# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 3 AI-Based Techniques Used for PV Output Power Forecasting
### Application of Machine Learning in PV Power Forecasting
The well-known MLP-ANN was used to estimate one day-ahead the power profile of a small-scale grid-connected 20-kWp PV plant installed at Tamil Nadu, India [53]. The MAE was in the range [1.92%-11.28%]. In this case, a number of learning algorithms has been investigated, while the Levenberg-Marquardt and Delta-Bar-Delta have shown the best performance. In [54] the authors used a SVR-based method applied to a small-scale PV plant located in Melaka, Malaysia. The use of different input has been investigated including the tilted and horizontal global irradiance, and the module temperature. The results showed that the model performs well in the tropical climate with a RMSE that was in the range [4.29%-6.85%]. A large dataset both from NPW and measurements from the field has been used to train different multi-model ensembles (MME) including SVM, ANN, and statistic models [55]. The investigated PV plant is located in Bolzano (Italy), and its capacity is 662 kWp. This work showed that the same algorithms differ in performance when using as input NWP data with comparable accuracy. A hierarchical-based approach with different time horizons (15 min, 1 h, and 24 h) was used in [56]. In this case, many different parameters have been used as an input including the plant output power, a number of environmental variables coming from NWP, and the geometry of the system. The conclusion was that this method performs better than others based on ANNs and SVR. In [57] the author developed an advanced fuzzy-logic method for forecasting the output power of two PV plants installed in Milano and Catania, Italy. The model was used to forecast the output power with a time horizon in the range [1 h-72 h]. The MAE was 0.56 kW for the PV plant installed in Catania and 0.64 kW for the one in Milano. This work showed that all investigated models including generalized adaptive, physical inspired, semi-statistical methods perform better in summer than in winter, while have similar performance in summer and autumn. The T-S fuzzy-based approach proposed in [58] uses as an input a number of meteorological parameters. The model was compared with other methods such as SVM, MLP-ANN, RNN, and other empirical models. The results showed that the proposed model outperforms all others with a quite low MAE = 9.77% in summer, but a high MAE = 30% in spring. In [59] the authors designed a SVM-based model for a one day-ahead forecasting. The model accepts as an input the historical powers and some meteorological data. The method was tested to forecast the PV power generation for different conditions corresponding to clear-sky, cloudy and rainy days, and for three locations in Malaysia. The results in terms of MAE = 34.57% has shown the good forecast ability of the proposed model. In [60] a MLP-based forecaster was trained using weather forecasts and historical data. The model performed better during sunny than partially cloudy days. The normalized MAE was lower than 15% for all the investigated cases. A new upscaling method was developed for estimating the power produced by a PV plant installed in Italy [61]. The method uses data from satellite and NWP to estimate the solar generation on a regional scale. The method was applied to the power generation of 1985 small-scale PV plants installed in the South Tyrol Region, Italy (the total covered area was 800 km\({}^{2}\)). The RMSE was in the range [5%-7%] for a time horizon of 4 h, and in the range [7%-7.5%] for the 1-day estimation. SVM and a MLP have been used to for the ultra-short-term forecasting of a small-scale PV plant installed in Beijing, China [62]. According to the authors, the designed model is particularly efficient and especially designed for particular environmental conditions with fog and haze. The input of these forecasters comprises the on-site measurements of air temperature, relative humidity, and aerosol indexes. In [63] an ELM algorithm has been developed in order to train a MLP network that forecasts 24-h ahead the power produced by a PV plant installed at Amman, Jordan.