## 1 Introduction  
Worldwide growth in the installation of renewable energy systems has been remarkable during the last decade. The most popular and widespread renewable energy technologies in Greece are wind, photovoltaic (PV), biomass/biogas and hydropower. According to the Renewable Energy Sources Operator andProvider of Guarantees of Origin of Greece, the installed power is 4426.23 MWp for wind, 4198.59 MWp for photovoltaics, 112.31 MWp for biomass/biogas and 257.92 MWp for hydropower [1]. The design, operation and maintenance of renewable energy systems is a challenging task that requires measurement accuracy and scientific soundness in the processing of monitoring data. Monitoring data from these energy systems can give important information for a system's design and performance evaluation. Furthermore, data can be used in energy management either for fault detection or for power forecasting. The following are considered as the most critical problems related to energy optimization and maintenance in photovoltaic systems: Max Power Point Tracking, Output Power Forecasting, Parameter Estimation, Defect Detection [2]. A fault detection method for a PV energy system can provide an accurate estimation of production under normal operating conditions as well as hints for the detection of the system's faults [3]. Faulty operating conditions result in a reduction in energy production and compromise the reliability of the system, while creating financial losses for investors [4]. Many researchers have studied the fault detection of photovoltaic systems by combining measurements, experiment and computational methods. Every approach is characterized by the specific type of fault that detects the methodology addressing this task, and the specific application category. Faults concern both the AC side and DC side, as commonly observed in stand-alone and grid-connected PV systems [5]. [PERSON] et al. categorized faults by: (i) short-circuit faults, that can affect cells, bypass diodes or modules, and the aging of PV modules; (ii) open-circuit faults that involve a break of wiring between PV modules or solar cells, and (iii) partial shading faults, which affect shaded modules, while the other part is normally exposed to solar irradiance, the passage of clouds, dirt on PV modules, snow, other light barriers and obstacles [6]. Other faults related to PV panels or panel covers involve: the fracture of the glass protective surface; bubbles and/or tears to the polymer cover of the backsheet; the corrosion of metallic frames; damage to the panel's insulation; failed soldering joints of the PV cells; hot spots; and the PID effect [7]. A large variety of techniques are applied to monitoring and measurement data management and processing. The multiple forms of machine learning (ML) are prominent in these techniques. ML may be broadly defined as the practice of using algorithms to parse data, learn from them, and then make a determination or prediction thereof [8]. Neural networks (NN) and statistical methods are the most frequent tool categories, applied in fault detection in the area of photovoltaic energy systems. [PERSON] et al. proposed an algorithm procedure based on the comparison of simulated and measured yields. The main type of faults examined and traced are inverter disconnection, partial shadowing and the disconnection of a string. The method was applied in a 9 kWp PV test installation in Algeria [9]. [PERSON] et al. proposed a procedure for automatic fault detection and diagnosis based on the statistical comparison of the means of measured and theoretical power using the \(t\)-test. The application and validation of this method was conducted in a 1.98 kWp system in United Kingdom [10]. [PERSON] et al. propose a method for fault detection comparing measured values with simulated values by use of a one-diode model.