Forecasting Solar Power Ramp Events Using Machine Learning Classification Techniques
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The document presents a study on forecasting solar power ramp events using various machine learning classification techniques, focusing on improving the accuracy of predicting high-rate ramp events. It discusses different methodologies, including feature selection and model evaluation metrics, highlighting the models' performances such as Random Forest and Support Vector Machines. Potential applications of accurate ramp event forecasts are explored, showing their importance in optimizing energy system operations and trading decisions.
Forecasting Solar Power Ramp Events Using Machine Learning Classification Techniques
- 1. Forecasting Solar Power Ramp Events Using Machine Learning Classification Techniques Mohamed Abuella Prof. Badrul Chowdhury Energy Production and Infrastructure Center Department of Electrical and Computer Engineering University of North Carolina at Charlotte S19:Grid Impact of Distributed Generation Date: Thursday, 28th June
- 2. Presentation Layout Definition of Solar Power Ramp Events Potential Applications Methodology Results and Evaluation
- 3. Ramp Events During a Cloudy Day Δ𝑃 Δ𝑡 Some ramps are with low rates, while others with high rates. Ramp rate, Δ𝑃 Δ𝑡 = 0.2−0.85 12:00−11:00 = −0.65 −65% 𝑟𝑎𝑚𝑝 𝑑𝑜𝑤𝑛 𝑜𝑓 𝑖𝑡𝑠 𝑛𝑜𝑟𝑚𝑎𝑙 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦, (𝑝𝑢/ℎ𝑟) Ramp rate, Δ𝑃 Δ𝑡 = 0.48−0.1 14:00−13:00 = +0.38 +38% 𝑟𝑎𝑚𝑝 𝑢𝑝 𝑜𝑓 𝑖𝑡𝑠 𝑛𝑜𝑟𝑚𝑎𝑙 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦, (𝑝𝑢/ℎ𝑟) For the illustrated cloudy day below: where P(t) is the solar power of the target hour, it can also be its forecast F(t); D is the time duration for which the ramp rate is determined. 𝑅𝑎𝑚𝑝 𝑅𝑎𝑡𝑒, 𝑅𝑅(𝑡) = )𝑑𝑃(𝑡 𝑑𝑡 = )𝑃(𝑡 + 𝐷) − 𝑃(𝑡 𝐷 Solar Power Ramp Rates Solar power ramp rate (RR) is the change of solar power during a certain time interval. 3
- 4. Ramp Classes as following: Class1: Ramp up of high rate, |rate| ≥ Tsh Class2: Ramp up of low rate, |rate| < Tsh Class3: Ramp down of high rate, |rate| ≥Tsh Class4: Ramp down of low rate, |rate| < Tsh Classes of Solar Ramp Events Solar Ramp Events Ramp-Down EventsRamp-Up Events Low-RateHigh-Rate Low-RateHigh-Rate Class1 ≥ Class2 0 < Class3 Class4 0 ≥ Class Class1 ≥ Class2 0 < Class3 Class 4 0 ≥ Total Ramp Events at Tsh = 0. / 131 1290 31 2376 3828 |RampRates| Solar Power Ramp Rates 4
- 5. Using Machine learning classification techniques to forecast PV solar power ramp events, which can be implemented several applications in distribution and transmission system. Potential Applications There are applications that rely on ramp event forecasts, such as: • Optimizing the operation of voltage regulation equipment; • Control mechanism of charging and discharging the energy storage systems. EPEX: European power exchange spot trading Optimizing the Transformer's Tap Changer position sequences using the solar forecast Whereas, in the bulk or the transmission level: • Forecasts of ramp events can be used in the trading decisions, and dispatching the operating reserve; • Managing the limits of the ramp rates for reliable and stable operation of electric power systems that have a high-level integrations of renewables. Distribution level: 5
- 6. The classification models that have been implemented for solar power ramp events. 1) Naive Bayes; 2) Linear Discriminant Analysis (LDA); 3) k-Nearest Neighbors (kNN); 4) Decision Tree (DT); 5) Logistic Regression (Log. Reg); 6) Random Forests (RF); 7) Support Vector Machines (SVM) 8) Artificial Neural Network (ANN) Classification Models Block diagram of PV solar power ramp events forecasting models Methodology 6 Training Dataset (Past solar power and weather data) Fitting / Learning Algorithm of (LDA / kNN / DT/ ANN / SVM, etc.) Task: PV Solar Power Ramp Events Forecasting Input Variables (Solar power and weather forecasts) Output Variable (PV solar power ramp events forecasts)
- 7. Diagram of combining the different forecasts Combined Forecasting Models Methodology Random Forest (RF) is chosen to be the ensemble learning method for combining the various models’ outcomes. Fcomb=WA*MA+ WB*MB + WC*MC ….+ WN*MN kNN Weather Data Ensemble Learning (RF) for Combining of Forecasts Combined Forecasts Individual PV Solar Power Ramp Events Forecasting Models Naive Bayes LDA DT Log. Reg RF SVM ANN where WA is an assigned weight for an outcome of model (A) 7
- 8. (a) Solar power forecasts, (b) their ramp rates for 2 days, (c) several weather variables (a) (b) (p.u.)(p.u./hr) Methodology 1) Weather variables, 2) Solar power forecasts, 3) Ramp rates of those solar power forecasts, 4) Class labels of those solar power forecasts. Weather Variables No. Variable Name 1 Cloud Water Content 2 Cloud Ice Content 3 Cloud Cover 4 2-m Temperature 5 Relative Humidity 6 10m-U Wind 7 10m-V Wind 8 Surface Pressure 9 Surface solar radiation down 10 Surface thermal radiation 11 Top net solar radiation 12 Total precipitation 13 Heat Index 14 Polar form of wind Features: (c) Features for classification models are including: So, there are 66 available features. 8
- 9. Data projection onto PC1 and PC2: Visualization Methodology Class Class1 Class2 Class3 Class 4 Total Samples 131 1290 31 2376 3828 Threshold=0.4pu/hr 9 3) All 50 Features (add ramp rates of forecasts) 4) All 66 Features (add class labels of forecasts) 1) All 14 weather variables 2) All 30 Features (add solar power forecasts)
- 10. Class Class1 Class2 Class3 Total Samples 131 31 3666 3828For clarity, low-rate classes (2&4) become as a one class, class3: Visualization Methodology Data projection onto PC1 and PC2: Threshold=0.4pu/hr 10 3) All 50 Features (add ramp rates of forecasts) 4) All 66 Features (add class labels of forecasts) 1) All 14 weather variables 2) All 30 Features (add solar power forecasts)
- 11. Features Selection: 1. Pick up a feature from the available features set; 2. Run the model with this feature; 3. Score the model, by using the following score: Max(Diff. index), where Diff. index is the difference between true and false ramp events; 4. Add another feature to the selected features; 5. Repeat steps 2 and 3; 6. Choose subset of features with the best score, remove the selected from the available features; 7. Repeat steps 1 to 6; 8. If there is no longer any feature to select, Stop. • The wrapping approach has a feedback which informs about the performance of the model with the selected features. • Consider all possible combinations of the available features. Mlle Bouaguel Waad "On Feature Selection Methods for Credit Scoring." (2015). LARODEC, ISG, University of Tunis Methodology Greedy Search - Wrapping Approach: Objective: Increase the true events, Decrease the false events. 𝑇𝑟𝑢𝑒 𝐸𝑣𝑒𝑛𝑡𝑠 & F𝑎𝑙𝑠𝑒 𝐸𝑣𝑒𝑛𝑡𝑠 11
- 12. Data Description: PV solar system is near Canberra, Australia, consisting of 8 panels, its nominal power of (1560W), and panel orientation 38° clockwise from the north, with panel tilt (of 36°). The historical observed solar power data are normalized to the rated capacity (i.e., 1560W). https://crowdanalytix.com/contests/global-energy-forecasting-competition-2014-probabilistic-solar-power-forecasting http://www.ecmwf.int Training Testing The weather forecast data and the available measured solar power data from April 2012 to May 2014. No. Month Year 1 April 2012 2 May 2012 3 June 2012 4 July 2012 5 August 2012 6 September 2012 7 October 2012 8 November 2012 9 December 2012 10 January 2013 11 February 2013 12 March 2013 13 April 2013 14 May 2013 15 June 2013 16 July 2013 17 August 2013 18 September 2013 19 October 2013 20 November 2013 21 December 2013 22 January 2014 23 February 2014 24 March 2014 25 April 2014 26 May 2014 Weather Variables No. Variable Name 1 Cloud Water Content 2 Cloud Ice Content 3 Surface Pressure 4 Relative Humidity 5 Cloud Cover 6 10m-U Wind 7 10m-V Wind 8 2-m Temperature 9 Surface solar radiation down 10 Surface thermal radiation down 11 Top net solar radiation 12 Total precipitation 13 Heat Index 14 Polar form of wind Weather predictions are produced by the global numerical weather prediction system (ECMWF). Case Study Hour-ahead Solar Power Ramp Events Forecasting (European Centre for Medium-Range Weather Forecasts) 12
- 13. • This study presents classification techniques to forecast the solar power ramp events. • The objective of implementing the classification techniques for the solar power ramp events forecasting is to increase the true events and decrease the false events of forecasts of high-rate ramp events. • The moving time window of the rolling forecasts of solar power ramp events is 1 hour (i.e., D, duration=1hr). • The forecasts of solar power ramp events over an entire year are generated and several evaluation metrics are used to assess the forecasts of the ramp events of solar power. • There are 162 high-rate ramp events when threshold (Tsh) =0.4pu/hr. Distribution of the classes of solar power ramp events at threshold (Tsh) =0.4pu/hr. Classes of solar power ramp events in the case study (a) Solar Ramp Events Ramp-Down EventsRamp-Up Events Low-RateHigh-Rate Low-RateHigh-Rate Class1 ≥ Class2 0 < Class3 Class4 0 ≥ Case Study Hour-ahead Solar Power Ramp Events Forecasting 13
- 14. where High denotes high-rate ramp events and Low refers to low rate ramps; True events are when the events are predicted to belong to the same classes as found in the actual observations, while False is indicated when the events are predicted to be in classes other than those found in the actual observations. 𝐷𝑖𝑓𝑓. 𝑖𝑛𝑑𝑒𝑥 = 𝑇𝑟𝑢𝑒 − 𝐹𝑎𝑙𝑠𝑒 𝑜𝑓 𝐻𝑖𝑔ℎ 𝑅𝑎𝑡𝑒 𝐸𝑣𝑒𝑛𝑡𝑠 𝑇𝑜𝑡𝑎𝑙 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑟𝑢𝑒 𝐸𝑣𝑒𝑛𝑡𝑠 𝑇𝑜𝑡𝑎𝑙 𝐸𝑣𝑒𝑛𝑡𝑠 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑟𝑢𝑒 𝐻𝑖𝑔ℎ 𝑇𝑟𝑢𝑒 𝐻𝑖𝑔ℎ + 𝐹𝑎𝑙𝑠𝑒 𝐻𝑖𝑔ℎ 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑟𝑢𝑒 𝐻𝑖𝑔ℎ 𝑇𝑟𝑢𝑒 𝐻𝑖𝑔ℎ + 𝐹𝑎𝑙𝑠𝑒 𝐿𝑜𝑤 𝐵𝑎𝑙𝑎𝑛𝑐𝑒 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 1 𝑐𝑙𝑎𝑠𝑠=1 4 𝑇𝑟𝑢𝑒 𝑐𝑙𝑎𝑠𝑠 𝑇𝑟𝑢𝑒 𝑐𝑙𝑎𝑠𝑠 + 𝐹𝑎𝑙𝑠𝑒 𝑐𝑙𝑎𝑠𝑠 Confusion matrix of possible cases of solar power ramp events Predicted Events High-Rate True High-Rate False High-Rate Low-Rate False Low-Rate True Low-Rate High-Rate Low-Rate Observed Events The following evaluation metrics are used to assess the performance of the classification techniques: The most suitable metrics for our application are the Diff. index and the F1 score. 𝐹1 𝑠𝑐𝑜𝑟𝑒 = 2 ∗ (𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗ 𝑅𝑒𝑐𝑎𝑙𝑙) 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙 Results and Evaluation Objective: Increase the true events, Decrease the false events. 𝑇𝑟𝑢𝑒 𝐸𝑣𝑒𝑛𝑡𝑠 & F𝑎𝑙𝑠𝑒 𝐸𝑣𝑒𝑛𝑡𝑠 14
- 15. Model Parameters Selected Features Naïve Bayes Distribution=Normal; distribution parameters are estimated in the training. 1, 5, 11 LDA Its coefficients (μ) are fitted in the training. 1, 2, 3, 6, 9, 10, 12 Decision Tree Max of splits=15; Min leaf size=1 1, 12 kNN Euclidean distance; k=15 (nearest 15 neighbors) 1, 4, 6, 7, 8 Logistic Regression Its coefficients (β) are fitted in the training. 1, 3, 11, 12 Random Forests Forest size=100 trees; Min. leaf size=1 1, 3, 11, 12 SVM Kernel= Radial basis function; C=184; gamma=5 1, 3, 11, 12 ANN Hidden layer=1; Neurons=10 1, 5, 12 (a) (b) No. Most Important Features 1 Cloud water content, NWP output 2 Cloud cover, NWP output 3 Top net solar radiation, NWP output 4 Hour-ahead combined forecasts of solar power 5 Ramp rates of NWP-driven day-ahead solar power forecasts by ANN 6 Ramp rates of NWP-driven day-ahead solar power forecasts by SVR 7 Ramp rates of hour-ahead combined forecasts of solar power 8 Ramp rates of time-series hour-ahead forecasts of solar power by NARX 9 Ramp classes of persistence hour-ahead forecasts of solar power 10 Ramp classes of NWP-drive day-ahead solar power forecasts by ANN 11 Ramp classes of NWP-driven day-ahead solar power forecasts by SVR 12 Ramp classes of hour-ahead combined forecasts of solar power (a) The most 12 important features out of 66; (b) selected features and parameters for each model Results and Evaluation 15
- 16. Solar power ramp events forecasts by the classification techniques of the high-rate ramp events (162 events) Method Naïve Bayes LDA Decision Trees kNN Logistic Regression Random Forest SVM ANN Combined Classifiers Precision (%) 62% 65% 73% 68% 79% 79% 77% 70% 79% Recall (%) 43% 40% 38% 31% 30% 43% 43% 38% 50% Balanced Precision (%) 75% 78% 80% 78% 59% 80% 80% 78% 87% F1-Score (%) 51% 49% 50% 43% 44% 56% 55% 49% 61% Diff. Index 27 30 38 27 36 51 48 35 60 27 30 38 27 36 51 48 35 60 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Diff.index Percentage(%) Precision (%) Recall (%) Balanced Precision (%) F1-Score Diff(True-False) Results and Evaluation 16
- 17. • This study presents the challenging aspect of ramp forecasting, which is not comparable to studies that detect the ramp events by using historical solar power observations and meteorological measurements. • For a general assessment of the classification model performance for solar power ramp events forecasting, the evaluation metrics that consider the precision and the recall together, such as Diff. Index and F1 score, should be used, in order to properly weigh both the true and the false events of high-rate ramp events. • In the individual classification models, the RF and SVM models yield the most accurate forecasts of solar power ramp events. • In addition, combining the outcomes of the models obtains an improvement of 18% over the best model, and leads to a more robust performance. • The classification techniques (i.e., RF, SVM, and combined classifiers) outperform the solar power forecasts that are used as features to those classification techniques by about 15~40% over the best solar power forecasts, which have Diff. index=42. Conclusions 17
- 18. Thanks for Your Listening Any Question? http://epic.uncc.edu/ Energy Production and Infrastructure Center University of North Carolina at Charlotte Mohamed Abuella mabuella@uncc.edu