A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy
<p>Two views of the photovoltaic (PV) generator of the UniVer Project. (<b>a</b>) PV pergola with semitransparent modules; (<b>b</b>) East view of the PV facade.</p> "> Figure 2
<p>Radiation and temperature sensors unit.</p> "> Figure 3
<p>Current and voltage sensors and microprocessor unit. (<b>a</b>) Two voltage sensor devices for the two branch of the generator under study (black units) and the <math display="inline"><semantics> <mrow> <mi>μ</mi> <mi>P</mi> </mrow> </semantics></math> unit (white one); (<b>b</b>) Two current sensor, toroidal cores, for the two branch of the generator under study.</p> "> Figure 4
<p>Architecture of the IoT module for collecting data in real-time.</p> "> Figure 5
<p>Example of data streams from sensor sources, segmentation and aggregation by temporal sliding windows.</p> "> Figure 6
<p>We show 2-day samples of ground truth of output power generation compared with the predictions. From the top to bottom: (<b>i</b>) the Araujo model, (<b>ii</b>) linear regression, (<b>iii</b>) kNN, (<b>iv</b>) random forest, (<b>v</b>) SVM, (<b>vi</b>) 3CNN+2LSTM.</p> ">
Abstract
:1. Introduction
2. Related Works
3. IoT Module for Real-Time Data Collection in the Opera Digital Platform
4. Machine Learning Approaches to Nowcast Power Generation
4.1. Data-Driven Model to Nowcast Power Generation
4.2. Human-Crafted Features and Multiple Temporal Windows for Efficient Nowcasting of Output Power Generation
- Aggregation functions based on statistical metrics, such as maximal, minimal, average and standard deviation have been defined in this configuration as they have been demonstrated as relevant features in describing sensor streams [47].
- Classification from efficient regressors, with low learning time and training requirements, such as linear regression, k-nearest neighbors (kNN), support vector machines (SVM) and random forest (RF).
4.3. Deep Learning Modeling to Nowcast Output Power Generation
- 2LSTM. Two layers of LSTM which have been previously identified as a suitable configuration to nowcast energy load [49].
- 3CNN+2LSTM. Three layers of CNN are firstly integrated as spatial feature extractors. Next, two layers of LSTM model the temporal dependencies from CNN. The combination of CNN-LSTM hybrid networks has been selected due to providing encouraging results in modeling output power generation [50].
5. Evaluation
5.1. Experimental Setup
- Human-crafted features and multiple temporal windows. We evaluate the nowcasting performance of the following models with human-crafted features and multiple temporal windows and times with the configurations shown below:
- -
- Linear regression, with intercept = True.
- -
- kNN (k-Nearest Neighbors), with number of neighbours = 5.
- -
- SVM (Support Vector Machine), with kernel = polynomial.
- -
- Random forest, with minimum samples leaf = 1 and minimum samples split = 2.
For each of these four models, three sliding temporal window configurations were defined and evaluated:- -
- T = 10 min, one single 10-min temporal window.
- -
- T = 30 min, three 10-min temporal windows.
- -
- T = 90 min, three incremental temporal windows, with a 10-min, 20-min and 60-min window.
- Deep Learning approaches, where we evaluate the performance and learning time of two DL models: 2LSTM and 3CNN+2LSTM, as described in Section 4.3. Concretely, we have evaluated two segmentation configurations: 10 min = 10 m and 5 min = 5 m:
- -
- = 10 m defined by a 90-minute sequence of data whose sequence length is , W = {[0 m, 9 m], [10 m, 19 m], …, [80 m, 89 m]}. For = 10 m and W = {[0 m, 4 m], [5 m, 9 m], …, [85 m, 89 m]} for = 5 m. This configuration generated a total of 24,031 samples for learning purposes.
- -
- = 5 m, defined by a 90-minute sequence of data whose sequence length is , W = {[0 m, 4 m], [5 m, 9 m], …, [85 m, 89 m]} for = 5 m. This configuration generated a total of 48,062 samples for learning purposes.
5.2. Results
5.3. Discussion
6. Conclusions and Ongoing Works
Author Contributions
Funding
Conflicts of Interest
Abbreviations
FF | Fill Factor |
IoT | Internet of Things |
LD | linear dichroism |
CNN | Convolutional Neural Network |
LSTM | Long Short-Term Memory |
DL | Deep Learning |
O&M | Operation and Maintenance |
PV | Photovoltaic |
PVG | PV Generator |
PVS | PV Systems |
STC | Standard test conditions for solar cells |
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Sample Availability: The data collected by the photovoltaic system and code of the models from Opera Project are available in https://github.com/galmonacid/opera/. |
Parameter | Symbol | Unit |
---|---|---|
Irradiance on PV surface | W·m | |
Ambient temperature | C | |
PVG output current | A | |
PVG output voltage | V | |
PVG output power generation | W |
2LSTM | 3CNN+2LSTM |
---|---|
LSTM (32 units) | 2 kernels × 16 filters |
dropout (0.25) | Re-Lu |
LSTM (32 units) | 2 kernels × 32 filters |
dropout (0.25) | Re-Lu |
connected (1 unit) | 2 kernels × 64 filters |
activation function: Re-Lu | Re-Lu |
loss function: MAE | dropout (0.25) |
LSTM (32 units) | |
dropout (0.25) | |
LSTM (32 units) | |
dropout (0.25) | |
connected (1 unit) | |
activation function: Re-Lu | |
loss function: MAE |
Model | RMSE (W) | MAE (W) | R2 |
---|---|---|---|
Araujo | 641.36 | 354.81 | 0.9947 |
Model | Sliding Window Sizes | RMSE (W) | MAE (W) | R2 |
---|---|---|---|---|
Linear Regression | 10 min | 637.20 | 425.74 | 0.9948 |
10 min + 10 min + 10 min | 590.27 | 375.11 | 0.9955 | |
10 min + 20 min + 60 min | 537.37 | 323.04 | 0.9963 | |
kNN | 10 min | 466.76 | 229.62 | 0.9972 |
10 min + 10 min + 10 min | 536.11 | 249.29 | 0.9963 | |
10 min + 20 min + 60 min | 528.40 | 253.13 | 0.9964 | |
Random Forest | 10 min | 410.44 | 201.91 | 0.9978 |
10 min + 10 min + 10 min | 375.49 | 183.49 | 0.9982 | |
10 min + 20 min + 60 min | 360.13 | 173.47 | 0.9983 | |
SVM | 10 min | 4474.17 | 2794.36 | 0.7421 |
10 min + 10 min + 10 min | 4593.69 | 2835.49 | 0.7281 | |
10 min + 20 min + 60 min | 4410.17 | 2653.65 | 0.7493 |
Model | Sliding Window Sizes | Learning Time (ms) | Evaluation Time (ms) |
---|---|---|---|
Linear Regression | 10 min | 11.46 | 2.10 |
10 min + 10 min + 10 min | 36.67 | 2.72 | |
10 min + 20 min + 60 min | 37.25 | 2.81 | |
kNN | 10 min | 86.52 | 13.00 |
10 min + 10 min + 10 min | 213.55 | 51.50 | |
10 min + 20 min + 60 min | 196.54 | 52.11 | |
Random Forest | 10 min | 22,743.35 | 42.89 |
10 min + 10 min + 10 min | 69,790.30 | 45.54 | |
10 min + 20 min + 60 min | 73,499.40 | 42.03 | |
SVM | 10 min | 30,912.46 | 322.88 |
10 min + 10 min + 10 min | 48,459.79 | 846.61 | |
10 min + 20 min + 60 min | 49,373.02 | 857.88 |
Model | Segmentation | RMSE (W) | MAE (W) | R2 |
---|---|---|---|---|
2LSTM | 5 min | 2393.75 | 618.70 | 0.9262 |
10 min | 706.57 | 376.76 | 0.9936 | |
3CNN+2LSTM | 5 min | 2384.14 | 583.11 | 0.9271 |
10 min | 531.08 | 274.87 | 0.9964 |
Model | Segmentation | Learning Time (ms) | Evaluation Time (ms) |
---|---|---|---|
2LSTM | 10 min | 222,657.12 | 6951.65 |
3CNN+2LSTM | 10 min | 197,593.52 | 5627.01 |
Model | RMSE (W) | MAE (W) | R2 |
---|---|---|---|
Araujo | 641.36 | 354.81 | 0.9947 |
3CNN+2LSTM | 531.08 | 274.87 | 0.9964 |
Random Forest | 360.13 | 173.47 | 0.9983 |
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Almonacid-Olleros, G.; Almonacid, G.; Fernandez-Carrasco, J.I.; Espinilla-Estevez, M.; Medina-Quero, J. A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy. Sensors 2020, 20, 4224. https://doi.org/10.3390/s20154224
Almonacid-Olleros G, Almonacid G, Fernandez-Carrasco JI, Espinilla-Estevez M, Medina-Quero J. A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy. Sensors. 2020; 20(15):4224. https://doi.org/10.3390/s20154224
Chicago/Turabian StyleAlmonacid-Olleros, Guillermo, Gabino Almonacid, Juan Ignacio Fernandez-Carrasco, Macarena Espinilla-Estevez, and Javier Medina-Quero. 2020. "A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy" Sensors 20, no. 15: 4224. https://doi.org/10.3390/s20154224