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Hybrid deep learning models for multivariate forecasting of global horizontal irradiation

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Abstract

Increasing photovoltaic (PV) instalments could affect the stability of the electrical grid as the PV produces weather-dependent electricity. However, prediction of the power output of the PV panels or incoming radiation could help to tackle this problem. It has been concluded within the European Actions “Weather Intelligence for Renewable Energies” framework that more research is needed on short-term energy forecasting using different models, locations and data for a complete overview of all possible scenarios around the world representing all possible meteorological conditions. On the other hand, for the Mediterranean region, there is a need for studies that cover a larger spectrum of forecasting algorithms. This study focuses on forecasting short-term GHI for Kalkanli, Northern Cyprus, while aiming to contribute to ongoing research on developing prediction models by testing different hybrid forecasting algorithms. Three different hybrid models are proposed using convolutional neural network (CNN), long short-term memory (LSTM) and support vector regression (SVR), and the proposed hybrid models are compared with the performance of stand-alone models, i.e. CNN, LSTM and SVR, for the short-term GHI estimation. We present our results with several evaluation metrics and statistical analysis. This is the first time such a study conducted for GHI prediction.

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Authors and Affiliations

  1. Sustainable Environment and Energy Systems, Middle East Technical University, Northern Cyprus Campus, 99738 Kalkanlı, Güzelyurt, Mersin 10, Turkey

    Nuray Vakitbilir & Adnan Hilal

  2. Electrical and Electronics Engineering, Center for Sustainability, Middle East Technical University, Northern Cyprus Campus, 99738 Kalkanlı, Güzelyurt, Mersin 10, Turkey

    Cem Direkoğlu

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  1. Nuray Vakitbilir
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Vakitbilir, N., Hilal, A. & Direkoğlu, C. Hybrid deep learning models for multivariate forecasting of global horizontal irradiation. Neural Comput & Applic 34, 8005–8026 (2022). https://doi.org/10.1007/s00521-022-06907-0

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