Evaluation of regression and neural network models for solar forecasting over different short-term horizons

Inanlouganji, Alireza; Reddy, T. Agami; Katipamula, Srinivas

doi:10.1080/23744731.2018.1464348

Title: Evaluation of regression and neural network models for solar forecasting over different short-term horizons

Abstract

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar photovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for different forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with different climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term fluctuations, different variants of the lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an artificial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict different short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Lastly, the effect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloudmore » cover data.« less

Authors:

Inanlouganji, Alireza ^[1]; Reddy, T. Agami ^[1];

^[2]

Arizona State Univ., Tempe, AZ (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Publication Date:: Fri Apr 13 00:00:00 EDT 2018

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1441212

Grant/Contract Number:: AC05–76RL01830

Resource Type:: Accepted Manuscript

Journal Name:: Science and Technology for the Built Environment

Additional Journal Information:: Journal Volume: 24; Journal Issue: 9; Journal ID: ISSN 2374-4731

Publisher:: Taylor & Francis

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY

Citation Formats


                    Inanlouganji, Alireza, Reddy, T. Agami, and Katipamula, Srinivas. Evaluation of regression and neural network models for solar forecasting over different short-term horizons.  United States: N. p., 2018. 
Web.  doi:10.1080/23744731.2018.1464348.

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                    Inanlouganji, Alireza, Reddy, T. Agami, & Katipamula, Srinivas. Evaluation of regression and neural network models for solar forecasting over different short-term horizons.  United States.  https://doi.org/10.1080/23744731.2018.1464348

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                    Inanlouganji, Alireza, Reddy, T. Agami, and Katipamula, Srinivas. Fri .  
"Evaluation of regression and neural network models for solar forecasting over different short-term horizons".  United States.  https://doi.org/10.1080/23744731.2018.1464348.  https://www.osti.gov/servlets/purl/1441212.

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@article{osti_1441212,

  title        = {Evaluation of regression and neural network models for solar forecasting over different short-term horizons},

  author       = {Inanlouganji, Alireza and Reddy, T. Agami and Katipamula, Srinivas},

  abstractNote = {Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar photovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for different forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with different climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term fluctuations, different variants of the lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an artificial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict different short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Lastly, the effect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloud cover data.},

  doi          = {10.1080/23744731.2018.1464348},

  journal      = {Science and Technology for the Built Environment},

  number       = 9,

  volume       = 24,

  place        = {United States},

  year         = {Fri Apr 13 00:00:00 EDT 2018},

  month        = {Fri Apr 13 00:00:00 EDT 2018}

}

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Similar Records

Title: Evaluation of regression and neural network models for solar forecasting over different short-term horizons

Abstract

Citation Formats

Hourly global solar irradiation forecasting for New Zealand journal, December 2015

Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe journal, August 2013

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Hourly global solar irradiation forecasting for New Zealand
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The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data
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Stochastic modeling and forecasting of solar radiation data
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Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms
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Comparative study of statistical and artificial neural network's methodologies for deriving global solar radiation from NOAA satellite images
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Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach
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Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review
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