Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications
Abstract
:1. Introduction
- This paper explains the specific definition of virtual collection for DPVS, gives the prerequisites for implementing virtual collection and refines virtual collection technology into three key steps. In addition, considering the current lack of research on the virtual collection, this paper illustrates the challenges faced by virtual collection technology through problem analogy.
- Given the challenges faced by virtual collection for DPVS data, this paper summarizes the methods applicable to DPVS similarity analysis, reference power station selection, and DPVS system data inference in various fields to provide theoretical support for the development of virtual acquisition technology.
- In this paper, according to the characteristics of virtual collection for DPVS, four application scenarios of DPVS virtual collection technology are proposed, giving diversified practical application values to the virtual collection technology.
- To the authors’ knowledge, this paper is the first comprehensive introduction, generalization, and summary of DPVS virtual collection and can provide a theoretical reference for subsequent research on DPVS virtual collection methods.
2. Overview of DPVS Virtual Collection
- Similarity analysis of DPVS in the virtual collection region.
- Selection of reference power stations (RPSs) for virtual collection.
- Data inference of DPVS, i.e., accurate estimation of the output of all power stations in the region through computational intelligence.
3. Process and Challenges of DPVS Virtual Collection
3.1. Similarity Analysis of Regional DPVS
3.2. RPS Selection for Virtual Collection
3.3. Data Inference for Regional DPVS
4. Methods for DPVS Virtual Collection
4.1. Similarity Analysis for Virtual Collection
- Distance-based similarity analysis;
- Non-distance-based similarity analysis.
4.1.1. Distance-Based Similarity Analysis
4.1.2. Non-Distance-Based Similarity Analysis
4.2. RPS Selection Methods for Virtual Collection
- Clustering-based algorithms;
- Optimization-based algorithms.
4.2.1. RPS Selection Based on Clustering Algorithm
4.2.2. RPS Selection Based on Intelligent Optimization Algorithm
- Naturalistic optimization algorithms;
- Evolutionary algorithms;
- Swarm intelligence optimization algorithms.
4.3. Data Inference Models for Virtual Collection
- Neural network-based models;
- Ensemble learning-based models.
4.3.1. Data Inference Based on Neural network
4.3.2. Data Inference Based on Ensemble Learning
5. Application Scenarios of Virtual Collection Technology
- DPVS operation data anomaly detection;
- DPVS fault diagnosis;
- DPVS missing data recovery;
- DPVS real-time operation data collection;
6. Conclusions
- The virtual collection process can be subdivided into three steps: similarity analysis, RPS selection, and PV data inference. Considering that there is little research on virtual collection at present, this paper reveals the types of problems similar to virtual acquisition in various fields and analyzes them by analogy so that readers can easily understand the meaning of virtual collection.
- The system analysis of this paper shows that the virtual collection technique requires strict prerequisites and complex data preprocessing. One of the most critical steps is selecting the reference power station, which determines the quality of the inference model input.
- This paper summarizes the methods that can be applied to DPVS virtual collection in various fields. As can be seen, DPVS virtual collection is a novel and comprehensive application of artificial intelligence in the PV industry, involving various machine learning methods, including clustering, optimization, regression, etc.
- This paper proposes a diversified application scenario of virtual collection in the field of PV, hoping to contribute to the needs of distributed energy data management in the context of carbon peaking and carbon neutrality. Our subsequent research will be directed at further explaining virtual collection from the perspective of physical models and providing more examples of virtual collection.
Author Contributions
Funding
Conflicts of Interest
Abbreviations
DPVS | Distributed Photovoltaic Systems |
IEA | International Energy Agency |
RPS | Reference Power Station |
DAP | Data Aggregation Point |
DTW | Dynamic Time Warping |
BDE | Binary Differential Evolution |
ACO | Ant Colony Optimization |
PSO | Particle Swarm Optimization |
WOA | Whale Optimization Algorithm |
HHO | Harris Hawk Optimization |
GWO | Gray Wolf Optimization |
LSTM | Long Short-Term Memory |
RF | Random Forests |
LightGBM | Light Gradient Boosting Machine |
RBF | Radial Basis Function |
AI | Artificial Intelligence |
WNN | Wavelet Neural Networks |
Nomenclature
d(X,Y) | Distance between two samples |
S | Covariance matrix |
Cov(X,Y) | Covariance coefficient |
σ | Standard deviation |
R2(X,Y) | Distance correlation coefficient |
v2(X,Y) | Distance covariance coefficient |
CS(X,Y) | Cosine similarity |
Transfer function | |
Xd(t) | Position of the d-th variable updated according to the original equation |
Position of the d-th binary variable updated by the transfer function | |
h | Output of the hidden layer |
Step size of gradient descent | |
y | Virtual collection data, kW |
Actual data, kW | |
W1 | The weight between the input and hidden layer |
W2 | The weight between the hidden and output layer |
b | Bias of the neural network |
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Methods | References | |
---|---|---|
Distance-based similarity analysis | Minkowski distance | [17,18,19] |
Mahalanobis distance | [20,21] | |
DTW distance | [22,23,24] | |
Non-distance-based similarity analysis | Pearson correlation coefficient | [25,26] |
Distance correlation coefficient | [27,28,29] | |
Cosine similarity coefficient | [30,31] | |
Other similarity coefficients | [32,33,34] |
Methods | References | |
---|---|---|
RPS selection methods based on clustering algorithms | Partition clustering | [35,36,37,38,39,40] |
Hierarchical clustering | [41,42,43] | |
Density-based clustering | [44,45,46,47,48] | |
Grid-based clustering | [49,50,51] | |
RPS selection methods based on optimization algorithms | Natural-like optimization algorithms | [52,53,54,55,56,57,58] |
Evolutionary algorithms | [59,60,61,62,63,64] | |
Swarm intelligence optimization algorithm | [65,66,67,68,69,70,71] |
Methods | References | |
---|---|---|
Neural network-based data inference models | Improved traditional artificial neural network | [75,76,77,78] |
Deep neural network | [79,80,81,82,83,84,85,86,87] | |
Ensemble learning-based data inference models | Bagging ensemble strategy | [88,89,90] |
Boosting ensemble strategy | [91,92,93,94,95,96] | |
Stacking ensemble strategy | [97,98,99] |
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Ge, L.; Du, T.; Li, C.; Li, Y.; Yan, J.; Rafiq, M.U. Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications. Energies 2022, 15, 8783. https://doi.org/10.3390/en15238783
Ge L, Du T, Li C, Li Y, Yan J, Rafiq MU. Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications. Energies. 2022; 15(23):8783. https://doi.org/10.3390/en15238783
Chicago/Turabian StyleGe, Leijiao, Tianshuo Du, Changlu Li, Yuanliang Li, Jun Yan, and Muhammad Umer Rafiq. 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications" Energies 15, no. 23: 8783. https://doi.org/10.3390/en15238783