## 4 Discussion of Results Regarding ANN Prediction Models
Subsequent models included meteorological variables besides physical variables in order to produce an ideal model of energy production, as close as possible to reality. Later publications are very much related to energy production efficiency, but also deal with a specific single type of failure mode of the asset. This tendency towards energy efficiency modelling is in line with the price reduction of sensors and increase in processing capacity of SCADA systems, located in plants and/or embedded in particular assets, to collect information from asset degradation, environmental and operating conditions. We have found that 86% of the contributions use ANN models for prediction. It is mentioned in the literature [88] how more than 40% of prediction studies are related to ANN models. In addition, the most used ANN model is the ANN Backpropagation model (81%), mainly with the Levenberg-Marquardt algorithm (which requires high computation capacity) for minimizing MSE. Another less used algorithm in ANN Backpropagation is the Quasi-Newton algorithm. In all the studies the correlation coefficient is higher than 80%, and results are improving in recent references with convolutional and recurrent ANN models. The most employed parameters for prediction and energy source are the following (see Figure 2):  
* Photovoltaic Energy: Temperature and Solar radiation. * Wind Energy: Wind Speed. * Hydraulic Energy: Hydrometric data, temperature, rainfall and water pressure. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Publication**} & \multicolumn{3}{c}{**Photovoltaic**} & \multicolumn{3}{c}{**Wind**} & \multicolumn{3}{c}{**Hydraulic**} & \multicolumn{3}{c}{**Other**} \\ \cline{2-11}  & **IMEP** & **MFM** & **ARAM** & **IMEP** & **MFM** & **ARAM** & **IMEP** & **MFM** & **ARAM** & **IMEP** & **MFM** & **ARAM** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 7: ANN tendency by Renewal Energy field. The main advantages of the use of ANN models, compared to the use of more conventional techniques (time series & regressions), are the following:  
* Prediction models with good correlation coefficients. * Quick fitting and flexibility to behavior patterns (pattern-recognition and fault tolerance capability including data absence and noise). * Better adaptation to complex and non-linear problems. * Adjustment to dynamic changes in real time. * Quick processing and easy integration in systems. * Availability increment of energy production due to proactivity in fault prediction. On the other hand, the main drawbacks of ANN models in the renewable energy sector are:
* Lack of asset condition monitoring variables. Many companies focus on operation monitoring variables instead. * Presence of poor-quality data, without discerning the different state conditions and without correlation with preventive and corrective activities and their execution results. * Difficulty to reach a local minimum, and to optimize the model coefficients. Need to select proper biases and initial weights. * Lack of out-of-the-box integration in SCADA systems, due to commercial solutions avoiding integration with external intelligent modules. * Lack of qualified professionals with knowledge in these recent types of energy sources, in condition-based and predictive maintenance, and in parallel with experience in big data and machine-learning techniques. New developments in prediction tools based on AI could be employed or combined with ANN models as the quantity and quality of data variables increase. As an example, the following machine learning techniques are the most recommended to be employed [103; 104; 105; 106; 107; 108; 109]: Bi-directional Long Short-Term Memory (BLSTM), Deep Learning and Neural Network, Extreme Machine Learning, SVM, T-Basts, Random Forest and Boosting.