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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Machine Learning Based Electric Energy Consumption Prediction of a Large-Scaled Production Plant with Small-Scaled Data

Küçük Ölçekli Verilerle Büyük Ölçekli Bir Üretim Tesisinin Makine Öğrenimine Dayalı Elektrik Enerjisi Tüketim Tahmini

How to cite: Özdemir V, Çalışkan A, Yiğit A. Machine learning based electric energy consumption prediction of a large-scaled production plant with small-scaled data . Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2020; 3(2): 84-89. DOI: 10.54856/jiswa.202012124

Full Text: PDF, in Turkish.

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Title: Machine Learning Based Electric Energy Consumption Prediction of a Large-Scaled Production Plant with Small-Scaled Data

Abstract: This report covers the statistical approach to predict consumed energy for a tire production plant. The reasons behind this study are also to optimize the energy consumption budget and to follow the production area wised KPIs which is also vital for ISO 50001 Energy management system standard. In order to make it happen, writers clarify the main problem, then start to apply the steps of the cross industry standard process for data mining (CRISP-DM) [1] methodology. The most important point of this study was that although the historical data is small scaled, the parameters have a higher dimension according to input examples. Hence, the data to be used as input could be explained with simple variables to be used in the budget period. The study introduces data preparation steps based on the production area, grid search for best regression algorithm, comparison of models, and seven-month validation results.

Keywords: Machine learning; regression; small scaled data analytics; energy consumption prediction


Başlık: Küçük Ölçekli Verilerle Büyük Ölçekli Bir Üretim Tesisinin Makine Öğrenimine Dayalı Elektrik Enerjisi Tüketim Tahmini

Özet: Bu çalışma bir lastik üretim fabrikası için tüketilen enerjiyi tahmin ve takip etmeye yönelik istatistiksel bir yaklaşımı kapsamaktadır. Çalışmanın arkasındaki nedenler, enerji tüketim bütçesini optimize etmek ve ISO 50001 Enerji Yönetim Sistemi Standardı için de kritik önem taşıyan üretim proses bilgisine dayalı performans indikatörlerini belirlemek ve takip etmektir. Bunun gerçekleşmesi için endüstriler arası veri madenciliği standart prosesinin (CRISP-DM) [1] adımları takip edilmiştir. Bu çalışmanın en önemli noktası, tarihsel verilerin küçük ölçekli olmasına rağmen, parametrelerin girdi örneklerine göre daha yüksek bir boyuta sahip olmasıdır. Böylelikle girdi olarak kullanılacak veriler bütçe döneminde kullanılacak basit değişkenlerle açıklanabilir. Çalışma, üretim alanına dayalı veri hazırlama adımlarını, çapraz geçerleme ile en iyi bağlanım algoritması ve parametrelerinin seçimini, makine öğrenimi modellerinin karşılaştırmasını ve sonuçlarını takip eden yedi aylık doğrulama adımını tanıtmaktadır.

Anahtar kelimeler: Makine öğrenimi; bağlanım; küçük ölçekli veri analizi; enerji tüketim tahmini


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