Topics:
✅ Introduction
✅ Embedded Systems
➡️ Energy Meter
➡️ ESP32
➡️ Raspberry Pi
✅ Acquisition
➡️ Example
✅ Identification
➡️ System Identification Toolbox - Matlab
✅ Control Design
➡️ Energy consumption prediction
✅ Hardware Design
➡️ Raspberry and ESP32
✅ Related Works
✅ Future Work
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
2. Topics
• Introduction
• Embedded Systems
• Energy Meter
• ESP32
• Raspberry Pi
• Acquisition
• Example
• Identification
• System Identification Toolbox - Matlab
• Control Design
• Energy consumption prediction
• Hardware Design
• Raspberry and ESP32
• Related Works
• Future Work
TELEMETRÍA DE CONSUMO DE ENERGÍA
ELÉCTRICA BASADO EN HARDWARE DE CÓDIGO
ABIERTO
5. Introduction
Energy Meter (Pzem-004t)
Características:
1. Rango de voltaje: 80-260V AC
2. Rango de lecturas: 0-9999.99kwh
3. Resolución de voltaje: 0.1V
4. Rango de corriente: 0-100A
5. Resolución de corriente: 0.001A
6. Rango de potencia: 0-23kw
7. Resolución de potencia: 0.1W
8. Frecuencia: 45-65Hz
9. Dimesiones: 9*6.05*2.3cm
10. Certificaciones: CE, FCC BV
11. Comunicación: TTL
7. Introduction
Raspberry Pi4
For more information about Hardware Design, check the link:
ELECTRONIC PROTOTYPES DESIGN
https://vasanza.blogspot.com/p/shared-material.html
22. Hardware Design
Hard-processor ARM
For more information about Hardware Design, check the link:
ELECTRONIC PROTOTYPES DESIGN
https://vasanza.blogspot.com/p/shared-material.html
23. Hardware Design
Hard-processor ARM
For more information about Hardware Design, check the link:
ELECTRONIC PROTOTYPES DESIGN
https://vasanza.blogspot.com/p/shared-material.html
24. Hardware Design
ESP32
For more information about Hardware Design, check the link:
ELECTRONIC PROTOTYPES DESIGN
https://vasanza.blogspot.com/p/shared-material.html
25. Behavioral Signal Processing with Machine Learning Based on FPGA
Related Works
Asanza V., Sanchez G., Cajo R., Peláez E. (2021) Behavioral Signal Processing with Machine Learning Based on
FPGA. In: Botto-Tobar M., Zamora W., Larrea Plúa J., Bazurto Roldan J., Santamaría Philco A. (eds) Systems
and Information Sciences. ICCIS 2020. Advances in Intelligent Systems and Computing, vol 1273. Springer,
Cham. https://doi.org/10.1007/978-3-030-59194-6_17
26. Behavioral Signal Processing with Machine Learning Based on FPGA
Overview of our proposed architecture
Results obtained while testing different ser of neurons in
Hidden Layer
Resources used by FPGA
Related Works
Asanza V., Sanchez G., Cajo R., Peláez E. (2021) Behavioral Signal Processing with Machine Learning Based on
FPGA. In: Botto-Tobar M., Zamora W., Larrea Plúa J., Bazurto Roldan J., Santamaría Philco A. (eds) Systems
and Information Sciences. ICCIS 2020. Advances in Intelligent Systems and Computing, vol 1273. Springer,
Cham. https://doi.org/10.1007/978-3-030-59194-6_17
28. Future Work
Bansal, S., & Kumar, D. (2020). IoT Ecosystem: A Survey on Devices, Gateways, Operating Systems,
Middleware and Communication. International Journal of Wireless Information Networks, 1-25.
29. Víctor Asanza
Mail: vasanza@espol.edu.ec
Facultad de Ingeniería en Electricidad y Computación, FIEC
Escuela Superior Politécnica del Litoral, ESPOL
Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863
090150 Guayaquil, Ecuador
For more information