Prediction of Unknown Location data using Virtual sensors modeling through Machine learning
What is Virtual Sensors?
Virtual sensors are created with the help of physical sensors that determine the information of the particular location using integrated embedded applications. These sensors physically record and fetch the recorded data for analysis and prediction of the invisible room
Is Virtual Sensors replace Physical sensors?
Virtual sensors are not the complete replacement of Physical sensors but it could replace few Physical sensors, so that it get the data from the existing physical sensors to create and model a new Virtual data
How Unknown Parameters are calculated?
Using machine learning algorithms, The existing physical sensors record the room parameters and create the training model that evaluate the time series data of virtual data. The model is developed, Trained in such as way if the training data is acquired from the real time existing environment, using time series prediction, (future prediction model) the proposed Virtual data is being acquired and hence the Unknown parameters of the location is determined.
Example :Application to Determine the Unknown room Parameter
Consider Room A, Room B and Room C available in the Particular Building.
Room A and B is connected with Physical sensors Namely
1. Illuminance Sensor
2. Temperature sensor
3. Humidity Sensor
- Room A and B data is considered as the Physical data and Training data for the Machine Learning Model
- Create a Time Series NARX model or any other Time Series regression Algorithm using Machine Learning ToolBox (Python or MATLAB)
- At the end of the prediction state , The time State t+1 determine the Room C data (Which is unknown)
- Each sensors are tuned like this and Predicted with t+1, 2t+1, 3t+1,4t+1 etc.....
- Accumulated results at the time series data act as the Virtual Data and the Entire analysis Set up act as the Virtual Sensor
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