Cited by 280Solar Radiation and PhotovoltaicsEnergy Load and Power ForecastingHydrological Forecasting Using AI

Solar Irradiance Forecasting Using Deep Neural Networks

Open access
Alzahrani, Ahmad, Shamsi, Pourya, Dagli, Cihan, Ferdowsi, Mehdi·2017·Procedia Computer Science

Abstract

Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying what form the variation should take and allow the extraction of high-level features. The DRNN is used to predict the irradiance. The data utilized in this study is real data obtained from natural resources in Canada. The simulation of this method will be compared to several common methods such as support vector regression and feedforward neural networks (FNN). The results show that deep learning neural networks can outperform all other methods, as the performance tests indicate.

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Crossref · 10.1016/j.procs.2017.09.045

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  • Many ML methods have been used to forecast electricity supply and demand.

    “To date, many ML methods have been used to forecast electricity supply and demand.”

    From Tackling climate change with machine learning (2023) · cites this paper as [19]

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  1. Gana, N. N., and D. O. Akpootu. "Angstrom type empirical correlation for estimating global solar radiation in North-Eastern Nigeria." The International Journal Of Engineering And Science 2 (2013): 58-78.

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Authors
Alzahrani, A., Shamsi, P., Dagli, C., & Ferdowsi, M.
Affiliations
Missouri University of Science and Technology (US)
Corresponding author
Alzahrani, Ahmad
Venue
Procedia Computer Science
Publisher
Elsevier BV
Published
2017
Volume
114
Pages
304-313
ISSN
1877-0509 (ISSN-L)
OpenAlex
W2763749451
Cited by
280
References
27
Citations by year
2026: 15 · 2025: 28 · 2024: 32 · 2023: 32 · 2022: 41 · 2021: 50 · 2020: 42 · 2019: 24 · 2018: 8 · 2017: 8
FWCI
10.23
Type
conference paper
Language
en
Open access
Locations
Procedia Computer Science (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Keywords
solar irradiance forecasting, deep recurrent neural networks, photovoltaic systems, renewable energy, support vector regression, feedforward neural networks, time series forecasting, long-range dependencies

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