## 3 Review of ANN Models for Prediction in the Renewal Energy Sector  
### Scope of the Review and Prediction Model Classification according to Model Use  
According to the above-mentioned advantages of using ANNs for prediction, several authors have implemented them in the different types of renewal energy sources. A significant number of contributions have been made for three renewal energy sources: photovoltaic, wind and hydraulic energies, even though there are some references on other sources of energy, in a minor volume [44; 45; 46; 47]. When we explored all these renewable energy prediction models based on ANN, our intention was to focus on those incorporating intelligence to anticipate reliability problems, those adding new capabilities to improve asset maintenance policies. The rationale for this is the importance that maintenance and reliability have in this sector, to increase the efficiency of the energy generation process. Failures are many times hidden and they end up having a high impact on business plans due to derived energy production losses. In this sector the risk of failures could even reach ten times the purchase equipment cost [48]. Difficulties in the detection of failures can be overcome with ANN models, which are more suitable to deal with changing environmental conditions for each specific asset geographical location, by using permanently updated intelligent algorithms [49]. Our idea is to identify how different authors have approached this problem (one layer, multilayer, convolutional, sequential, deep learning, etc.) to serve as an important guide for all companies with renewable energy facilities that want to use these techniques to predict failures and to improve energy supply continuity (implementation techniques, main input variables, indicative results, etc.). In this research we have concentrated our literature review on specific databases (Sciencedirect, Elsevier, Scopus, IEEE Xplore) and several keywords (renewable energy, photovoltaics energy, hydraulic energy, wind energy, wind power, neural network, prediction model, energy forecasting, failure detection, intelligence artificially, machine learning, deep learning), and as a result we have found different utilizations of ANN models for prediction, that we will classify as follows:  
* Ideal models to predict only energy production (named in this paper IMEP); or
* Energy prediction models incorporating meteorological forecasting aspects (named MFM); and
* Asset reliability assessments models (named ARAMs), the ones we are interested in, describing algorithms for early problem detection for assets, algorithms that are part of a defined CBM (Condition Based Maintenance) policy, many times resulting as an output of the RCM (Reliability Centered Maintenance) methodology (RCM is the most employed methodology to design preventive maintenance policies [50] given an asset's operational context [51], including risk quantification [52] of failures under existing operating and environmental conditions [53,54]). Concretely, ARAMs have to follow international standards so any future change, modification, improvement, management of the solution will be very much facilitated and understood. For a wide comprehension of the ARAM, potential is needed to analyze its implementation cases for three components [55]: monitoring, diagnosis and prognosis. [PERSON] et al. [56] describe these components in terms of failure mode control:  
* Detection/monitoring is associated with the system states (for example, the transition from function state to fault state) and, in general, with normal behavior-anomalies distinction (in reference to defined baseline data);
* Diagnosis is associated with the location of the failure mode and its causes;
* Prognosis is associated with the evolution of the failure mode or its future behavior (risk of failure and remaining useful life at the current time).