S.Chandraprabha, G.Pradeepkumar, M.D.Saranya, S.Satheesh Kumar and R. Sowmya, Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India.
Dineshkumar Ponnusamy, Senior Applications Consultant, Capegemini America, INC. Salt Lake City, Utah, United States of America
Online First : 30 December 2020
Publisher Name : IJAICT India Publications, India.
Print ISBN : 978-81-950008-0-7
Online ISBN : 978-81-950008-1-4
Page :158-161
Abstract
This paper outfits artificial intelligence based real time LDR data which is implemented in various applications like indoor lightning, and places where enormous amount of heat is produced, agriculture to increase the crop yield, Solar plant for solar irradiance Tracking. For forecasting the LDR information. The system uses a sensor that can measure the light intensity by means of LDR. The data acquired from sensors are posted in an Adafruit cloud for every two seconds time interval using Node MCU ESP8266 module. The data is also presented on adafruit dashboard for observing sensor variables. A Long short-term memory is used for setting up the deep learning. LSTM module uses the recorded historical data from adafruit cloud which is paired with Node MCU in order to obtain the real-time long-term time series sensor variables that is measured in terms of light intensity. Data is extracted from the cloud for processing the data analytics later the deep learning model is implemented in order to predict future light intensity values.
Keywords
Adafruit, Deep Learning, IoT, LSTM, Light dependent resistor, NODE MCU.
Cite this article
S.Chandraprabha, G.Pradeepkumar, M.D.Saranya, S.Satheesh Kumar, R. Sowmya and Dineshkumar Ponnusamy, “Real Time LDR Data Prediction using IoT and Deep Learning Algorithm”, Innovations in Information and Communication Technology, pp. 158-161, December 2020.
Copyright
© 2020 S.Chandraprabha, G.Pradeepkumar, M.D.Saranya, S.Satheesh Kumar, R. Sowmya and Dineshkumar Ponnusamy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
