## 3 Review of ANN Models for Prediction in the Renewal Energy Sector
### ANN in Hydraulic Energy
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Case Study**} & \multirow{2}{*}{**Obj.**} & \multirow{2}{*}{**Met.**} & \multicolumn{6}{c}{**Parameters**} & \multicolumn{3}{c}{**Results**} \\ \cline{5-16}  & & & & & **RF** & **HD** & **T** & **WP** & **O** & **1** & **2** & **3** & **4** \\ \hline
[89] & 1995 &
\begin{tabular}{c} The Leaf river close to Collins, \\ Mississippi \\ \end{tabular} & B & DE & **g** & **g** & **g** & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & \(\vee\) & \\ \hline
[37] & 1998 &
\begin{tabular}{c} The Amber and Mole rivers in the \\ United Kingdom \\ \end{tabular} & B & D & \(\square\) & **g** & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & \(\vee\) & \(\vee\) \\ \hline
[90] & 1999 & River Ontario, Canada & B & D & **g** & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & \(\vee\) & \\ \hline
[91] & 2000 &
\begin{tabular}{c} Chute-du-Diable hydro system in \\ northern Quebec (Canada) \\ \end{tabular} & B & D & **g** & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & & \\ \hline
[92] & 2000 &
\begin{tabular}{c} The River Dove and the River Tent at \\ Colwick United Kingdom \\ \end{tabular} & B & D & \(\square\) & **g** & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & \\ \hline
[93] & 2007 &
\begin{tabular}{c} The River North Platte in (EEUU) \\ \end{tabular} & B & DE & \(\square\) & **g** & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & \(\vee\) \\ \hline
[94] & 2017 & Based on software EPANET & C & DE & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\vee\) & \(\vee\) \\ \hline
[95] & 2017 & Brazil & B & D & \(\square\) & \(\square\) & \(\square\) & \(\blacksquare\) & \(\square\) & \(\vee\) & \(\vee\) \\ \hline
[96] & 2018 &
\begin{tabular}{c} The Drina River, between the cities of \\ Föca and Gora\(\tilde{\text{z}}\)de. Bosnia and \\ Herzogovina \\ \end{tabular} & B & DF & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\square\) & \(\blacksquare\) & \(\vee\) & \(\vee\) \\ \hline
[97] & 2018 & Rassauta, Algerie. & B & DF & \(\square\) & \(\square\) & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\blacksquare\) & \(\vee\) & \\ \hline \hline \multicolumn{2}{c}{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 4: Reviewed ANN studies on Hydraulic Energy. The main advantage of ANN application in the hydraulic field is the development of simulation models and real-time dynamic prediction, predominantly in a short-term due to the normal variation of the climatological conditions. The most used input variable in these predictive models has been hydrometric data. In this field, the main disadvantage of ANN application is that a large amount of historical data are required to obtain good models and results, while in previous energies (photovoltaic and wind) the assets are distributed in farms in a replicated way and so offer enough data that reproduce the same operation context. That is to say, in the two previous energy sources, a wide range of information is available to be processed to obtain a reliable prediction while here it can be scarcer. ARAMs are less implemented than the other types and are centered on the single failure mode water leakage; a clear example is reference [94] for failure detection and localization of it, searching regularities and patterns using not only AI-ML ANN but also Logistic Regression and Support Vector Machine.