TY - GEN
T1 - Design of an Artificial Neural Network Controller for a Tankless Water Heater by Using a Low-Profile Embedded System
AU - Laurencio-Molina, Juan Carlos
AU - Salazar-Garcia, Carlos
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/12
Y1 - 2018/9/12
N2 - Tankless water heaters (TWHs) have been become more popular day-by-day in special because of the low-power consumption that characterizes these devices in comparison with the tank water heaters. Nonetheless, it is desirable that these systems have a rapid response to disturbances such as changes in water flow or the inlet temperature. Different methods of classic control have been used for solving this problem for decades. These techniques provide a good solution although not necessarily the optimal one. With the recent boom in automatic control techniques based on Artificial Neural Networks (ANNs) [1]-[3] and the scaling in terms of computational power of embedded systems, this has led to the use of ANNs in low-profile embedded systems. In this work, we present an implementation of an ANN for a commercial application of a TWH running on a low-profile embedded system where we demonstrated that the stabilization time is reduced by up to 25% whilst the overshoot by up to 50%, both in comparison with a classic methods of automatic control using a low-performance microcontroller.
AB - Tankless water heaters (TWHs) have been become more popular day-by-day in special because of the low-power consumption that characterizes these devices in comparison with the tank water heaters. Nonetheless, it is desirable that these systems have a rapid response to disturbances such as changes in water flow or the inlet temperature. Different methods of classic control have been used for solving this problem for decades. These techniques provide a good solution although not necessarily the optimal one. With the recent boom in automatic control techniques based on Artificial Neural Networks (ANNs) [1]-[3] and the scaling in terms of computational power of embedded systems, this has led to the use of ANNs in low-profile embedded systems. In this work, we present an implementation of an ANN for a commercial application of a TWH running on a low-profile embedded system where we demonstrated that the stabilization time is reduced by up to 25% whilst the overshoot by up to 50%, both in comparison with a classic methods of automatic control using a low-performance microcontroller.
KW - Artificial Neural Networks
KW - Embedded system
KW - Instantaneous water heater
KW - PID controller
KW - Tankless water heater
UR - https://www.scopus.com/pages/publications/85054552998
U2 - 10.1109/IWOBI.2018.8464196
DO - 10.1109/IWOBI.2018.8464196
M3 - Contribución a la conferencia
AN - SCOPUS:85054552998
SN - 9781538675069
T3 - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
BT - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018
Y2 - 18 July 2018 through 20 July 2018
ER -