# A State-of-Art-Review on Machine-Learning Based Methods for PV

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## Abstract

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## 1. Introduction

- This is the first paper, as far as authors know, which gathers only more recent and promising, in authors’ opinion, applications of ML in many different fields of PV and not only in a specific one,
- For each of the fields under consideration a critical analysis is reported, highlighting the architecture/solution that, in literature, has proven to be the most suitable for that specific task,
- The pros and cons of each solution are detailed, in addition to suggesting ideas for further investigation.

## 2. Machine Learning, Deep Learning and Related Methods

- Bagging
- Boosting
- Stacking

- Random Forest (RF) (bagging ensemble method);
- XGBoost or LightGBM (boosting ensemble method).

## 3. Literature Review of Review Paper for Each of the Fields of Interest in PV

## 4. Latest Research in PV Power Forecasting

- Point-forecast
- Interval-forecast

- Very short-term, from few seconds to some minutes;
- Short-term, up to 48 or 73 h;
- Medium-term, in the range from few days to one week;
- Long-term, usually several months or one year.

- Its hyper-parameters and network structure, i.e., number of layers and types, have already been tested and found to be successful;
- The earlier layers of a CNN are essentially learning the basic features of the image sets such as edges, shapes, textures, etc. Only the last one or two layers of a CNN are performing the most complex tasks of summarizing the vectorized image data into the classification. Weights of the first layers are frozen while only the last layers are trained for the specific task in the target domain knowledge; this turns out to be a faster training method.

**Table 2.**Point-forecast ML-based methods for PV power production. Publication year considered: 2018–2021.

Year | Reference | Forecasting Horizon & Sampling | Parameters | Tested on One Location or Regional | Methods & Notes |
---|---|---|---|---|---|

2021 | [31] | 1–16 steps ahead 15 min | Forecasted irradiance Historical powers | One location | DELM model that uses a SD training data selection method based on grey correlation analysis (applied on irradiance values) and Pearson correlation (applied on power production value). A novel decomposition method ECBO-VMD for power production time series. Fast training time for DELM. Forecasting horizon from 15 min to 4 h (1–16 steps-ahead if data is sampled every 15 min). Results compared with other DL models show great accuracy in everyday conditions, especially for 1–2 steps ahead. |

2021 | [32] | One day ahead 30 min | Historical weather Historical power Power from a physical model | Regional | AML model providing an ensemble of Elastic Net CV regression, Gradient Boosting Regression and RF Regression. An improved GA algorithm is used to select optimal features for the base models varying in each region. A physical model adds power production base prediction level, improving results of the final model. |

2021 | [33] | Annually/Quarterly | Historical power | Two locations (three cases/datasets) | A novel discrete grey model with time-varying parameters known as ATDGM(1,1). Almost 10/11 years for training and one or two years for testing. Results benchmarked with ARIMA, SARIMA, BPNN, LSTM and SVR models. |

2021 | [35] | G_{h} every 15 minP _{wind} hourly | Historical Solar energy & Wind energy (Public datasets) | 10 sites for solar energy 7 wind farms | Evolving Multivariate Fuzzy Time Series (E-MVFTS) + Typicality and Eccentricity Data Analytics (TEDA). Interesting methodology to detect concept drift. The model was developed in Python using the pyFTS library. |

2020 | [36] | One hour ahead 5 min | Historical power Historical meteorological data | One location | A hybrid DL model combining wavelet packet decomposition (WPD) and long short term memory (LSTM) networks. Comparisons with individual LSTM, RNN, GRU and MLP. |

2020 | [56] | 12 to 24 h ahead Hourly | Historical weather Weather forecast Historical powers | One location | LSTM network that employs a synthetic irradiance forecast derived using a k-MEANS classification algorithm resulting in an improvement in the obtained accuracy of 33%, concerning using the hourly type of sky forecast, or 44% over using the daily type of sky forecast. |

2020 | [37] | Day-ahead 15 min | Historical power direct normal irradiance (DNI) and temperature | One location | An ensemble formed by LSTM-RNN and a Time Correlation Modification model (TCM) whose coefficient is moduled by a partial daily pattern prediction (PDPP) framework. |

2020 | [38] | 10 min 1–4 weeks | Historical irradiance Historical power | One location | A share-optimized-layer LSTM (SOL-LSTM) network, whose hyperparameters are optimized using Sequential Model-Based Optimization (SMBO), where Transfer Learning (TF) is applied from a source domain, solar irradiance series (historical data), to the target domain, power production series, to overcome scarcity in training data. |

2020 | [39] | 1–12 steps ahead 30 min | Historical weather features | One location | LightGBM models combined with a temporal pattern aggregation and TS-SOM for weather clustering. Interesting performances from an accuracy point of view but also as training and inference time, even in edge devices. |

2020 | [40] | 1–150 steps ahead 5 min | Historical weather features Historical power | One location | Hybrid model made up by BPNN for final forecasts whose training data are PV power historical data decomposed by CEEMD algorithm and weather selected by RF and data-optimized by IGIVA |

2019 | [41] | 1–8 steps ahead 7.5 min | Historical temperature and power | One location | Ensemble model of two LSTMs with Attention Mechanism, one for temperature series and one for power series. |

2019 | [42] | 1–24 steps ahead 1 h | Weather forecasts Day-ahead Hourly | Ensemble, using ridge regression, of RF models using a preliminary cluster analysis of weather forecasts | |

2019 | [43] | Not applicable | Not applicable | Not applicable | R-GAN to generate realistic data to be used for training energy forecasting models |

**Table 3.**Interval-forecast ML-based methods for PV power production. Publication year considered: 2019–2021.

Year | Reference | Forecasting Horizon & Sampling | Parameters | Tested on One Location or Regional | Methods & Notes |
---|---|---|---|---|---|

2021 | [47] | 1–6 h ahead (21 steps) 15 min | Historical Weather Historical power (inverter level and plant level) Forecast altitude & azimuth sun position (pvlib-solar position) | One location | FFNN & LSTM-RNN+GRU-RNN |

2021 | [50] | 1–24 h ahead Hourly data | Direct, diffuse and horizontal solar irradiance, temperature, zenith & azimuth solar position | Five locations | Gaussian process regression (GPR) with Matern 5/2 kernel function on pre-clustered data (by k-means) |

2020 | [51] | 1–24 h ahead Hourly data | Solar irradiance, temperature, humidity, historical PV power | One location | Quantile CNN (QCNN), two-stage training strategy to solve the training problem of the QCNN caused by the non-differentiable loss functions of the QR. PI and PINAW provided |

2020 | [52] | 1,3,6 h ahead | Weather data Historical PV power | One location | Hybrid model WT+RBFNN+PSO. PI provided using Bootstrap and results compared QR. Bootstrap obtains better results in terms of reliability diagrams for the PI. |

2019 | [53] | 30 min–36 h ahead 30 min | Forecast from NWP Satellite images to estimate GHI PV power, temperature, GTI, clear-sky profile using McClear model | Three locations | Analog Ensemble (AnEn) model using NWP data, satellite images and in situ data. State-of-the-art results in 5–36 h horizon. |

## 5. The Latest Research on Anomaly Detection (a.k.a. Fault Detection) and Diagnosis in PV

- Analysis of string/panel current and/or voltage, or current/voltage measured at the inverter with the use of exogenous variables as environmental ones,
- Image analysis performed mainly by infrared (IFR) images detected by Unmanned Aerial Vehicle (UAV),
- Clustering-based techniques that can detect anomalies using unlabelled data.

- A discussion of anomalies/faults analyzed in literature with ML-based methods
- Suggestions on which approach from the most current literature review (from 2018 till 2021) seems to produce better results
- Common challenges and insight on possible future trends

#### Detectable Faults by ML-Based Methods

_{MPP}), current at MPP (I

_{MPP}), OC voltage (V

_{OC}) and SC current (I

_{SC}), almost always supported by environmental variables such as ambient and module temperature and solar irradiance at the panel level. These models necessarily require a labelled dataset and are mainly based on the difference between the models’ predicted system performance and the real measured one. Many ML-based models that employ SNN apply input pre-processing as Discrete Wavelet Transform (DWT); this is a typical form of feature engineering that has proven to be beneficial to improve the FD accuracy of the model. For the faults described so far, the models usually employed consist of SNNs of various typologies, but also DT ensembles such as RF or 1-SVM. Considering faults detectable using image analysis as module delamination/crack, hotspot or soiling (dust and birds’ droppings), this is a field dominated by DL and especially CNNs trained on thermal infrared (IR) images acquired by UAV. For detecting faulty cells or modules electroluminescence (EL) images are also considered, while at the array level only IR images, generally EL images, embed more fault information and are the preferred type of images. The type of CNN used in this field varies from pre-trained known CNN architectures such as LeNet and VGG-16 to custom architecture. This is a field where Transfer Learning [38] can be very beneficial and where data augmentation techniques are also very common (image rotation, flip, etc.).

## 6. The Latest Research on MPPT in PV

_{reached}, and that one that should be obtained, P

_{GMPP}, has been calculated as:

Year | Reference | ML Method | Description | Results on Reached Power | Transient Response | Simulation/ Experimental | Advantages |
---|---|---|---|---|---|---|---|

2021 | [100] | ANN, segmentation-based approach and hill-climbing | The paper deals with the feasibility study and implementation of a novel easy and cost-effective hybrid two-stage GMPPT algorithm. The first stage synergically combines two different methods to predict the optimal operating condition: an ANN-based algorithm and a segmentation-based approach. A traditional hill-climbing method is used in the second stage to finely track MPP. Various ANN structures have been implemented and tested. | Figures show the MPP_ratio (maximum value 99.55%) | - | Simulation (Matlab) | Very fast dynamic behaviour |

2021 | [101] | PSO, ANN GA-FLC, PSO-FLC, GA-ANN and Combined GA-FLC-ANN | Two artificial intelligence-based MPPT systems are proposed in the paper for grid-connected PV units. The first design is based on an optimized FL control using a genetic algorithm and PSO for the MPPT system. In turn, the second design depends on the genetic algorithm-based ANN. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the MPPT system. The simulation results demonstrate that the GA/PSO-FLC and the GA-ANN-based MPPT methods have significant improvement in terms of the output DC power and the tracking speed. | Quantitative evaluation of INC, GA-FLC, PSO-FLC, GA-ANN and Combined GA-FLC-ANN | Rise time = [0.0168s–0.0251s] | Simulation (Matlab) | |

2021 | [102] | Backstepping terminal sliding mode control (BTSMC) | A nonlinear BTSMC is proposed for maximum power extraction. The system is finite-time stable and its stability is validated through the Lyapunov function. A DC-DC buck-boost converter is used to deliver PV power to the load. For the proposed controller, reference voltages are generated by an RBF NN. | MPP_ratio = 98.74% Under varying climatic conditions = 98.72% Under faulty condition | Simulation (Matlab/Simulink) | Best performance of the proposed control technique in all conditions | |

2020 | [95] | MFA + ANFIS + P&O | After being trained using the modified firefly algorithm (MFA), the ANFIS (adaptive neuro-fuzzy inference system) based on the radiation conditions on solar panels provides a quantity as the optimal duty cycle, from which point the P&O algorithm starts to enter the tracking cycle and tries to detect the MPP under partial shading conditions. | MPP_ratio = [65.05–99.95%] | - | Simulation (Simulink) | High speed in tracking the MPP |

2020 | [103] | RL + DL | The deep Q-network (DQN) and deep deterministic policy gradient (DDPG) are proposed to harvest the MPP in PV systems, especially under a PSC. Two robust MPPT controllers based on DRL are proposed, including DQN and DDPG. Both algorithms can handle the problem with continuous state spaces, in which DQN is applied with discrete action spaces while DDPG can deal with continuous action spaces. Rather than using a look-up table in the RL-based method, DRL uses neural networks to approximate a value function or a policy so that high memory requirement for sizeable discrete state and action spaces could be significantly reduced. | Powers increase by 17.9% (DQN) and 15.4% (DDPG) | Simulation (Matlab/Simulink) | No prior model of the control system is needed. Significant tracking speed | |

2020 | [77] | Q-learning-based | The paper presents a novel GMPPT method that is based on the application of a machine-learning algorithm (Q-learning-based method). | MPP_ratio = [97.1–99.7%] | Simulation (Matlab/Simulink) | - (a)
- it does not require knowledge of the operational characteristics of the PV modules and the PV array comprised in the PV system;
- (b)
- it is capable of detecting the GMPP in significantly fewer search steps.
| |

2020 | [76] | GRNN and Support Vector Regression (SVR) | The main contribution of the work is to predict the optimum reference voltage of the PV panel at all-weather conditions using ML strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. | RMSE = 0.0278 (SVR) RMSE = 0.044 (GRNN) | Simulation (Matlab/Simulink) | Robust against internal and external disturbances | |

2020 | [104] | ANN | The authors propose a simple MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. The expression for the output current of the NN model is used to develop an analytical, gradient MPPT algorithm which can provide high prediction accuracy of the maximal power. Finally, to avoid the usage of the pyranometer, a simple irradiance estimator, which is also based on the identified NN model, has been proposed. The presented algorithm has smaller computational complexity compared to the other NN-based MPPT algorithms, in which the MPP position is predicted by one multilayer NN or by two single-layer NNs. | Relative error between the predicted and true maximal power:- P&O = [0.011–32.397%]
- equivalent circuit (EMPPT) [0.366–56.772%]
- NN-based MPPT [0.0001–18.881%]
- cascade NN-based MPPT [0.003–0.251%]
| Simulation | Low computation complexity | |

2020 | [105] | DT, Multivariate Linear Regression (MLR), Gaussian Process Regression (GPR), Weighted K-Nearest Neighbors (WK-NN), Linear Discriminant Analysis (LDA), Bagged Tree (BT), Naïve Bayes classifier (NBC), SVM, RNN | Nine ML-based MPPT techniques, by presenting three experiments under different weather conditions, in case of no sensor, are introduced. DT, Multivariate Linear Regression (MLR), Gaussian Process Regression (GPR), Weighted K-Nearest Neighbors (WK-NN), Linear Discriminant Analysis (LDA), Bagged Tree (BT), Naïve Bayes classifier (NBC), SVM and Recurrent Neural Network (RNN) performances are validated. | RMSE: DT = 0.42 WK-NN = 0.37 MLR = 0.44 LDA = 0.48 BT = 0.73 GPR = 0.4 NBC = 0.51 SVM = 0.14 RNN = 0.36 | Training time: DT = 0.91 s WK-NN = 0.78 s MLR = 6.17 s LDA = 2.32 s BT = 2.35 s GPR = 5.04 s NBC = 8.56 s SVM = 1.1178 s RNN = 8.9 s | Simulation (Matlab/Simulink) | Give the possibility to compare different ML algorithms |

2020 | [106] | FL and ANFIS | An FLC with a reduced number of rules-based MPPT and ANFIS based MPPT have been developed and tested in MATLAB/Simulink environment, based on the simulation it can be concluded that with both controllers the PV panel can deliver the maximum power. However, the performance of fuzzy with reduced rules MPPT is better than ANFIS based MPPT in terms of tracking speed and static error due to its reduced number of rules (8) Table instead of conventional (25) which makes it lighter and improves global performance. | Static error = 0.016% (FLC With reduced Rules) 0.020% (ANFIS) | Tracking time = 0.005 s (FLC With reduced Rules) 0.011 s (ANFIS) | Simulation (Matlab/Simulink) | |

2019 | [94] | Fuzzy neural network (FNN) | An FNN controller based on the MPPT technique has been designed and implemented to control the duty cycle of a boost converter and to elicit the maximum power from the PV cells. The FNN controller is also refined using a gradient descent-based back-propagation algorithm to obtain optimal results. | MPP_ratio = [96.09–96.67%] | - | Simulation (Matlab/Simulink) | The FNN controller has good stable sets of responses where there is no oscillation around the optimal value. |

2019 | [92] | Sequential Monte–Carlo (SMC) filtering + ANN | An improved MPPT method for PV systems method is proposed utilizing the state estimation by the sequential Monte–Carlo (SMC) filtering, which is assisted by the prediction of MPP via an ANN. A state-space model for the sequential estimation of MPP is proposed in the framework of the INC MPPT approach. The ANN model is based on the input of the voltage and current or the irradiance measurements and predicts the generalised local log-likelihood ratio (GLLR) given the knowledge learned from training data. Furthermore, the ANN-based refinement is triggered only when the proposed GLLR change detector declares the irradiance change, which decreases the number of redundant ANN predictions when the irradiance is steady. | Prediction quality index = [87.7–96.2%] | SMC = 0.22 s I-C = 0.35 s | Simulation (Simscape Power Systems in Matlab) | Efficient and economical MPPT solution |

2019 | [78] | Reinforcement learning -Q-Table and the RL-Q-Network (QN) | Two reinforcement learning-based MPPT (RL MPPT) methods are proposed by the use of the Q-learning algorithm. One constructs the Q-table and the other adopts the Q-network. These two proposed methods do not require the information of an actual PV module in advance and can track the MPP through offline training in two phases: the learning phase and the tracking phase. A Markov decision process model is suitable for describing the interaction between the circuit connected to the PV module and the controller. An MDP model consists of four elements, which are state, action, transition and reward. With the MDP model described, an RL-QT MPPT method is proposed by constructing the Q-table to perform MPPT control. However, the state representation is needed to be discretized for the tabular method, which may cause the loss of MPPT control accuracy. Therefore, a Q-network-based MPPT method is proposed. In the RL-QN MPPT method, the Q-table is approximated by a neural network, so that the discretization of the states is not needed. | Quantitative evaluation | Experimental | Small oscillations and high average power | |

2019 | [107] | Transfer reinforcement learning (TRL) | The paper aims to introduce a novel maximum power point tracking (MPPT) strategy called TRL, associated with space decomposition for PV systems under PS conditions (PSC). The space decomposition is used for constructing a hierarchical searching space of the control variable, thus the ability of the global search of TRL can be effectively increased. | Quantitative evaluation | - | Simulation | Fast convergence and a high convergence stability |

2019 | [108] | ANN + Backstepping Sliding Mode (BSM) | The paper presents a novel hybrid technique for tracking the maximum power point of the photovoltaic panel. This approach includes two loops: the first one is the ANN loop that is used to quickly predict the desired voltage, which minimizes the calculation and allows a rapid system response. While the second loop consists of a combination of the sliding mode and the backstepping control approaches, the main aim is to track the reference voltage that is generated by the ANN loop, the second purpose is to have a rapid, robust and accurate system under various and difficult changes of meteorological conditions. The proposed technique is compared with the conventional algorithms and the hybrid controllers, ANN combined with the Integral sliding mode controller and ANN combined with the backstepping controller, to prove its effectiveness and tracking performance. | Figures show the effectiveness of the proposed approach | Simulation (Matlab) | A robust controller | |

2019 | [109] | Neuro-fuzzy | In the paper, an IC-based variable step size Neuro-Fuzzy MPPT controller has been propose and investigated. The proposed NF MPPT controller is developed firstly in the offline mode required for testing a different set of neural network parameters to find the optimal neural network controller used secondly in the online mode to track the output power of the PV system under different atmospheric conditions. The inputs variables for NF MPPT are the same as the IC algorithm inputs i.e., I and V, while the output power is the PWM ratio used to drive the DC-DC boost converter. | Figures show the effectiveness of the proposed approach | Simulation (Matlab/Simulink) | Response time, ripple, steady-state oscillation accuracy | |

2019 | [110] | ANN | The authors design an MPPT controller based on an ANN for a solar structure using Boost and Cuk converter topology. The performances of the proposed solution are analyzed under uniform and varying climatic. Cuk converter provides good performance under all climatic conditions but the main disadvantage is its cost which is comparatively high than that of the Boost converter. | MPP_ratio = 95.5% (boost) and 99.56% (Cuk) | Rise Time (μs) = 600.6 (boost) 465.1 (cuk) Settling time (μs) = 801(boost) 757.4 (cuk) | Simulation (Matlab/Simulink) | Good performance with accurate tracking, high efficiency and low oscillation under uniform and rapidly changing climatic conditions |

2018 | [111] | SVM and extreme learning machine (ELM) | A customized MPPT design was proposed to determine the optimal step sizes according to three different weather types. The weather-type labelling was automatically provided by a supervised learning classification system. Two classical machine learning technologies were employed and compared, including SVM and ELM. The classification probability from SVM or ELM is deployed as the confidence level and is designed as a fuzzy-weighted classification system to further improve the MPPT design. | Classification accuracy reaches over 90% for both SVM and ELM methods | Simulation (Matlab/Simulink) | High MPPT efficiency by using a low-cost simple micro-controller | |

2018 | [98] | Bayesian fusion | An intelligent Bayesian network technique is proposed for global MPP tracking of a PV array under partial shading conditions. The algorithm sweeps the output voltage of a DC-DC converter, measures the corresponding current, computes the resulting power, and uses the Bayes rule to compute an estimate of the MPP. A PID controller is used for a more efficient real-time controller with minimum overshoot and minimum rise time in output power. | η = 98.9% (simulation) η = 98.4% (Experimental) | 1.72 s (simulation) and 1.86 (experimental)when the time interval of the irradiation change is 10 s–20 s when G = 1000 W/m^{2} to G = 500 W/m^{2}1.81 s (simulation) and 1.88 (experimental)when the time interval of the irradiation change is 20 s–30 s when G = 500 W/m ^{2} to G = 800 W/m^{2} | Simulated (Matlab) and then experimentally validated | Enhanced response time and efficiency |

2018 | [99] | ANN + hill climbing | A global maximum power point tracking algorithm including an ANN and a hill-climbing method is combined. The proposed solution is suitably designed for handling fast-changing partial shading conditions in photovoltaic systems. Through only a limited number of preselected current measurements, the proposed algorithm is capable of automatically detecting the global maximum power point of the photovoltaic array and also minimizing the time intervals required to identify the new optimal operating condition. | QI1 = [8.96–14.26%] | - | Simulation | Does not require any information on the environmental operating conditions and it is cost-effective, with no additional hardware requirements |

2018 | [112] | ANN and FL | Authors propose a new MPPT algorithm based on FL and an ANN to improve the performances of a system that consists of three main parts: PVG, a DC-DC boost converter and a DC motor coupled with a centrifugal water pump. The ANN is used to predict the optimal voltage of the PVG, under different environmental conditions (temperature and solar irradiance) and the fuzzy controller is used to command the DC-DC boost converter. The proposed algorithm gives better stability and accuracy to the system compared to P&O-based MPPT. | Comparison based on figures | - | Simulation (Matlab/Simulink) | |

2018 | [113] | ||||||

2018 | [114] | Coarse-Gaussian SVM and ANN | The paper introduces an innovative MPPT algorithm that combines two powerful ML techniques of coarse-Gaussian SVM (CGSVM) (a particular type of classification learning technique) and an ANN as the ANN-CGSVM technique. The results of the proposed MPPT algorithm were compared with that of Adaptive Neuro-Fuzzy Inference Systems (ANFIS), conventional ANN and the hybrid of ANN and P&O (ANN-PO) results to verify the proposed algorithm performance for the MPPT task. The obtained results suggested that the CGSVM classifier could extract considerable power from the PV panel under varied weather conditions. | MPP_ratio = [69.34–98.99%] | Tracking time between 0.006 s and 1.486 s | Simulation | Good efficiency and the convergence speed |

## 7. Other Applications in the PV Field

## 8. Concluding Remarks and Future Trends

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## Nomenclature

AI | Artificial Intelligence |

ABC | Artificial Bee Colony |

ACO | Ant Colony Optimization |

AD | Anomaly Detection |

AM | Attention Mechanism |

AML | Auto-ML |

AnEn | Analog Ensemble |

ANFIS | Adaptive Neuro-Fuzzy Inference Systems |

ANN | Artificial Neural Networks |

ARD | Automatic Relevance Determination |

BE | Bagging Ensemble |

BIPV/T | Building-integrated PV/T |

BPNN | Backpropagation NN |

BR | Bayesian Regression |

BSM | Back-stepping Sliding Mode |

BT | Bagged Tree |

BTSMC | Backstep-ping terminal sliding mode control |

CEEMD | Complementary Ensemble Empirical Mode Decomposition |

CGSVM | coarse-Gaussian SVM |

CI | Confidence Interval |

CNN | Convolutional Neural Network |

CPRS | Continuous Ranked Probability Score |

CPV | Concentrating PV |

DDPG | Deep Deterministic Policy Gradient |

DELM | Deep Extreme LM |

DIFPSO | Dynamic Factor PSO |

DL | Deep Learning |

DNN | Deep Neural learning/Network |

DQN | Deep Q-network |

DQR | Direct Quantile Regression |

DT | Decision Tree |

DWT | Discrete Wavelet Transform |

ECBO | Enhanced Colliding Bodies Optimization |

EI | Error Indicator |

EL | Electroluminescence |

ELM | Extreme Learning Machines |

EM | Ensemble Methods |

EMD | Empirical Mode Decomposition |

E-MVFTS | Evolving Multivariate Fuzzy Time Series |

FD | Fault Detection |

FDD | Fault Detection and Diagnosis |

FFNN | Feedforward Neural Network |

FL | Fuzzy Logic |

FLC | Fuzzy Logic Control |

FNN | Fuzzy neural network |

GA | Genetic Algorithm |

GAN | Generative Adversarial Network |

GFF | Generalized Feed-Forward |

GLLR | Generalized Local Log-likelihood Ratio |

GMPP | Global MPP |

GPR | Gaussian Process Regression |

GRNN | Generalized Regression Neural Network |

GRU | Gated Recurrent Unit |

HCPV | High CPV |

HIF | High Impedance Fault |

IFR | Infrared |

IGIVA | Improved Grey Ideal Value Approximation |

I_{MPP} | Current at MPP |

INC | Incremental Conductance |

IR | Infrared |

IRT | Infrared Thermography |

IS | Isolation Forest |

I_{SC} | SC current |

KDE | Kernel Density Estimation |

KNN | k-Nearest Neighbour |

LDA | Linear Discriminant Analysis |

LG | Line to Ground fault |

LL | Line-to-Line |

LLG | Double Line to Ground fault |

LLLG | Three-phase fault |

LOF | Local Outlier Factor |

LSSVM | Least-squares SVM |

LSTM | Long short-term memory |

MAE | Mean Absolute Error |

MAPE | Mean Average Percentage Error |

MCC | Matthews Correlation Coefficient |

ME | Mean Error |

ML | Machine Learning |

MLP | Multilayer Perceptron |

MLR | Multivariate Linear Regression |

MPE | Mean Percentage Error |

MPP | Maximum Power Point |

MSE | Mean Square Error |

MVFTS | Multivariate Fuzzy Time Series |

NBC | Naïve Bayes classifier |

NNQF | Nearest Neighbours Quantile Filter |

NWP | Numerical Weather Prediction |

OC | Open Circuit |

P&O | Perturb and Observe |

Probability Density Function | |

PDPP | Partial Daily Pattern Prediction |

PDPP | Partial Daily Pattern Prediction |

PEC | Performance Evaluation Criteria |

PI | Prediction Interval |

PINAW | Prediction Interval Normalized Average Width |

PS | Partial Shading |

PSC | PS Conditions |

PSO | Particle Swarm Optimization |

PV/T | PV/Thermal |

QCNN | Quantile CNN |

QDA | Quadratic Discriminant Analysis |

QELM | Quantile Extreme Learning Model |

QESN | Quantile Echo State Network |

QI1 | quality indicator |

QN | Q-Network |

QR | Quantile Regression |

QRF | Quantile Regression Forest |

RBF | Radial Basis Function |

RF | Random Forest |

RGAN | Recurrent Generative Adversarial Network |

RL | Reinforcement Learning |

RMSE | Root Mean Square Error |

RMSQP | Root Mean Squared Percentage Error |

RT | Random Tree |

SC | Short Circuit |

SI | Swarm Intelligence |

SMBO | Sequential Model-Based Optimization |

SMC | Sequential Monte–Carlo |

SMO | Sequential Minimal Optimization |

SNN | Shallow Neural Networks |

SOFM | Self-organization feature map |

SOL-LSTM | Share-Optimized-Layer LSTM |

SVM | Support-Vector Machines |

SVR | Support Vector Regression |

TCM | Time Correlation Modification |

TCM | Time Correlation Modification model |

TEDA | Typicality and Eccentricity Data Analytics |

TEDA | Typicality and Eccentricity Data Analytics |

TL | Transfer Learning |

TRL | Transfer Reinforcement Learning |

TS-SOM | Tree-Structured Self-Organized Map |

UAV | Unmanned Aerial Vehicle |

VMD | Variational Mode Decomposition |

V_{MPP} | Voltage at MPP |

V_{OC} | OC voltage |

WK-NN | Weighted K-Nearest Neighbors |

WPD | Wavelet Packet Decomposition |

WPT | Wavelets Packet Transform |

WT | Wavelet Transform |

Greek symbols | |

σ | Standard Deviation error |

η | Overall power tracking efficiency |

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**Figure 2.**An example of the power-voltage characteristic of a photovoltaic (PV) array under partial shading conditions [77].

Year | Reference | Notes |
---|---|---|

2018 | [21] | A review of ML and statistical models based on historical data. Concludes that ANNs and Support-Vector Machines (SVMs) are the best-performing models, especially due to their capability to rapidly adapt to varying environmental conditions. Genetic Algorithms (GAs) result as the most frequently used method in optimizing forecasting models’ hyper parameters. |

2019 | [22] | A very interesting review, from the taxonomy point of view, of AI-based methods in solar power forecasting. Methods analyzed include ANNs, SVMs, Extreme Learning Machines (ELMs), Recurrent Neural Networks (RNNs), Long short-term memory (LSTM), RF, stacked Auto-Encoders, Generative Adversarial Networks (GANs), Fuzzy Logic (FL), Particle Swarm Optimization (PSO) and others. For each method is indicated their pros & cons and optimal field of application. This paper outlines challenges and future research directions, mainly: probabilistic prediction of solar energy, model explainability and prediction of the movement and thickness of clouds. |

2019 | [23] | A review focused only on DL methods for renewable energy forecasting, both deterministic and probabilistic (deep belief network, stack auto-encoder, deep recurrent neural network, etc.) Forecasting horizon from 15 min ahead to 120 min ahead. Some notes on data preprocessing techniques |

2020 | [24] | A comprehensive review of papers from 2008 till 2019 on ML, DL and hybrid models to forecast power production from PV. Interesting concluding remarks. Mainly focused on methods for point forecasting. |

2020 | [25] | A comparison of state-of-the-art models to forecast PV power production focused on a horizon of 36 h in advance. Many models tested from simple linear regression (also Ridge, Lasso and Elastic Net), to the DT and ensemble models, both bagging (RF) and boosting (eXtreme Gradient Boosting). Robust 10-Fold Cross-Validation procedure to test each model’s performance and grid search to find each model’s optimal hyperparameters. All models were tested on a single dataset (plant located in Asia). Weather forecast and observations were used as model input. XGBoosting performed best. |

2020 | [26] | A review focused only on three DL methods; LSTM, RNN, Gated Recurrent Unit (GRU) and a hybrid Convolutional Neural Network + LSTM (CNN+LSTM) to forecast solar irradiance and PV power production. Generally, LSTM performs overall the best but if enough data is available CNN+LSTM is the preferred model to choose. This paper highlights the use of RMSE as the most useful metric, allowing easy comparison of results. |

2020 | [27] | A review of various reinforcement learning methods, both classical (multi-agent RL, etc.) and deep (Deep Q-network, etc.) in sustainable energy and electric systems. It is a more generic review not focused on PV but with a paragraph on MPPT worth reading to a general overview of RL. |

**Table 4.**Review papers for fault/anomaly detection and diagnosis in PV. Publication year considered: 2018–2021.

Year | Reference | Notes |
---|---|---|

2021 | [63] | A review of AI-based methods for remote sensing and fault detection and diagnosis (FDD) in PV emphasizing the applicability of models and the use of IoT technologies for remote monitoring and diagnosis. |

2020 | [64] | A very comprehensive review on fault detection in PV using both SNNs and DL. Analysis related to the years 2009–2020. MLP and CNNs result as the more diffused methods employed in this field. Some public datasets (cell images) were reported. Proposes the build of a large open database of healthy and faults modules/plants (1D and 2D images) |

2019 | [65] | Four major faults are analyzed: ground, line-line, arc and hot-spot. For each fault are proposed both conventional and advanced methods to deal with them: ML-based (MLBTs), reflectometry-based, statistical and signal based and comparison based. Proposes a scoring system to ranks methods. |

2018 | [66] | A review of applicable methods, ML-based but also statistical-based, to FDD in PV. Highlights that most methods employ I-V curve data but also irradiance and module temperature. |

2018 | [62] | An in-depth analysis of all major faults that can affect PV systems is accompanied by a complete list of methodologies that can be employed to detect and diagnose faults. Only a small section is devoted to ML-based methods. |

2018 | [61] | After describing all major faults that can occur in PV, it focuses on FDD methods especially suited for faults occurring in a PV array: statistical, I–V analysis, power loss analysis, voltage and current measurement and AI-based. This paper concludes by highlighting the pro and cons of each method with some recommendations and insight into possible future trends. |

2018 | [67] | Analyzes all major faults that can affect PV with a review of methods in the literature for PV fault monitoring and detection. Emphasizes how statistical methods do not require previous data but cannot identify failure types. On the other hand, numerical methods can detect failure types, but require knowledge of previous data. Knowledge model-based methods using residual current voltage or power can provide fault detection and identification but require historical data and also meteorological ones. |

**Table 5.**Papers for fault/anomaly detection and diagnosis in PV. Publication year considered: 2018–2021.

Year | Reference | Metrics | Applied to | Faults Detectable | Methods & Notes |
---|---|---|---|---|---|

2021 | [69] | Kappa Statistic, Precision, Recall, CM, F-measure | Software simulation | HIF, Line to Ground fault (LG), LL, Double Line to Ground fault (LLG), Three-phase fault (LLLG) | LSTM+DWT |

2020 | [68] | TPR, FNR, PPV, FDR, ROC, F-measures | PV panels of a 22 modules plant | Hot-spot | Hybrid SVM using IRT images and custom feature extraction methodology (41 total features) |

2020 | [70] | Accuracy | Software simulation | LL | SVM+ GA for optimal model hyper-parameter selection (Gaussian kernel) and feature selection (three or two from a set of ten) |

2020 | [71] | Accuracy, F1 | Hardware simulation | Five total faults AC or DC. | RK-RF_{Kmeans} and RK-RF_{ED} |

2020 | [72] | Software simulation | LL, ARC, PS, OC, No-Fault, faults in PS | Pre-trained AlexNet with last three layers fine-tuned with 2-D scalogram from PV data | |

2019 | [73] | Precision, Recall, F1, Detection Accuracy | Two large solar farms | Five types of common anomalies (ageing, building shading, hot spot, grass shading and surface soiling) | Hierarchical context-aware anomaly detection (Auto-GMM+ auto thresholding, Multimodal feature extraction+XGboost) |

2019 | [74] | ||||

2018 | [75] | Accuracy (10-fold CV) | Software simulation + laboratory PV system | RF using only voltage and string currents from PV array optimized with grid search (out-of-bag accuracy) |

Year | Reference | Notes |
---|---|---|

2021 | [79] | The paper provides a comparative and comprehensive review of some relevant PSO-based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading and efficiency. |

2021 | [80] | The paper intends to review the previous articles and provide a proper division, performance method. This explains the performance, application, advantages and disadvantages of algorithms to be a good reference for selecting the appropriate algorithm. Algorithms in the presented paper are divided into four categories methods based on measurement, calculation, intelligent schemes and hybrid schemes. |

2021 | [81] | The paper represents a review of two modern techniques used in solar photovoltaic systems which enhance the extraction of maximum output power in an efficient manner. The Artificial Intelligence-Based MPPT Techniques for PV Applications and a Forecasting System of Solar PV Power Generation using Wavelet Decomposition and Bias- compensated RF are reviewed and compared in the paper. |

2021 | [82] | The paper presents an organized and concise review of MPPT techniques implemented for the PV systems in literature along with recent publications on various hardware design methodologies. Their classification is done into four categories, i.e., classical, intelligent, optimal and hybrid depending on the tracking algorithm utilized to track MPP under PSCs. |

2021 | [83] | The review of MPPT techniques proposed in the paper has been grouped into two groups. The first group includes all the benchmark facilities. The second group includes the intelligent techniques that explain the fuzzy-based MPPT, ANN-based MPPT evolutionary techniques, hybrid methods and MPPT techniques used in energy harvesting. |

2020 | [84] | In the presented paper, a compendious study of different Swarm Intelligence (SI)-based MPPT algorithms for PV systems feasible under partially shaded conditions are presented. The methods are compared in terms of their swarm intelligence and advantages. |

2020 | [85] | A detailed comparison of classification and performance between six major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. Each technique is compared in terms of algorithm structure, cost, complexity, platform, input parameters, tracking speed, oscillation accuracy, efficiency and their applications. The AI-based MPPT techniques are generally classified into fuzzy logic control (FLC), ANN, GA, swarm intelligence (SI), ML and other emerging techniques. |

2020 | [86] | The presented study gives an extensive review of 23 MPPT techniques present in literature along with recent publications on various hardware design methodologies. MPPT classification is done into three categories, i.e., Classical, Intelligent and Optimisation depending on the tracking algorithm utilised. During uniform insolation, classical methods are highly preferred as there is only one peak in the P-V curve. The paper furnishes the hardware information of the particular technique by different authors performed in various platforms with their tracking speeds and efficiencies. In addition, the parameters of these techniques, their flowcharts and a clear explanation of MPPT algorithm implementation are explained in brief. The fundamental objective is to give ongoing innovation advancements in MPPT techniques. |

2020 | [87] | The main MPPT techniques for PV systems are reviewed and summarized and divided into three groups according to their control theoretic and optimization principles: Traditional MPPT methods, MPPT methods based on intelligent control and MPPT methods under PSCs. In particular, the advantages and disadvantages of the MPPT techniques for PV systems under PSCs are compared and analyzed. |

2020 | [88] | This paper reviews (extensively) the most used MPPT algorithms. They are classified into three groups: (1) direct, such as hill climbing, Perturb and Observe (P&O) and incremental conductance (INC); (2) indirect, namely fractional short-circuit current, Fractional Open-Circuit Voltage and pilot cell and (3); soft computing methods such as a Kalman filter, FLC, ANN, PSO, ant colony optimization (ACO), artificial bee colony (ABC), bat algorithm and hybrid PSO-FLC. The purpose of the presented review is to provide a general insight into various MPPT methods describing their principles of operations and highlighting their advantages and limitations. In addition, the suitable embedded board for the hardware implementation of each method is outlined; low-cost only embedded boards have been studied. |

2019 | [89] | This study provides an extensive review of the current status of MPPT methods for PV systems which are classified into eight categories (methods based on mathematical calculations, constant parameters-based methods, measurement and comparison-based methods, trial and error based methods, numerical methods, intelligent prediction based methods and methods based on iterative in nature). The categorization is based on the tracking characteristics of the discussed methods. The novelty of this study is that it focuses on the key characteristics and 11 selection parameters of the methods to make a comprehensive analysis, which is not considered together in any review works so far. Again, the pros and cons, classification and immense comparison among them described in this study can be used as a reference to address the gaps for further research in this field. A comparative review in tabular form is also presented at the end of the discussion of each category to evaluate the performance of these methods, which will help in selecting the appropriate technique for any specific application. |

2018 | [90] | The paper focuses mainly on a review of advancements of MPPT techniques of PV systems subjected to partial shading conditions (PSC) to help the users to make the right choice when designing their system. The choice of MPPT depends on several parameters such as the application, hardware availability, cost, convergence speed, precision, and system reliability. |

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**MDPI and ACS Style**

Tina, G.M.; Ventura, C.; Ferlito, S.; De Vito, S. A State-of-Art-Review on Machine-Learning Based Methods for PV. *Appl. Sci.* **2021**, *11*, 7550.
https://doi.org/10.3390/app11167550

**AMA Style**

Tina GM, Ventura C, Ferlito S, De Vito S. A State-of-Art-Review on Machine-Learning Based Methods for PV. *Applied Sciences*. 2021; 11(16):7550.
https://doi.org/10.3390/app11167550

**Chicago/Turabian Style**

Tina, Giuseppe Marco, Cristina Ventura, Sergio Ferlito, and Saverio De Vito. 2021. "A State-of-Art-Review on Machine-Learning Based Methods for PV" *Applied Sciences* 11, no. 16: 7550.
https://doi.org/10.3390/app11167550