Construction planning is a critical and complex phase in the deployment of large-scale renewable energy infrastructure. This study applies artificial intelligence techniques to a domain-specific problem that has traditionally relied on expert judgement: the generation of detailed construction schedules for photovoltaic power plants. As renewable generation is a key part to meet the challenges of energy transition, the implementation of large projects has increased in recent years and this trend is expected to continue in the future. The main difficulty in meeting construction deadlines is the elaboration of an adequate planning. A tool that automatically generates schedules can be of great help to set up an initial baseline planning. To this end, this work compares five artificial intelligence techniques, on a data set consisting of real examples of successfully completed projects. The evaluation of the results obtained on test data shows that Adaptive Neuro-Fuzzy Inference System (ANFIS) is the technique that obtains the best performance in all error metrics, although it entails a high computational cost. The model thus obtained manages to generate a complete construction schedule with an error of 8% of the total duration. The use of metrics as MAE, MSE and provides a robust understanding of prediction accuracy, variability, and fit. These metrics are commonly used in project planning evaluations and help interpret model behaviour under different error profiles. Additionally, the resulting 8% total duration error implies a deviation of around 24 days in a 300-day project, which is highly actionable in real-world solar project management. The findings not only demonstrate the feasibility of using AI for solar construction planning, but also lay the groundwork for the development of intelligent software tools or platforms that could support planners in the renewable energy sector. While this study focuses on photovoltaic plants, the approach is extendable to other power plants as wind farms, combined-cycle or nuclear plants, or even to other construction projects.