Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues
Abstract
:1. Introductions
2. Research on Data Stream Classification Methods and Their Applications
2.1. Data Stream Classification Methods
2.2. Application of Data Stream Classification to Wind and Photovoltaic Power Data
2.2.1. Data Streaming in Wind Power
- Fault Diagnosis Based on Electrical Indicators
- 2.
- Fault Diagnosis Based on Vibration Signals
2.2.2. Data Streaming in Photovoltaics
3. Functions for Various Drifts in the Data Stream and Their Forms
3.1. Covariate Drift
3.2. Prior Probability Drift
3.3. Concept Drift
3.3.1. Different Forms of Concept Drift
- Label drift
- 2.
- Feature Drift
- 3.
- Instance Drift
3.3.2. Concept Drift over Different Time Intervals
3.3.3. Concept Drift Detection Methods
- Error Rate-based Concept Drift Detection Method
- 2.
- Window-based Concept Drift Detection Methods
- 3.
- Concept Drift Detection Method based on Data Distribution
- 4.
- Multiple Hypothesis Testing Drift Detection Methods
3.3.4. Model Updating Strategies for Addressing Conceptual Drift
3.4. The Issue of Concept Drift in Data Stream Classification Research within Energy Systems
4. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ADWIN | Adaptive Sliding Window Algorithm |
BP | Back Propagation |
CM | Competence Model-Based Drift Detection |
CEVDT | Concept-Adapting Evolutionary Algorithm for Decision Tree |
CVFDT | Concept-Adapting Very Fast Decision Tree |
CFS | Correlation-Based Approach to Attribute Selection |
CUSUM | Cumulative Sum |
DMM | Drift Detection Method |
DELM | Dynamic Extreme Learning Machine |
DWM | Dynamic Weighted Majority |
EDDM | Early Concept Drift Detection Method |
e-Detector | Ensemble of Detectors |
EDE | Equal Density Estimation |
ECDD | EWMA for Concept Drift Detection |
EWMA | Exponentially Weighted Moving Average Charts |
FHDDM | Fast Hoeffding Drift Detection Method |
FW-DDM | Fuzzy Windowing Drift Detection Method |
GAN | Generative Adversarial Networks |
HDDM | Heoffding’s Inequality Based Drift Detection Method |
HCDTs | Hierarchical Change-Detection Tests |
HHT-CU | Hierarchical Hypothesis Testing with Classification Uncertainty |
HLFR | Hierarchical Linear Four Rate |
HGA | Hybrid Genetic Algorithms |
IV-Jac | Information Value and Jaccard Similarity |
ITA | Information-Theoretic Approach |
ID | Instance Drift |
KS | Kolmogorov–Smirnov |
KL | Kullback–Leibler |
LLDD | Learning with Local Drift Detection |
LFR | Linear Four Rate Drift Detection |
LDD-DSDA | Local Drift Degree-Based Density Synchronized Drift Adaptation |
LSTM | Long Short-Term Memory |
NLA | Normalized Likelihood Allocation |
OCDD | One-Class Drift Detector |
OGMMF-VRD | Online Gaussian Mixture Model with Noise Filter for Handling Virtual and Real Concept Drifts |
OS-PCA | Oversampling Principal Component Analysis |
PL | Paired Learners |
PV | Photovoltaic |
RDDM | Reactive Drift Detection Method |
SCD | Statistical Change Detection |
STEPD | Statistical Test of Equal Proportion Detection |
SVMs | Support Vector Machines |
UCVFDT | Uncertainty-Handling and Concept-Adapting Very Fast Decision Tree |
VFDT | Very Fast Decision Tree |
WTGs | Wind Turbine Generators |
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Category | Algorithms |
---|---|
Error rate-based | DDM [67] |
EDDM [69] | |
ECDD [70] | |
RDDM [71] | |
FW-DDM [72] | |
HDDM [73] | |
LLDD [74] | |
DELM [75] | |
Window-based | ADWIN [76] |
OCDD [77] | |
PL [78] | |
STEPD [79] | |
FHDDM [80] | |
GDDM [81] | |
Data distribution-based | ITA [63] |
SCD [84] | |
CM [56] | |
EDE [91] | |
LDD-DSDA [66] | |
Multiple hypothesis testing | JIT [84] |
LFR [85] | |
IV-Jac [59] | |
e-Detector [86] | |
DDE [87] | |
HCDTs [87] | |
HLFR [89] | |
HHT-CU [62] |
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Zhu, X.; Wu, Y.; Zhao, X.; Yang, Y.; Liu, S.; Shi, L.; Wu, Y. Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues. Energies 2024, 17, 4371. https://doi.org/10.3390/en17174371
Zhu X, Wu Y, Zhao X, Yang Y, Liu S, Shi L, Wu Y. Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues. Energies. 2024; 17(17):4371. https://doi.org/10.3390/en17174371
Chicago/Turabian StyleZhu, Xinchun, Yang Wu, Xu Zhao, Yunchen Yang, Shuangquan Liu, Luyi Shi, and Yelong Wu. 2024. "Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues" Energies 17, no. 17: 4371. https://doi.org/10.3390/en17174371
APA StyleZhu, X., Wu, Y., Zhao, X., Yang, Y., Liu, S., Shi, L., & Wu, Y. (2024). Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues. Energies, 17(17), 4371. https://doi.org/10.3390/en17174371