TY - GEN
T1 - IoT-Driven Smart Energy Monitoring
T2 - 21st IEEE Student Conference on Research and Development, SCOReD 2023
AU - Ali, Usman
AU - Ramzan, Muhammad Umer
AU - Ali, Waqas
AU - Rana, Muhammad Ehsan
AU - Qayyum, Abdul
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper unveils a meticulous creation process for an Internet of Things (IoT)-powered intelligent energy monitoring framework. This system adeptly gauges current, voltage, active power, power factor, energy, and unit consumption in real-time, spanning hourly, weekly, and monthly intervals. This instantaneous data is seamlessly relayed via the integrated WiFi module within the ESP32 module. Subsequently, the cloud orchestrates the dissemination of these measurements to an Android application, wherein real-time values are conveniently visualized. The Android application, far from being restricted to visualization, demonstrates an additional facet by harnessing machine learning-powered time-series forecasting models to predict future unit consumption. Remarkably, the implementation of the ARIMA, Prophet, and LSTM models for future unit prediction highlights LSTM's superiority in accuracy over the other models.
AB - This paper unveils a meticulous creation process for an Internet of Things (IoT)-powered intelligent energy monitoring framework. This system adeptly gauges current, voltage, active power, power factor, energy, and unit consumption in real-time, spanning hourly, weekly, and monthly intervals. This instantaneous data is seamlessly relayed via the integrated WiFi module within the ESP32 module. Subsequently, the cloud orchestrates the dissemination of these measurements to an Android application, wherein real-time values are conveniently visualized. The Android application, far from being restricted to visualization, demonstrates an additional facet by harnessing machine learning-powered time-series forecasting models to predict future unit consumption. Remarkably, the implementation of the ARIMA, Prophet, and LSTM models for future unit prediction highlights LSTM's superiority in accuracy over the other models.
KW - ESP32
KW - Internet of Things
KW - Machine Learning
KW - Smart Meter
KW - Time Series Forecasting
UR - https://www.scopus.com/pages/publications/85197679635
U2 - 10.1109/SCOReD60679.2023.10563825
DO - 10.1109/SCOReD60679.2023.10563825
M3 - Conference contribution
AN - SCOPUS:85197679635
T3 - 2023 IEEE 21st Student Conference on Research and Development, SCOReD 2023
SP - 672
EP - 677
BT - 2023 IEEE 21st Student Conference on Research and Development, SCOReD 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 December 2023 through 14 December 2023
ER -
