Deep learning algorithm applied to daily solar irradiation estimations

Date

2018-07-02

Author

Akbaba, Erol C.
Yüce, Emre
Akinoglu, Bulent G.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

200
views

0
downloads

Deep learning is applied in many research areas, and in many of them remarkable outcomes are attained compared to conventional methods. There are quite a number of studies also in the estimation of solar irradiation. Estimation of solar irradiation is vitally important for the design of solar energy systems. In this work, multi-layer perceptron (MLPs) method of deep learning is used to develop an estimation method for calculating horizontal daily solar irradiation and the results are compared with classical approaches. The results indicate that deep learning with little inputs can be used to estimate daily solar irradiation with a decent accuracy that is comparable to classical approaches.

Subject Keywords

ANN, Deep learning, Solar irradiation

URI

https://hdl.handle.net/11511/35905

DOI

https://doi.org/10.1109/irsec.2018.8702963

Collections

Department of Physics, Conference / Seminar

Suggestions

OpenMETU
Core

Multi-task Deep Neural Networks in Protein Function Prediction Rifaioğlu, Ahmet Süreyya; Doğan, Tunca; Martin, Maria Jesus; Atalay, Rengül; Atalay, Mehmet Volkan (2017-05-01) In recent years, deep learning algorithms have outperformed the state-of-the art methods in several areas thanks to the efficient methods for training and for preventing overfitting, advancement in computer hardware, the availability of vast amount data. The high performance of multi-task deep neural networks in drug discovery has attracted the attention to deep learning algorithms in bioinformatics area. Here, we proposed a hierarchical multi-task deep neural network architecture based on Gene Ontology (GO...
Visual Object Tracking with Autoencoder Representations Besbinar, Beril; Alatan, Abdullah Aydın (2016-05-19) Deep learning is the discipline of training computational models that are composed of multiple layers and these methods have recently improved the state of the art in many areas as a virtue of large labeled datasets, increase in the computational power of current hardware and unsupervised training methods. Although such a dataset may not be available for lots of application areas, the representations obtained by the well-designed networks that have a large representation capacity and trained with enough dat...
Deep Learning-Enabled Technologies for Bioimage Analysis Rabbi, Fazle; Dabbagh, Sajjad Rahmani; Angın, Pelin; Yetisen, Ali Kemal; Tasoglu, Savas (2022-02-01) Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of em...
Deep learning approach for laboratory mice grimace scaling Eral, Mustafa; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2016) Deep learning is extremely attractive research topic in pattern recognition and machine learning areas. Applications in speech recognition, natural language processing, and machine vision fields gained huge acceleration in performance by employing deep learning. In this thesis, deep learning is used for medical purposes in order to scale pain degree of drug stimulated mice by examining facial grimace. For this purpose each frame in the videos in the training set were scaled manually by experts according to ...
HYPERSPECTRAL CLASSIFICATION USING STACKED AUTOENCODERS WITH DEEP LEARNING Özdemir, Ataman; Cetin, C. Yasemin Yardimci (2014-06-27) In this study, stacked autoencoders which are widely utilized in deep learning research are applied to remote sensing domain for hyperspectral classification. High dimensional hyperspectral data is an excellent candidate for deep learning methods. However, there are no works in literature that focuses on such deep learning approaches for hyperspectral imagery. This study aims to fill this gap by utilizing stacked autoencoders. Experiments are conducted on the Pavia University scene. Using stacked autoencode...

Citation Formats

E. C. Akbaba, E. Yüce, and B. G. Akinoglu, “Deep learning algorithm applied to daily solar irradiation estimations,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35905.