Monitoring residential scale photovoltaic (PV) systems is important for maximizing the energy yield and detecting malfunctions. Analytical-based approaches are not reliable in these systems because of the lack of on-site measurements and detailed PV system specifications. In this paper, a collaborative approach is proposed which does not depend on weather data but on similar PV systems. Based on the so-called performance-to-peer approach, the aim of this work is to improve this baseline model by adding PV systems characteristics and by optimizing with machine learning techniques. The methodology has been tested in a fleet of more than 12,000 PV systems located in the Netherlands with up to 7 years of data per system. The proposed model achieves an average (Formula presented.) of 94.1% and a NRMSE of 0.05, outperforming in terms of (Formula presented.) the baseline model by 1.4 points, and the analytical approach by 3.8. The data requirements of this model are not high: With 1,700 years of PV system data with daily resolution, the maximum performance can be achieved as long as a minimum of 6 months of data per system and 100 PV systems are considered. The application of this model for fault detection and categorization has also been shown. The proposed approach has shown its strengths with respect to other methods through its ability of distinguishing between system mismatch and actual fault and of adapting to new situations via retraining.