Unsupervised Energy Disaggregation of Home Appliances

2017 ◽  
pp. 398-409 ◽  
Author(s):  
Kondwani M. Kamoto ◽  
Qi Liu ◽  
Xiaodong Liu
Keyword(s):  
Home Appliances ◽  
Energy Disaggregation

Home Automation Using Bluetooth

2020 ◽  
pp. 675-678
Author(s):  
Mukesh Mahajan ◽  
Astha Dubey ◽  
Samruddhi Desai ◽  
Kaveri Netawate
Keyword(s):  
Temperature Sensor ◽  
Humidity Sensor ◽  
The Other ◽  
Automation System ◽  
Home Automation ◽  
Wireless Devices ◽  
Home Appliances ◽  
Home Automation System ◽  
Condition Systems

This paper reviews basically about Bluetooth based home automation system. It is controlled by PIC microcontroller. Home automation can be defined as the ability to perform tasks automatically and monitor or change status remotely. These include tasks such as turning off lights in the room, locking doors via smartphone, automate air condition systems and appliances which help in the kitchen. Now a days several wireless devices are available such as Bluetooth, Zigbee and GSM. Since Bluetooth is low in cost than the other two and hence is used more. In this paper we have described the methods of automating different home appliances using Bluetooth and pic microcontroller. Different sensors are involved in this system to advance and make it smarter. Sensors such as temperature sensor, liquid sensors, humidity sensor etc. can be used.

Consumer Statisfaction with Rubber and Home Appliances

2011 ◽  
Vol 3 (2) ◽  
pp. 44-45
Author(s):  
A. Joseph Succour Jolly ◽  
Keyword(s):  
Home Appliances

CONTROLING AND MONITORING HOME APPLIANCES THROUGH CLOUD USING IoT

2018 ◽  
Vol 74 (2) ◽  
Author(s):  
Saranya Vanama ◽  
PACHIPALA YELLAMMA ◽  
A RAMYA ◽  
G.V KALYANI ◽  
CHALLA NARASIMHAM
Keyword(s):  
Home Appliances

Muscles

2018 ◽  
Author(s):  
Iain A. Anderson ◽  
Benjamin M. O’Brien
Keyword(s):  
Sensory Feedback ◽  
Artificial Muscles ◽  
Combustion Engines ◽  
Electric Motors ◽  
Home Appliances ◽  
Leg Muscles ◽  
Mechanical Devices ◽  
Control Surfaces

Mechanical devices that include home appliances, automobiles, and airplanes are typically driven by electric motors or combustion engines through gearboxes and other linkages. Airplane wings, for example, have hinged control surfaces such as ailerons. Now imagine a wing that has no hinged control surfaces or linkages but that instead bends or warps to assume an appropriate shape, like the wing of a bird. Such a device could be enabled using an electro-active polymer technology based on electronic artificial muscles. Artificial muscles act directly on a structure, like our leg muscles that are attached by tendon to our bones and that through phased contraction enable us to walk. Sensory feedback from our muscles enables proprioceptive control. So, for artificial muscles to be used appropriately we need to pay attention not only to mechanisms for muscle actuation but also to how we can incorporate self-sensing feedback for the control of position.

scholarly journals Design of IoT Based Smart Energy Meter for Home Appliances

2021 ◽  
Vol 1964 (5) ◽  
pp. 052001
Author(s):  
M Rupesh ◽  
N Anbu Selvan
Keyword(s):  
Home Appliances ◽  
Energy Meter

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

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