The integration of rooftop solar photovoltaic systems into the electricity grid may be crucial in the current energy scenario. At present, this type of electricity generation is cost-competitive in many countries due to its modularity, the availability of the solar resource and the cost of the components, without the need for subsidies. Rooftop Solar Photovoltaic systems have the potential to cover 20-30% of electricity demand in Spain. In order to assess the potential of this technology and to facilitate the deployment of this type of systems, it is very important to provide a proper performance analysis of PV Rooftops systems from monitored data. In this way, self-consumption and self-sufficiency indices are commonly used, however they may not provide a complete assessment. Hence, indices such as the self-sufficiency index for sunshine hours, self-production index and grid-liability rate are also analyzed. These indices estimate the performance of rooftop solar PV systems and provide maximum and minimum values when estimated as a function of array peak power. Moreover, new indices such as the self-production index and the grid-liability rate for sunshine duration have been developed to estimate the system's performance during sunshine hours. These indices can complement the commonly used metrics and improve the performance analysis from monitored data. Moreover, they may also help determine the proper size of the array power of these systems in the industrial sector. The metrics are evaluated using data from four canning industries equipped with rooftop solar photovoltaic systems that have been monitored for a year.

Standard monitoring for photovoltaic (PV) systems, often based on IEC 61724-1, the standard published by the International Electrotechnical Commission (IEC) titled “Photovoltaic system performance—Part 1: Monitoring”, is frequently slow to detect critical operational anomalies, particularly those related to energy self-consumption where conventional generation-centric metrics may appear normal. This work presents a validated industrial SCADA (i.e., Supervisory Control and Data Acquisition) framework designed for the accelerated fault diagnosis of such systems. The proposed methodology leverages high-resolution, real-time visualization of specific energy-flow indicators, including the Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR), to provide immediate operational intelligence. The novelty of this approach lies not in the individual parameters themselves, but in their synergistic integration into a validated, high-speed SCADA system design and real-time diagnostic methodology. The framework’s diagnostic superiority was validated on two distinct, real-world case studies in Jaén, Spain (a 2.97 kW residential and a 58.5 kW commercial system), with primary research results confirming: (1) a simulated comparative benchmarking study demonstrated a significant reduction in Mean-Time-to-Detection (MTTD), achieving a consistent diagnostic speed improvement of over 80% for critical anomalies, and (2) a 10,000 h probabilistic simulation confirmed the statistical robustness of the proposed indicators across a wide range of operating conditions. By demonstrating the practical implementation of these principles within a scalable industrial platform, this work provides a validated and reproducible technical methodology that enhances PV system diagnostics, translating performance metrics into a tangible, high-speed tool for improving operational reliability.