Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study
المؤلفون المشاركون
Dalipi, Fisnik
Yildirim Yayilgan, Sule
Gebremedhin, Alemayehu
المصدر
Applied Computational Intelligence and Soft Computing
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-06-15
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS).
Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific.
For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model.
The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF).
We use the data collected from buildings at several locations for a period of 29 weeks.
Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient.
In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms.
The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Dalipi, Fisnik& Yildirim Yayilgan, Sule& Gebremedhin, Alemayehu. 2016. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1094898
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Dalipi, Fisnik…[et al.]. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1094898
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Dalipi, Fisnik& Yildirim Yayilgan, Sule& Gebremedhin, Alemayehu. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1094898
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1094898
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر