Although photovoltaic arrays are a highly reliable solution to generating energy, the full scale use of these integrated arrays presents formidable challenges since their future generation carries high uncertainty. In this paper, the success of a large-scale solar farm in generating different percentages of its committed value is forecasted by a new opinion formation model. In the proposed model, each node uses local generation data, transforms them to a fuzzy set called perception, and evolves its fuzzy opinion about the entire farm’s future generation by using this perception and negotiating with other nodes. As a result, the fuzzy predictions, which can be used in fuzzy energy management systems, are achieved with simple, distributed, and scalable computations. As a case study, Belgium is considered here as the entire solar farm with its provinces as the local generation units. To evaluate the predicted fuzzy opinions, we extract the lower/upper and point-wise predictions from them and compare these extracted values with the outputs of central data-driven forecasting algorithms. Furthermore, to evaluate the proposed aggregation mechanism, the obtained opinions are compared with the fusion of perceptions by conventional fusion algorithms. The results show that in both experiments, the proposed strategy performs better than its competitors in the sense of maximum absolute percentage error and mean-squared error.

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