## 1 Introduction  
Solar PV, hydraulic and wind energy sources are supporting continuity of energy supply, which is a key strategic issue for many countries to guarantee their industry growth. They contribute to the use of inexhaustible energy sources, to the implementation of energy multi-sourcing strategies, to a more environmental friendly production of energy, and/or to the preservation of power generation, and distribution means integrity, ensuring dependability of the entire system [1; 2; 3]. However, the integration of these renewable energy plants into the conventional electrical grid has many challenges. Some of these challenges are related to reliability of the generation systems being used, but others have to do with the fact that these sources of energy are intermittent in nature, and they depend on the climatic conditions, affecting the stability of the network. Matching the supply and the load becomes troublesome and is a clear disturbance of the network. The stability of the network is based on maintaining grid frequency. A load greater than supply makes the frequency fall and a load lesser than supply makes the frequency increase. In this context, relevant research activities are taking place to develop more accurate models for renewable energy supply prediction [4]. In spite of the existence of well-developed underlying physical models for each component of all kinds of renewal energy generation systems, complexity arising from the combination of these elements makes impractical the direct characterization of the system through closed mathematical expressions, so stochastic models are selected in practice to characterize the behavior of this sort of system. To gain prediction accuracy, intelligence and flexibility need to be incorporated into prediction models, and that is why the application of Artificial Intelligence (AI)--Machine Learning (ML) techniques is increasing in this field. AI-ML techniques consist of fitting the parameters of a model from observed data (experience) and are best suitable to discover behavioral patterns from data series in the presence of randomness. This property of machine learning algorithms is invaluable in anomaly detection problems [5]. Within AI-ML techniques, Artificial Neural Network (ANN) models have delivered good results for real-time estimations, and especially when learning from dynamic changes in environmental conditions becomes a key factor to improve prediction accuracy [6]. This paper reviews the different uses of ANN models for better renewable energy prediction. The idea is, at the same time, to identify those contributions with special emphasis on understanding assets' reliability issues. The rationale for this is that ANN tools may also become an excellent tool for asset performance monitoring, also a complex problem in these environments where:  
* The assets can perform in very diverse operating conditions (due to diverse environmental conditions);
* Asset conditions are many times not feasible to be monitored, or simply doing it becomes a complex technical problem with a very troublesome and economically non-viable solution (difficulty is many times related to specific functional locations);
* Altogether, this could result in a serious lack of asset performance control and subsequent loss of expected performance efficiency. Therefore, the paper explores the efforts made for ANN models to become a practical asset performance monitoring tool, for any potential asset location, environment (the reader may also notice that this review also remarks on contributions incorporating meteorological forecasting) and operating conditions, offering the possibility to control asset performance and reliability, ensuring life cycle expectations according to existing business plans. In the review accomplished in this paper, it has also been recorded those occasions in which research was conducted for technique comparison purposes (considering other prediction techniques) or with the intention to identify possibilities of different prediction techniques complementarity.