A photovoltaic/thermal solar collector operates efficiently if the surrounding conditions are in a favorable state; where the factors or parameters such as solar radiation, ambient temperature, photovoltaic collector temperature and air mass flow rates are taken into consideration to ensure the performance of the collector achieves optimum level. Dependency on surrounding conditions limited the width of analysis that could be done on the factors affecting the performance of the solar collector. This study aims to generate fuzzy rules for solar collector performance evaluation. Experiments on the performance of a single passage air photovoltaic/thermal solar collector have been carried out, and a set of membership functions representing all significant factors has been generated. Then fuzzy rules of forecasting were developed using a weighted subsethood-based algorithm to predict the efficiency of the photovoltaic/thermal solar collector. In this fuzzy time series application, the concept of fuzzy rule-based systems was embedded to generate fuzzy if-then rules. The results showed that the PV/T solar collector performance with changes in parameters could be predicted based on the fuzzy rules that have been generated, and thus further could be used to determine the optimum factors conditions required to achieve optimum collector performance without having to carry out experiments. © Published under licence by IOP Publishing Ltd.