# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Forecasting
Combining different NNs in an ensemble has been found to be a strategy to reduce these problems. The work of [80] tested different combinations using temperature and solar irradiance as inputs. Every combination was found to be better than using only a single NN. The data were composed of 7300 data from 365 different days. The findings were that the best architecture for forecasting is the one which uses an iterative methodology to find the outputs, forecasting one at a time with a Mean Absolute Error (MAE) of 51.48% and Mean Relative Error (MRE) of 17.24%. The work in [81] used a fixed methodology, changing activation functions, learning rules and architecture in order to find the best neural network for their dataset. The data were acquired along a period of 70 days, obtaining 11,200 examples. The best network had 1 hidden layer with a Linear Sigmoid Activation Function. The learning rule as Conjugate Gradient [82], which uses second derivatives to determinate the weight update, inputs temperature and photovoltaic power and outputs next-day forecasting of PV power output. The validation study indicates that this network is simple and versatile and can precisely forecast with a minimum MAPE of 0.8655. The experiments were implemented using the NeuroSolutions [83]. Another problem of NN is that training can be slow since back-propagation is highly demanding. For solving this problem, the work in [84] used the extreme learning machine (ELM) technique to train the network. ELM [85] has a faster learning speed while obtaining better generalization performance, and it also optimizes the number of hidden neurons. The system is composed of three networks, one for each kind of weather. The network is trained with the PV output history and the weather history data. Based on the weather report of the next day, the model is chosen to forecast the day-ahead PV. The results show that ELM networks outperformed BP networks with a MAPE of 2.78% in the best case. The experiments were implemented using MATLAB. Another improvement can be found in [86]; the neural network is aided by a technique known as Wavelet Transform (WT) [87]. This algorithm is specialized in isolating the spikes produced by continuous fluctuations of the PV data. It has two stages: decomposition of the input signal, which is performed before the neural networks, and reconstruction, which is performed with the output of the NN. The model used is a Radial Basic Neural Networks (RBNN) [88], which needs less computation time and is more effective than Back-propagation Neural Networks and takes as input the PV, solar irradiance and temperature of the current hour, twelve hours before and twenty hours before in order to predict the one-hour-ahead power output. The results show that the proposed model outperformed RBNN without WT for hourly PV for the horizon of 12 hours with a MAPE of 2.38% in the best case. Figure 5: Taxonomy of most used IA methods for forecasting. WT is used along other architectures as in [89]. RNNs are probed to be useful in order to predict from time series and WT deals with the fluctuations on the data provided by the meteorological time series obtained from sampling at intervals of 10 min and stored as time series. This combination proved to be able to forecast 2 days ahead more accurately than other Neural Networks. A recent use of WT is found in [90]. This work presents a hybrid algorithm composed of WT, PSO and RBFNN used to forecast from 1 to 6 hours ahead. The inputs that are used in the model are set as Actual PV, irradiance and temperature. The WT is used to perform an data filtering on the past 15 days before the forecast day. The RBFNN is optimized by the PSO algorithm. The network performed better than the compared methods, with an MAE of 4.22% on average for a 1-hour-ahead forecast, 7.04% for a 3-hour-ahead one and 9.13% for 6-hour- ahead one. Recurrent Neural Networks are also used in [91]. Deep Recurrent Neural Networks (DRNN), RNNs with many hidden layers, are used to forecast.