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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:
 [1];  [1]; ORCiD logo [2]
  1. Arizona State Univ., Tempe, AZ (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1441212
Grant/Contract Number:  
AC05–76RL01830
Resource Type:
Journal Article: 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.
Inanlouganji, Alireza, Reddy, T. Agami, & Katipamula, Srinivas. Evaluation of regression and neural network models for solar forecasting over different short-term horizons. United States. doi:10.1080/23744731.2018.1464348.
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. doi:10.1080/23744731.2018.1464348. https://www.osti.gov/servlets/purl/1441212.
@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},
issn = {2374-4731},
number = 9,
volume = 24,
place = {United States},
year = {2018},
month = {4}
}

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Works referenced in this record:

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


Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques
journal, February 2000


Hybrid methodology for hourly global radiation forecasting in Mediterranean area
journal, May 2013


Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation
journal, March 2012


Stochastic modeling and forecasting of solar radiation data
journal, January 1977


Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm
journal, March 2015


Artificial intelligence techniques for photovoltaic applications: A review
journal, October 2008

  • Mellit, Adel; Kalogirou, Soteris A.
  • Progress in Energy and Combustion Science, Vol. 34, Issue 5, p. 574-632
  • DOI: 10.1016/j.pecs.2008.01.001

Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms
journal, June 2016

  • Premalatha, Neelamegam; Valan Arasu, Amirtham
  • Journal of Applied Research and Technology, Vol. 14, Issue 3
  • DOI: 10.1016/j.jart.2016.05.001

Comparative study of statistical and artificial neural network's methodologies for deriving global solar radiation from NOAA satellite images
journal, February 2012

  • Rahimikhoob, A.; Behbahani, S. M. R.; Banihabib, M. E.
  • International Journal of Climatology, Vol. 33, Issue 2
  • DOI: 10.1002/joc.3441

Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach
journal, January 2017


The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review
journal, October 2015


Review of photovoltaic power forecasting
journal, October 2016


Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review
journal, January 2008

  • Mellit, Adel
  • International Journal of Artificial Intelligence and Soft Computing, Vol. 1, Issue 1
  • DOI: 10.1504/IJAISC.2008.021264

Solar radiation estimation using artificial neural networks
journal, April 2002


Neural networks and their applications
journal, June 1994

  • Bishop, Chris M.
  • Review of Scientific Instruments, Vol. 65, Issue 6
  • DOI: 10.1063/1.1144830

Solar forecasting methods for renewable energy integration
journal, December 2013

  • Inman, Rich H.; Pedro, Hugo T. C.; Coimbra, Carlos F. M.
  • Progress in Energy and Combustion Science, Vol. 39, Issue 6
  • DOI: 10.1016/j.pecs.2013.06.002

Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models
journal, November 2013


Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN
journal, May 2011


The value of day-ahead solar power forecasting improvement
journal, May 2016


A benchmarking of machine learning techniques for solar radiation forecasting in an insular context
journal, February 2015


Solar radiation prediction using Artificial Neural Network techniques: A review
journal, May 2014


Predicting solar radiation at high resolutions: A comparison of time series forecasts
journal, March 2009


Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey
journal, July 2011