# A State-of-Art-Review on Machine-Learning Based Methods for PV
###### Abstract  
In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. In this scenario, machine learning (ML), a subset of AI techniques, provides machines with the ability to programmatically learn from data to model a system while adapting to new situations as they learn more by data they are ingesting (on-line training). During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art ML models applied in solar energy's forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters' optimization and efficiency improvement, anomaly detection and energy management of PV's storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. The paper gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems. machine learning; solar energy; forecast; diagnostic; electricity markets +
Footnote †: journal: Medical  
the risk factors of certain diseases and prevent epidemics [1,2]. ML and associated technologies are developing rapidly, and we are just starting to discover their capabilities [3,4]. AI technologies have now also arrived in the field of renewable energy; from those, such as Google, who use them in wind farms to improve forecast data [5,6], to those who use them to increase the efficiency of solar panels [7]. Several AI and ML solutions are already available to predict wind and PV energy production, for predictive maintenance systems for wind turbines or to search for new materials for solar panels [5]. The perspective of ML applications for the development of renewable energy is almost unlimited. Many players in this market are testing innovative solutions to improve the performance of their systems. ML applications can make it possible to exploit in the best way the operation of plants, forecasting weather conditions, such as the exposure to the sun of the PV surfaces, the direction and strength of the wind in the case of wind power or rainfall for hydroelectric generators [8,9]. ML and predictive models can also help in the management of energy supply for households in cities, optimizing their distribution network [10,11,12]. According to the International Energy Agency (IEA), in the coming years, in the energy field AI will be decisive and will radically transform global energy systems, making them more interconnected, reliable and sustainable [13]. During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art of recent advances in ML for photovoltaic and solar applications, which provides a broad overview of current advanced techniques to academics and practitioners. In particular, papers published in international journals from 2018 to 2021 have been taken under consideration. For the literature review step, the following search engines for research articles (journals and book chapters) have been extensively employed: Microsoft Academic, Scopus/ScienceDirect, ResearchGate and GoogleScholar.