Our executive summaries provide tables that summarize all results from different chapters and related analyses, ultimately giving clients detailed conclusions and recommendations that will allow them to improve their assets' performance.
Our HSI provides information about the "health" of machines or the inverter of every single renewable energy facility. The evaluations are presented in graphics following the traffic light principle. This allows clients to easily identify the actual status of different components and the evaluation of specific performance analyses.
Based on daily output data and 10 or 15-minute interval data, we calculate the aggregated energy production per year. This information is compared with the annual forecast and the monthly output distribution and subsequently analyzed. In addition, key performance indicators (KPIs) such as full performance hours and the yearly utilization as well as production-specific KPIs are calculated and visualized.
Based on power curve analytics, this chapter examines the performance of specific facilities or the entire park to identify optimization potential. In the "potential analysis" subchapter, clients will find information about how high the additional output potential for their asset is so that they can maximize its performance in the long term.
This chapter assesses the facility in question based on meteorological conditions. For wind turbines, these include wind speed distribution, wind direction, wind direction specific energy production as well as an analysis of potential wake effects in the park. For solar energy facilities, locally measured solar radiation values in connection with the course of the sun as modeled for the site in question are analyzed along with potential shading effects in the park.
This part of the report analyzes all temperatures measured in the main components of every system. Anomalies and irregular operational conditions will be identified using 10-minute data. In the case of solar energy facilities, temperature analytics refers to all temperature values delivered by production units (e.g., measured module temperatures, temperatures from inverter components, etc.)
In this chapter, all provided data are checked for completeness, and data quality is assessed. We believe that this data analysis is essential because it is the basis of all our analyses and assessments. The higher the data quality, the more reliable the analyses will be.
IRIS is a technological spin-off of our unique cloud-based IoT/AI platform ARISTOTELES. The powerful analysis options inherent to algorithms developed by us set a new standard for time-specific data analyses of renewable energy assets. The result: highly precise, individually priced reports for investors, financing banks, operators and other stakeholders that will allow decision-makers to make the very best strategic decisions thanks to smart data analytics. This makes IRIS a driver for tomorrow's renewable energy investment strategies, starting today.