# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 2 Relevant PV Problems
### Forecasting Problems  
There are several variants of the forecasting problem which arise in PV: weather forecasting, solar irradiance forecasting and energy production forecasting, which is to estimate the energy production of the system. This is really important to optimize the real-time management of systems that use this kind of energy (smart cities, villages, etc.). This problem has high priority for electric companies because they want a more robust and reliable system to predict the changes in energy loads and demands. Another important aspect is the amount of time that has to be predicted.  
* Short-term forecasting is usually from 1 hour to a week ahead and is used for scheduling energy transfer, economic load dispatch and demand response.
* Mid-term forecasting is usually considered between 1 month and 1 year ahead, usually for planning the near-forthcoming power plans and to show the dynamics of the system in that interval.
* Long-term forecasting is considered between 1 year and 10 years. Its function is to plan the generation power plant so as to satisfy future requirements and cost efficiency.  
Another important factor for forecasting is the number of parameters, the amount of information and data is key when it comes to obtaining a precise forecasting model, but it  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Methods** & **Features** \\ \hline Perturbation and Observation [15; 16] & This algorithm does not depend on previous knowledge, it is the simplest and is widely used due to its balance between efficiency and simplicity. It disturbs the operating point of the system, causing the PV voltage to fluctuate near the MPP voltage. \\ \hline Incremental Conductance [15; 16] & This method is based on the basis that the slope of a PV curve is zero at MPP. The algorithm tracks the MPP by searching for the peak of the PV curves. This algorithm uses the instantaneous conductance \(I/V\) and the incremental conductance \(dI/dV\). \\ \hline Curve Fitting [15; 16] & This method implements a mathematical function to describe the output of the generator. The disadvantage of this method is that it requires a high memory capacity and is not optimum for high-speed changes in the irradiance. \\ \hline Open Circuit [15; 16] & This method implements a mathematical function to describe the output of the generator. The disadvantage of this method is that it requires a high memory capacity and is not optimum for high-speed changes in the irradiance. This method is simple, and it uses a single control loop. \\ \hline Feedback Tension [15; 16] & This method can be used with the feedback of the voltage of the panels, which is compared with the tension of constant reference to adjust the word cycle. This system is unable to adapt to changes in irradiance or climate. \\ \hline Measurement of the Current of the PV Generator [15; 16] & This method is based on one variable, the output current of the PV Generator, which is the input current of the generator. The control of the output optimizes the maximum output current. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Traditional Methods for MPPT.  
is also true that sometimes too much data can provide noise or misleading information that can injure performance.  
Each kind of forecasting is usually tacked as a different problem, since the amount of data and precision required are highly different. More information about forecasting can be found in [17].