Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis
Date
2020-01-01
Author
Özkan, Mehmet Barış
Küçük, Dilek
Buhan, Serkan
Demirci, Turan
Karagöz, Pınar
Metadata
Show full item record
Item Usage Stats
328
views
0
downloads
Cite This
Intelligent data analysis techniques such as data mining or statistical/machine learning algorithms are applied to diverse domains, including energy informatics. These techniques have been successfully employed in order to solve different problems within the energy domain, particularly forecasting problems such as renewable energy and energy consumption forecasts. This chapter elaborates the use of intelligent data analysis techniques for the facilitation of renewable energy monitoring and forecast. First, a review of the literature is presented on systems and forecasting approaches applied to the renewable energy domain. Next, a generic and large-scale renewable energy monitoring and forecast system based on intelligent data analysis is described. Finally, a genuine implementation of this system for wind energy is presented as a case study, together with its performance analysis results. This chapter stands as a significant reference for renewable energy informatics, considering the provided conceptual and applied system descriptions, heavily based on smart computing techniques.
URI
https://hdl.handle.net/11511/72829
Relation
Handbook of Research on Smart Computing for Renewable Energy and Agro-Engineering
Collections
Department of Computer Engineering, Book / Book chapter
Suggestions
OpenMETU
Core
Activity Learning from Lifelogging Images
Belli, Kader; Akbaş, Emre; Yazıcı, Adnan (2019-01-01)
The analytics of lifelogging has generated great interest for data scientists because big and multi-dimensional data are generated as a result of lifelogging activities. In this paper, the NTCIR Lifelog dataset is used to learn activities from an image point of view. Minute definitions are classified into activity classes using images and annotations, which serve as a basis for various classification techniques, namely SVMs and convolutional neural network structures (CNN), for learning activities. The perf...
Learning to rank web data using multivariate adaptive regression splines
Altınok, Gülşah; Batmaz, İnci; Karagöz, Pınar; Department of Statistics (2018)
A new trend, called learning to rank, has recently come to light in a wide variety of applications in Information Retrieval (IR), Natural Language Processing (NLP), and Data Mining (DM), to utilize machine learning techniques to automatically build the ranking models. Typical applications are document retrieval, expert search, definition search, collaborative filtering, question answering, and machine translation. In IR, there are three approaches used for ranking. The one is traditional model approaches su...
An intelligent multimedia information system for multimodal content extraction and querying
Yazıcı, Adnan; Yilmaz, Turgay; Sattari, Saeid; SERT, MUSTAFA; Gulen, Elvan (2018-01-01)
This paper introduces an intelligent multimedia information system, which exploits machine learning and database technologies. The system extracts semantic contents of videos automatically by using the visual, auditory and textual modalities, then, stores the extracted contents in an appropriate format to retrieve them efficiently in subsequent requests for information. The semantic contents are extracted from these three modalities of data separately. Afterwards, the outputs from these modalities are fused...
Mask Combination of Multi-Layer Graphs for Global Structure Inference
Bayram, Eda; Thanou, Dorina; Vural, Elif; Frossard, Pascal (2020-01-01)
Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a da...
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 ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. B. Özkan, D. Küçük, S. Buhan, T. Demirci, and P. Karagöz,
Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis
. 2020, p. 77.