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Smart Electricity Meter Monitoring and Prediction using iSocket
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Home Projects Embedded Smart Electricity Meter Monitoring and Prediction using iSocket
Design And Implementation Of Assistive Robot For The Elderly And Impaired Person
Design And Implementation Of Assistive Robot For The Elderly And Impaired Person ₹9,750.00
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Development of IoT Based Logistic Vehicle Maintenance System
Development of IoT Based Logistic Vehicle Maintenance System ₹9,000.00

Smart Electricity Meter Monitoring and Prediction using iSocket

₹10,500.00

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SKU: Embedded - Machine Learning Categories: Embedded, Internet of Things(IoT) Tags: Electric, iot, Machine Learning, Python, Random Forest algorithm
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Description

Aim:

Ā Ā Ā Ā Ā Ā Ā Ā Ā  Aim of the project is to build a smart home automation system to control home appliances, monitor the power consumed by the appliances and predict the power consumption using machine learning algorithm.

Synopsis:

Ā Ā Ā Ā Ā Ā Ā Ā Ā  In order to implement the concepts of smart residential automation, this project develop a prototype for power consumption monitoring in smart homes. In this device we embed a voltage and current sensor for individual sockets, then we can measure the power consumed over a time. Day wise the power consumption data will be stored in the cloud data base.

Ā Ā Ā Ā Ā Ā Ā Ā Ā  Using Machine learning algorithm we can predict the consumption of power in future days. Prediction of power consumption helps the consumer to calculate the Electricity bills, individual usage of blocks and etc.

Ā Ā Ā Ā Ā Ā Ā Ā Ā  Here, NodeMCU used as master microcontroller to monitor the power usage of devices and control of devices. This controller can directly connected network and cloud database and receive the control signals from the android mobile from user.

Ā Ā Ā Ā Ā Ā Ā Ā Ā  Customized android mobile application is designed to monitor the power usage and control the home appliances from anywhere through internet.

Proposed System:

Ā Ā Ā Ā Ā Ā Ā Ā Ā  Proposed system can monitor power usage as well as control the devices from user-end. Also Random forest machine learning algorithm is used to predict the power usage in future day.

 

Additional information
Weight 2.5 kg
Dimensions 16 × 10 × 4 cm
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