Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach †
Abstract
:1. Introduction
2. Internet of Things and Artificial Intelligence Infrastructure on eoTICC Project
- Things. Today there are millions of things (sensors/actuators and devices found in commercial and industrial settings) connected directly through wireless networks and accessing the Internet. Usually, the IoT solutions have things filtered and managed using data locally and/or connected to gateways that provide extended functionality. Basic devices are tagged like things. Each thing has data that can be shared in the Internet.
- Local Gateway. Most of existing things were not designed to connect to the Internet and cannot share data with the cloud. To resolve this difficulty gateways act as intermediaries between things and the cloud, providing the needed connectivity, security, and manageability.
- Network and Cloud. Cloud infrastructure contains large pools of virtualized servers and storage that are networked together. IoT solutions run applications that analyse and manage data from devices and sensors in order to generate services that produce information used in decision making.
3. Materials and Methods
- Learning power consumption patterns
- Forecast power consumption and forecast power generation.
- Home appliances control using forecast data and decision trees.
4. Results
- Iot node connects to open weather services to obtain hourly weather forecast.
- Solar and wind data are analysed.
- Power generation is estimated.
- Power consumption is calculated and an algorithm with decision trees decides control actions.
- Process 1: Data capture. An algorithm captures system data: rpm wind turbine, wind and solar energy generated, power consumption, ambient data, controllers and other. This algorithm communicates these data to cloud and other control processes.
- Process 2: Forecast. Hourly algorithm that is connected to open internet weather data. This algorithm calculates the power generation and consumption forecasts. It uses rules based on decision trees. The first decision tree is shown in Figure 9.
- Process 3. Decision tree and control. This algorithm controls the installation: start/stop, security and others. Decision tree data allows processing different control strategies.
- Process 4. Cloud services. A cloud platform is used to implement dashboard monitoring, storage and control.
5. Conclusions
Author Contributions
Acknowledgments
References
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Operating Mode | Wind Speed (m/s) | |
---|---|---|
Cut in | 2 | zone I |
Rated | 12.5 | zone II |
Cut out | 26 | zone III |
Solar Panels | Wind Turbine | Total Production | Consumption | |
---|---|---|---|---|
(W) | (W) | (W) | (W) | |
00:00 to 1:00 a.m. | - | 100 | 100 | 400 |
1:00 to 2:00 a.m. | - | 90 | 100 | 300 |
2:00 to 3:00 a.m. | - | 90 | 90 | 300 |
3:00 to 4:00 a.m. | - | 80 | 100 | 120.1 |
4:00 to 5:00 a.m. | - | 50 | 50 | 120.1 |
5:00 to 6:00 a.m. | - | 50 | 50 | 120 |
6:00 to 7:00 a.m. | - | 20 | 20 | 120 |
7:00 to 8:00 a.m. | - | 20 | 20 | 120.3 |
8:00 to 9:00 a.m. | 240.2 | - | 240.2 | 220 |
9:00 to 10:00 a.m. | 360.1 | - | 360.1 | 320.5 |
10:00 to 11:00 a.m. | 442.9 | 11 | 453.9 | 120.2 |
11:00 a.m. to 12:00 p.m. | 485.0 | 30 | 515.0 | 160 |
12:00 p.m. to 13:00 p.m. | 485.0 | 95 | 580.0 | 300 |
13:00 to 14:00 p.m. | 442.9 | 400 | 842.9 | 900 |
14:00 to 15:00 p.m. | 360.1 | 315 | 675.1 | 1200 |
15:00 to 16:00 p.m. | 240.1 | 450 | 690.1 | 600 |
16:00 to 17:00 p.m. | 79.6 | 450 | 529.6 | 300 |
17:00 to 18:00 p.m. | 16.0 | 210 | 226.0 | 100 |
18:00 to 19:00 p.m. | - | 340 | 340 | 300.2 |
19:00 to 20:00 p.m. | - | 100 | 160 | 600.3 |
20:00 to 21:00 p.m. | - | 55 | 55 | 600.5 |
21:00 to 22:00 p.m. | - | 55 | 55 | 700 |
22:00 to 23:00 p.m. | - | 35 | 35 | 500 |
23:00 to 24:00 p.m. | - | 25 | 25 | 500 |
Total production | 6312.9 | 9022.2 |
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Ferrández-Pastor, F.-J.; Gómez-Trillo, S.; Nieto-Hidalgo, M.; García-Chamizo, J.-M.; Valdivieso-Sarabia, R. Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach. Proceedings 2018, 2, 1239. https://doi.org/10.3390/proceedings2191239
Ferrández-Pastor F-J, Gómez-Trillo S, Nieto-Hidalgo M, García-Chamizo J-M, Valdivieso-Sarabia R. Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach. Proceedings. 2018; 2(19):1239. https://doi.org/10.3390/proceedings2191239
Chicago/Turabian StyleFerrández-Pastor, Francisco-Javier, Sergio Gómez-Trillo, Mario Nieto-Hidalgo, Juan-Manuel García-Chamizo, and Rafael Valdivieso-Sarabia. 2018. "Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach" Proceedings 2, no. 19: 1239. https://doi.org/10.3390/proceedings2191239
APA StyleFerrández-Pastor, F.-J., Gómez-Trillo, S., Nieto-Hidalgo, M., García-Chamizo, J.-M., & Valdivieso-Sarabia, R. (2018). Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach. Proceedings, 2(19), 1239. https://doi.org/10.3390/proceedings2191239