A quick-scan algorithm has been developed in order to evaluate rooftop PV potential in the Netherlands. Both its panel fitting and yield prediction functions have been validated with existing systems monitored by Solar Monkey. The calculation times of different parts of the algorithm were measured and decreased while keeping the accuracy of the algorithm in the desired range.

First, a new approach to determine the roof segment orientation was introduced, using both the normal vector and the longest side of the roof segment polygon. A visual inspection was carried out, in which recent aerial images were compared to 3D roof segments that were provided by Readaar. For 145 roofs, the roof segments on which PV was placed were manually selected, such that they could validate the quick-scan. From the visual inspection it was deduced that in-roof obstacles were often not detected. Sometimes the customer had no desire to use the full potential of the roof, or preferred a rectangular panel layout instead of fitting the maximum amount of panels. Another finding was that the distance kept from the roof segment edge was much smaller than initially expected. For pitched roofs, there was virtually no distance between the roof edge and installed panels, while for flat roofs around 20 cm was kept. Using zero distance from the roof edge, the panel placement algorithm still underestimates the roof potential by 17.5% on average, with a relative standard deviation of 46.3%.

Three yield calculation methods were compared: the Solar Monkey method, the SVF & SCF method, and the method without obstacles. The predicted performance or final annual AC yield was compared with the actual measured performance, both measured in kWh/kW_p per year. For the three methods, relative standard deviation values of 7.2%, 7.5% and 9.1% were found respectively. The three methods could generate yield predictions for 91.0%, 92.7%, and 93.1% of the 145 roofs. For highly shaded roofs with Sun Coverage Factor values above 0.25, the method neglecting obstacles performed significantly worse. Additionally, the performance of large roofs with an average segment area above 70m² was generally under-predicted, while the relative standard deviation was highest. It is expected that using one obstacle view is not accurate enough for large roof segments.

The computational speed of the panel fitting algorithm for pitched roofs without internal obstacle segments was found to be 20.1± 5.0 m²s^-1, whereas for flat roofs it was found to be 56.9±12.0 m²s^-1. In order to optimise the quick-scan algorithm in both speed and accuracy, two filtering steps were carried out. Segments with a pitch angle over 10° and an orientation between 0° to 60° or 300° to 360º were filtered out, since they would have an annual performance below 650 kWh/kW_p. Moreover, segments with an area less than 8.4 m² were filtered out, since they were observed to fit less than 2 panels. The quick-scan calculation times for different yield prediction methods were found to be 15.50±1.01, 14.58±1.13 and 2.75±0.44 seconds per roof, respectively. These times were measured for data sets that had around 2 segments per roof after filtering on segment area and pitched segment orientation. It can be concluded that the method without obstacles is preferred when the calculation time is a limiting factor, whereas for accuracy the Solar Monkey method is preferred.

The amount of photovoltaic (PV) systems in the world is growing rapidly. The designing process is however still old fashioned and mostly done by hand. Creating a method to automatically design these PV systems can help to stimulate this growing market even further. In this thesis a research is done
into the development of an automatic PV system design algorithm.

The research consists of two main parts. A section related to panel placement and a section related to the inverter choice. Strategies were developed for both parts and that resulted in several prototype algorithms. These algorithms were tested to see if these algorithms have practical potential. The panel placement algorithms are divided into two categories: maximum panel placement and finite panel placement. Development of these categories was done for both flat and pitched roof sections. The shading caused by surrounding obstacles was taken into account to find the most optimal positions to place the panels. A grading system for the panel layout was developed to ensure that preferred layouts
are found. The shading caused by the surrounding obstacles was also used to determine the type of inverter is that optimal for certain panel configurations.

A proof concept was conducted for the developed algorithms by testing algorithms on real roofs and comparing the results with PV system designs that were made manually. The designs were compared with each other in terms of predicted PV system performance, the layout and the duration of the designing process.

It was demonstrated that the finite panel placement algorithm produced PV systems with an average performance difference of about - 0.83 % with a standard deviation of 5.26 %, compared to the manual designs. The maximum panel placement algorithm took an average of 2.7 minutes with a standard
deviation of 2.1 minutes. The finite panel placement algorithm took on average 3.8 minutes with a standard deviation of 5.5 minutes. Both of which can be considered as being significantly faster than making a manual design.

For a PV system with many panels and several string inverters, algorithms were developed that predict the optimal configuration of the strings. It also predicts the total performance losses that occur when panels are connected in series. These algorithms can help to determine the optimal type of inverter and how to optimally configure separate panel strings in large systems. The algorithms work
based on initial tests, but not enough testing has been done to be conclusive.

The work done in this thesis can be used as a stepping stone for further development of automatic PV systems design algorithms. The panel placement algorithms and the inverter algorithms can be developed further into a complete automatic PV system design algorithm.