In this paper a partial multi-criteria analysis (MCA) is used to evaluate and compare methods that can be used for detection and identification of failures or malfunctions during the operation of solar thermal systems. A multi-criteria analysis is often applied to support policy decisions and evaluate different alternatives [1]. One of the advantages of MCA is that the criteria, on basis of which the comparison is made, are explicit. There are several MCA methods; a relatively simple one without weighting the different scores and combining these to a result score will be used here. Since the weights would depend on the application of the method, this will not be carried out here.

The MCA conducted in this paper consists of four steps.

1. Identification of aims and decision makers

2. Identification of failure detection methods that can be used for achieving the objectives

3. Identification of the criteria that are used to compare different options/failure detection methods

4. Generation of a performance matrix in which the expected performance of each method against the criteria is described

The objective of the multi-criteria analysis is to evaluate several failure detection methods with regards to the effectiveness of failure detection. Key players are users of monitoring and failure detection methods and developers. They have similar aims and therefore, this will not be considered here.

2.3. Monitoring and failure detection methods for solar thermal systems (Step 2)

The monitoring methods have been identified in an extensive literature study. One criterion for inclusion was the ability to operate for the whole lifetime of the system. The following methods will be discussed in this paper:

• Manual monitoring with the example of the Optisol Project (MM)

• Function control for small solar thermal systems without heat measurements (FUKS)

• Input-Output Controller (IOC)

• Guaranteed Solar Results (GSR)

• Method developed at Kassel University (KU)

• Spectral method (SP)

• Failure detection with Artificial Neural Networks (ANN)

A further description of the methods can be found in section 3.