Internet of Things and Energy Management

2018 ◽  
Vol 138 (2) ◽  
pp. 90-93 ◽  
Author(s):  
Hiroshi ARAI
Keyword(s):  
Internet Of Things ◽  
Energy Management

Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities

2020 ◽  
Vol 55 ◽  
pp. 102615 ◽  
Author(s):  
Hasan Ali Khattak ◽  
Komal Tehreem ◽  
Ahmad Almogren ◽  
Zoobia Ameer ◽  
Ikram Ud Din ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Energy Management ◽  
Dynamic Pricing ◽  
Smart Cities ◽  
Industrial Internet Of Things ◽  
Industrial Internet

scholarly journals IoT - Internet of Things Based Energy Management for Smart Home

2018 ◽  
Vol Volume-2 (Issue-2) ◽  
pp. 1539-1542
Author(s):  
R. Nareshkumar ◽  
B. J. Naveenkumar ◽  
R. Sivasankari ◽  
Keyword(s):  
Internet Of Things ◽  
Energy Management ◽  
Smart Home

Research on intelligent city energy management based on Internet of things

2018 ◽  
Vol 22 (S4) ◽  
pp. 8291-8300 ◽  
Author(s):  
Miao Yu ◽  
Guojun Yue ◽  
Jinguo Song ◽  
Xu Pang
Keyword(s):  
Internet Of Things ◽  
Energy Management ◽  
Intelligent City

scholarly journals Energy Management Considering Energy Storage and Demand Response for Smart Energy Hub in Internet of Things

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Jinyong Lei ◽  
Changcheng Zhou ◽  
Xiaolin Li ◽  
Andi Huang ◽  
Hao Bai ◽  
...  
Keyword(s):  
Energy Storage ◽  
Internet Of Things ◽  
Energy Management ◽  
Demand Response ◽  
Energy Hub

scholarly journals Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ji Ke ◽  
Yude Qin ◽  
Biao Wang ◽  
Shundong Yang ◽  
Hao Wu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategies ◽  
Principal Component ◽  
Building Energy ◽  
Physical Models ◽  
Data Driven ◽  
Building Energy Management

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.

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