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In this paper we describe a fuzzy rule-based classifier applied to forecasting of energy production in solar photovoltaic installations. After adapting the available numerical data to a dataset appropriate for classification, we propose a processing method to create an efficient rule base. The aim is to build an intelligent system able to forecast the class label of the energy production from a photovoltaic installation, given the values of some environmental parameters. Despite some already existing methods for forecasting problems, the main advantages of our approach are easier interpretability and versatility, as we deal with class labels. Moreover we propose a way to extract an ad hoc training dataset, in order to perform an effective training even when we deal with non optimal data (e.g., non-uniformly sampled data, missing samples, etc.). With a fuzzy forecasting system, in place of a traditional one, even the non-expert user of a photovoltaic system may be able to make decisions more easily. The results obtained show a correct classification percentage of almost 93%.