## 4 Discussion of Results Regarding ANN Prediction Models  
The historical tendency of the application of ANN models in energy generation prediction grows according to the promotion of the different renewable energy sources. Table 7 summarizes the tendency and evolution of the application of ANN in prediction problems in Photovoltaic, Wind and Hydraulic energies. These contributions are presented according to:  
* The source of energy: Photovoltaic, Wind, Hydraulic, other sources and new related research. * The type of model: IMEP; MFM and ARAM. In Table 7, the reader can also find the number of contributions, per source of energy and model type, per year. It can be appreciated how for each type of energy source, ANN model applications show a similar pattern, consisting of an initial sort of induction phase, when the models are introduced, developed and deployed, and after that, more pressure is placed on the efficiency of the models in what we could call an efficiency improvement phase. Table 6 shows that references about hydraulic energy applications are the first to appear, in line with the maturity of the technology, while there have not been more publications recently. For this source of energy, all existing ANN publications have focused on energy prediction modelling. In the other two sources of energy, we can find more recent ANN model applications, initially solely energy prediction models, but lately a few of them oriented to specific failure mode detection. In Table 6, this tendency is distinguished through time intervals; in the top of the reference list, a specific orientation to failure detection can be acknowledged. Conventional (time series & regressions) prediction techniques were common in many initial contributions, while in the last publications, ANNs become a more popular tool as long as the scope of the studies also changes to improving efficiency in energy production by anticipating failures, avoiding economic losses as a consequence of low reliability. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Year** & **Case Study** & **Obj.** & **Met.** & **Parameters** & **Res.** \\ \hline
[98] & 1991 & Seattle/Tacoma & B & D &
\begin{tabular}{c} Hourly temperature, expected future \\ temperature and load data \\ \end{tabular} & 1,2 \\ \hline
[99] & 2016 & Germany & B & DF &
\begin{tabular}{c} Historical NWP (Numerical Weather \\ Prediction) data and the produced \\ power in a three-hour resolution \\ available for 990 days. \\ \end{tabular} & 1,2,4 \\ \hline
[100] & 2016 &
\begin{tabular}{c} Ningxia, Jilin, Inner \\ Mongolia and Gansu, \\ China \\ \end{tabular} & B & D & Historical records of wind speeds. & 1 \\ \hline
[101] & 2018 & China & B & E &
\begin{tabular}{c} Electrical Load, Temperature, Humidity, \\ Rainfall, Voltage, Charging Rate, State of \\ Charge, Power Quality. \\ \end{tabular} & 1,2 \\ \hline
[102] & 2018 & Washington (EEUU) & A & D &
\begin{tabular}{c} Wind and solar times-series data from \\ NREL ([[https://www.nrel.gov/grid/wind-](https://www.nrel.gov/grid/wind-)]([https://www.nrel.gov/grid/wind-](https://www.nrel.gov/grid/wind-)) \\ integration-data.html; [[https://www.nrel.gov/grid/sind-toolkit.html](https://www.nrel.gov/grid/sind-toolkit.html)]([https://www.nrel.gov/grid/sind-toolkit.html](https://www.nrel.gov/grid/sind-toolkit.html))) \\ \end{tabular} & 1,2,4 \\ \hline \hline Obj. = Objective (A = IMEP; B = MFM; C = ARAM) Met= Methodology (D = Neural Network; E = Comparisons \\ Models; F = Others) T = Temperature; D = Date; H = Humidity; WH = Work Hour; SR = Solar Radiation; O = Others. 1. Validate the use ANN 2. The improvement of ANN vs. classic models is included. 3. ANN used a complement for a higher level reliability analysis framework. 4. Authors recommend complementing the analysis with other techniques. \\ \end{tabular}
\end{table} 
Table 6: References about recent research tendencies. Concerning model categories, many early works included meteorological forecasting models, along with the physical models as input.