# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 2 Machine Learning, Deep Learning and Related Methods
Authors claim very accurate results in the range of 4-8 steps ahead (1-2 h) but also state that a CNN+LSTM model can obtain results even better if enough data is provided, especially for longer forecasting horizons. In the last several years, many different ML frameworks have been developed; this gives the opportunity to easily develop ML models and eventually deploy them in production an effective solution with ease. Some of these solutions provide what is known as Auto-ML (AML), i.e., an approach that can automatically select, train and optimize an ML model or eventually an ensemble of ML models. This is what is proposed in [32], where an AML is employed to derive an ensemble where the features used by each building model are derived using an improved GA optimization method capable of selecting optimal features for each region. In this work, historical data coming from PV plant data (panel temperature and power generation) and weather data (temperature, irradiance, cloud cover, precipitation and humidity) are used in conjunction with the results of a physical model that provide power production as a function of the tilted solar irradiance perpendicular to the solar PV panel, the temperature of the solar PV panel and the ambient temperature. The dataset used spans over 2 years, 2016-2017, with data sampled every 30 min, and is used for a multi-regional model, i.e., applied to data from plants at different regions. The ensemble selected by AML is made up of Elastic Net CV regression, Gradient Boosting Regression and RF Regression. Historical data of PV power plants located in Hokkaido (Northern Japan) from 1 January 2016 to 31 December 2017, is used for training while only one month is used for testing (December 2017). This is one of the very few papers assessing the viability of AML in forecasting PV production. Interestingly enough, the models selected to build up the ensemble were previously rarely used in this field. While most of the research is focused on the very short- to short-term forecasting horizon, long-term PV production forecasting is investigated in [33] using a grey box prediction model. In detail, an adaptive discrete grey model with a time-varying parameter denoted as ATDGM(1,1) with a single variable and one order is used. This type of model does not require exogenous variables and belongs to the group of the model generally applicable to time series prediction problems. More on grey methods can be found in [34]. For the first time, to the best of our knowledge, the concept drift issue is discussed in the field of energy forecasting in [35]. This work is about solar and wind energy forecasting and not PV power production, but it has been included in this paper as it employs some techniques that can be easily adopted in the field of interest and because it takes into consideration a public dataset. An evolving Multivariate Fuzzy Time Series (e-MVFTS) is here adopted to forecast a time series and its potential has been evaluated in solar and wind energy using a public dataset made available by United States National Renewable Energy Laboratory (NREL) for solar energy data, and extracted from Global Energy Forecasting Competition 2012 (GEFCom2012). The wind energy dataset has been published on the Kaggle platform repository. To allow for the complete reproducibility of the results, all code and data were made publicly available. The proposed method, combining a forecasting model based on Fuzzy Time Series with an evolving clustering method based on Typicality and Eccentricity Data Analytics (TEDA), can adapt to the concept drift that occurs in the time series, i.e., can automatically deal with changes in the data distribution. In [36] a hybrid model based on wavelet packet decomposition (WPD) and long short-term memory (LSTM) networks is proposed which employs historical power and historical meteorological data as input variables, including global horizontal irradiance, diffuse horizontal irradiance, ambient temperature, wind speed and humidity. No forecasted irradiance is used in the model.