An intelligent heating system based on the Internet of Things and STM32 microcontroller

Under the rapid growth of Internet of Things technology, many households are moving towards smart solutions. Addressing the inflexibility of temperature control in traditional heating systems, this research focuses on designing an intelligent heating system. To enhance flexibility and intelligence,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Energy Informatics Jg. 7; H. 1; S. 22 - 18
1. Verfasser: Su, Yan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.12.2024
Springer Nature B.V
SpringerOpen
Schlagworte:
Algorithms
Computer Science
Control algorithms
Control methods
Control systems
Control theory
Dynamic matrix control
Fuzzy control
Genetic algorithms
Heating
Heating system
Heating systems
Households
Information Systems and Communication Service
Internet of Things
Microcontrollers
Optimization
Optimization control
Predictive control
Proportional integral derivative
STM32 microcontroller
Temperature control
Dynamic matrix control
Heating system
Internet of Things
STM32 microcontroller
Optimization control
ISSN:2520-8942, 2520-8942
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Under the rapid growth of Internet of Things technology, many households are moving towards smart solutions. Addressing the inflexibility of temperature control in traditional heating systems, this research focuses on designing an intelligent heating system. To enhance flexibility and intelligence, an intelligent heating system based on the Internet of Things and STM32 microcontroller is proposed. Furthermore, the study identifies limitations of traditional proportional-integral-derivative control methods and establishes an optimization control model for heating system output temperature based on the Dynamic Matrix Control algorithm. Results indicate that the system's web interface successfully draws temperature curves, displaying clear data on detected temperature and humidity. The output temperature optimization control model shows a temperature rise of 2 °C and a temperature control error index of 0.0543 during the initial heating stage, and a control error index of 0.0353 during the mid-heating stage when the valve relative opening is close to 0. And the temperature control effect is better than traditional PID control, fuzzy PID control, genetic algorithm based PID control, and predictive feedback predictive control, without obvious indoor temperature overshoot phenomenon, which has certain advantages. In conclusion, the proposed system and model exhibit favorable application outcomes, offering technological support for the intelligent management of heating systems.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2520-8942
2520-8942
DOI:10.1186/s42162-024-00326-2