to effectively and efficiently balance the energy grid as the penetration of solar power increases. This study presents a cloud regime-dependent short-range solar irradiance forecasting system to provide 15-min average clearness index forecasts for 15-min, 60-min, 120-min and 180-min lead-times. A k-means algorithm identifies the cloud regime based on surface weather observations and irradiance observations. Then, Artificial Neural Networks (ANNs) are trained to predict the clearness index. This regime-dependent system makes a more accurate deterministic forecast than a global ANN or clearness index persistence and produces more accurate predictions of expected irradiance variability than assuming climatological average variability.

AB - Solar power can provide substantial power supply to the grid; however, it is also a highly variable energy source due to changes in weather conditions, i.e. clouds, that can cause rapid changes in solar power output. Independent systems operators (ISOs) and regional transmission organizations (RTOs) monitor the demand load and direct power generation from utilities, define operating limits and create contingency plans to balance the load with the available power generation resources. ISOs, RTOs, and utilities will require solar irradiance forecasts to effectively and efficiently balance the energy grid as the penetration of solar power increases. This study presents a cloud regime-dependent short-range solar irradiance forecasting system to provide 15-min average clearness index forecasts for 15-min, 60-min, 120-min and 180-min lead-times. A k-means algorithm identifies the cloud regime based on surface weather observations and irradiance observations. Then, Artificial Neural Networks (ANNs) are trained to predict the clearness index. This regime-dependent system makes a more accurate deterministic forecast than a global ANN or clearness index persistence and produces more accurate predictions of expected irradiance variability than assuming climatological average variability.

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McCandless TC, Haupt SE, Young GS. A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. Renewable Energy. 2016 Apr 1;89:351-359. https://doi.org/10.1016/j.renene.2015.12.030

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