Resource Planning Optimization (RPO) is a common task that many companies need to face to obtain several benefits, like budget improvements and run-time analyses. It is often addressed by using several software products and tools, based on sophisticated mathematical artifacts. However, these tools are not able to provide a practical solution because they are often expensive and time-consuming. On the other hand, Artificial Intelligence-based approaches have been increasingly used in many industrial and scientific fields in last decades, and have demonstrated to be a valid alternative to the classical mathematical-based methods. For this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach. Additionally, we show that multiple neural networks achieve up to a 6% improvement in average accuracy over Naive Bayes classifier, up to a 12% over Multi-Layer Perceptron classifier and up to a 13% over state-of-the-art ANNs in the presence of unbalanced training dataset.