# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Parameter Estimation  
Finding the parameters of the PV models is vital to simulate their behavior and to optimize their production. This problem is simplified by finding the unknown parameters in order to optimize the output power. Different techniques, most of them metaheuristics, have been used to solve this problem, as can be seen in Figure 6.  
Metaheuristics are the most used techniques to estimate PV parameters. Different kinds of algorithms have been evaluated in recent years. The work in [112] compares different evolutionary algorithms, comparing Genetic Algorithms [48], Particle Swarm Optimization [52] and differential evolution [113]. DE is an evolutionary algorithm similar to Genetic Algorithms but which uses real numbers to codify the problem, this solves the problem of GA when it comes to converging speed. The fitness function was computed as the sum of the absolute errors in current and voltage. The findings showed that the best results were given by DE and the worst ones by GA. The authors also implemented different hybrids: Tabu Search [114] assisted differential evolution to avoid falling in local minimums, PSO assisted DE in which PSO is  
Figure 6: Taxonomy of most used metaheuristics for parameter estimation. \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Type** & **Features** \\ \hline Convolutional Neural Networks & Two inputs. Parameters are selected by testing different combinations. [107] \\ \hline Dendritic Neural Networks & Aided by WT. Provides better convergence speed and better fitting ability. [109] \\ \hline Radial Basis Network & Two inputs, aided by Wavelet Transform to preprocess the input data. [86] \\  & High-resolution time series as input. Aided by Wavelet Transform to preprocess input data and PSO to optimize the neural network. [90] \\ \hline  & Aided by Wavelet Transform to deal with fluctuation in time series input data. [89] \\  & Preprocessed and normalized high-resolution time series as input. Two hidden layers of 35 neurons. [91] \\ Recurrent Neural Network & Tested Different RRN architectures. LSTM, which uses previous time steps, found the best one. [93] \\  & Uses Echo State Networks aided by Restricted Boltzmann Machine, Principal Component Analysis and DFP Quasi-Newton Algorithm to optimize the network. [94] \\ \hline  & Two inputs. A parameter to tune the number of SVM during training. [75] \\ Support Vector Machines & SMV compared with KNN. SMV was found to be better. [111] \\  & Multi-input SV. Different combinations of inputs were tested. Three inputs was the best one found. [76] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._activated after 5 generations of DE and DE assisted by Tabu Search where DE is used to search for the optimal solution in a subset of the whole search space, while TS is used to move the local search within the global space. These hybrids performed better than the originals and provided more stability. DE assisted TS and provided the best results, and it was the fastest. In [18], an ABC-based approach is proposed. This method combines Extreme Optimization (EO) with 2 different versions of ABC. EO provides new insights into the optimization of metaheuristics due to the fact that only the worst variables in the suboptimal solutions are selected to be mutated, instead of favoring the good ones; this is provided by the strong local-search capability of EO. The introduction of EO to ABC is applied when the global optimum is not becoming better for several iterations. The results show that the addition of EO to ABC outperformed other metaheuristics such as PSO on the single-diode model (mean RSME of \(1.1678\times 10^{-3}\)) and on the double-diode model (mean RSME of \(1.1479\times 10^{-3}\)). The major drawback is that EO has a higher computation cost than other methods. The experiments were run in MATLAB. The authors of [115] presented a variant of the Covariance Matrix Adaptation Evolution Strategy. CMA-ES is an efficient derivative-free optimization algorithm.