Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture
المؤلفون المشاركون
Wu, Hao
Yang, Hang
Ke, Ji
Qin, Yude
Wang, Biao
Yang, Shundong
Zhao, Xing
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-20، 20ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-15
دولة النشر
مصر
عدد الصفحات
20
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon.
However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive.
Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling.
This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture.
Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation.
Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate.
The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity.
The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD).
The proposed strategies are demonstrated both in residential and office buildings.
The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ke, Ji& Qin, Yude& Wang, Biao& Yang, Shundong& Wu, Hao& Yang, Hang…[et al.]. 2020. Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1214721
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ke, Ji…[et al.]. Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1214721
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ke, Ji& Qin, Yude& Wang, Biao& Yang, Shundong& Wu, Hao& Yang, Hang…[et al.]. Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1214721
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214721
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