Your photovoltaic plant or wind farm has been operational for a few years, and you have noticed that it is not producing according to the initial project plan. This impacts the long-term predictability of your park and, consequently, the financial results and profitability of the project.
You are aware that your project is falling short of expectations, and you are unsure if there is anything you can do to improve it because you have not been able to pinpoint the true reason for this discrepancy. Perhaps the park’s operation is not optimal, and there are unforeseen plant shutdowns, either due to equipment malfunctions or external factors like environmental or social constraints, or even from the electrical grid, known as ‘curtailment’ due to overproduction. You are unsure if equipment performance is optimal or if it could be enhanced. Maybe the estimated resource, be it wind or radiation, has not materialized due to inaccurate forecasting.
The first step towards making the right decision is to understand the cause behind the production decline, in order to successfully address a new operational model. But how do you quickly and reliably identify the cause without significantly affecting or minimizing the park’s operation?
AI-Based Models
Models based on algorithms created using artificial intelligence emerge as a powerful solution to quantify production loss and identify the reasons for the plant’s suboptimal performance. Entities like CIRCE can detect issues without disrupting the daily operation of the plant, in an agile manner and with the independent assurance of a third party.
However, not just any model will suffice. It must be built on the experience of analyzing large volumes of data over an extended period and a comprehensive understanding of both the data and the operational plants. In this context, being able to identify the various factors triggering machinery alarms is crucial for effectively managing the information provided by data mining models.
Reversing Energy Losses by Up to 25%
Through a thorough analysis using a robust model, at the CIRCE technological center, we have observed that it is possible to identify energy production losses of up to 25% and largely reverse them by providing tools to facilitate necessary changes in operational policies. This ensures that your renewable generation plant achieves its optimal output. As a result, renewable plant owners enhance their competitiveness without requiring investments in new equipment, but by optimizing what is already in place.
