scholarly journals Design of high fidelity building energy monitoring system

2011 ◽  
Author(s):  
Hao Wang
Keyword(s):  
Monitoring System ◽  
Building Energy ◽  
High Fidelity ◽  
Energy Monitoring

scholarly journals Green switch: an IoT based energy monitoring system for mabini building in De La Salle Lipa

2021 ◽  
Vol 24 (2) ◽  
pp. 754
Author(s):  
Oscar Bryan M. Magtibay ◽  
Rodelio H. Cabrera ◽  
Joselito P. Roxas ◽  
Mark Anthony De Vera
Keyword(s):  
Monitoring System ◽  
Air Conditioning ◽  
Building Energy ◽  
Raspberry Pi ◽  
Energy Monitoring ◽  
Voltage Sensors ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Class Schedule ◽  
And Control

<p>Building energy management systems (BEMS) are critical tools for managing and controlling a facility's technical systems and services, such as lighting, ventilation, heating, and air conditioning, to ensure that the building operates at peak efficiency while decreasing energy waste. The Mabini Building at De La Salle Lipa has nearly a hundred rooms, 70 of which are used by college students for lecture and laboratory classes. From 7:30 a.m. to 9:00 p.m., these rooms are available. In a daily class schedule, air conditioning units and lights are used an average of 10 hours per day, while fans and power outlets are used an average of 5 hours. Even when no classes are being held, the aforementioned equipment is frequently left open in these rooms. The researchers created and constructed an IoT-based energy monitoring system to monitor and control the lights and outlets in a room. The system will also record the number of kilowatt-hours (kWh) consumed. The system employs NodeMCU, current, and voltage sensors, a Raspberry Pi 3, and the school's existing network to send and receive data from the server. The building administrator will use the collected data to give consumption statistics and reduce the carbon footprint.</p>

A Critical Study on Campus Energy Monitoring System and Role of IoT

2021 ◽  
Author(s):  
Mopuri Deepika ◽  
Merugu Kavitha ◽  
N. S. Kalyan Chakravarthy ◽  
J. Srinivas Rao ◽  
D. Mohan Reddy ◽  
...  
Keyword(s):  
Monitoring System ◽  
Critical Study ◽  
Energy Monitoring ◽  
Campus Energy

scholarly journals Proposing 3D Thermal Technology for Heritage Building Energy Monitoring

2021 ◽  
Vol 13 (8) ◽  
pp. 1537
Author(s):  
Antonio Adán ◽  
Víctor Pérez ◽  
José-Luis Vivancos ◽  
Carolina Aparicio-Fernández ◽  
Samuel A. Prieto
Keyword(s):  
Computer Vision ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Building Energy ◽  
Energy Monitoring ◽  
3D Computer Vision ◽  
Building Monitoring ◽  
Heritage Buildings ◽  
Heritage Building ◽  
Definition Of

The energy monitoring of heritage buildings has, to date, been governed by methodologies and standards that have been defined in terms of sensors that record scalar magnitudes and that are placed in specific positions in the scene, thus recording only some of the values sampled in that space. In this paper, however, we present an alternative to the aforementioned technologies in the form of new sensors based on 3D computer vision that are able to record dense thermal information in a three-dimensional space. These thermal computer vision-based technologies (3D-TCV) entail a revision and updating of the current building energy monitoring methodologies. This paper provides a detailed definition of the most significant aspects of this new extended methodology and presents a case study showing the potential of 3D-TCV techniques and how they may complement current techniques. The results obtained lead us to believe that 3D computer vision can provide the field of building monitoring with a decisive boost, particularly in the case of heritage buildings.

Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System

2021 ◽  
Vol 17 (3) ◽  
pp. 1-20
Author(s):  
Vanh Khuyen Nguyen ◽  
Wei Emma Zhang ◽  
Adnan Mahmood
Keyword(s):  
Supervised Learning ◽  
Monitoring System ◽  
End Users ◽  
Training Data ◽  
Smart Device ◽  
Energy Monitoring ◽  
Load Monitoring ◽  
Benchmark Datasets ◽  
Smart Home Environment ◽  
Smart Appliance

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

scholarly journals A Novel Method for Detecting Abnormal Energy Data in Building Energy Monitoring System

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Liang Zhao
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Real Time ◽  
Difference Method ◽  
Building Energy ◽  
Energy Monitoring ◽  
Order Difference ◽  
Energy Data ◽  
First Order ◽  
Novel Method

This paper presents a novel abnormal data detecting algorithm based on the first order difference method, which could be used to find out outlier in building energy consumption platform real time. The principle and criterion of methodology are discussed in detail. The results show that outlier in cumulative power consumption could be detected by our method.

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