Based on the Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), aggregated data can systematically be structured and analyzed, and future events can be anticipated. The relevance of analyzed data will increase exponentially as the data volume increases. This is an important benefit which we use to efficiently help investors, operators and financing banks reach their goals now and in the future: using data to accelerate decision-making processes and to develop new business models.
Data Intelligence relies on the Internet of Things (IoT), which is the interconnection and communication among facilities, machines, and devices. At Kaiserwetter, the IoT is all about interconnecting renewable energy facilities with Machine2Machine Connectivity, which has no geographical limits. Ultimately, the implementation of IoT is in line with our global business model. We remove borders to aggregate unstructured data and make them accessible for our AI solutions.
We aggregate and analyze data to create added value based on YOUR data, i.e., as our client, you will remain the owner of all technical and financial data that we enrich. We use data science and sophisticated platform technology to create actual added value from our client's data. Compared to the "Software as a Service" (SaaS) approach, DaaS is more flexible, more robust, and more forward-looking. And here is what's most important: DaaS does not impose any limits on itself. While software service packages are fully tailored to their specific application (and expanding them is time-consuming), our data platform can process any data volume that is required.
When current data are collected on an ongoing basis and historical data from time series are added, machine learning becomes possible. Patterns, probabilities, and principles are identified in existing data sets and are then used to train machine learning algorithms to simulate future processes based on historical time series. This is how we infer relevant information from renewable energy assets that were analyzed by algorithms specifically developed for this purpose. This allows us to identify and address irregularities and anomalies in a facility's operations at an early stage.
We use predictive analytics to make statements about future developments regarding an asset’s operations. One example of this is early failure detection. Thanks to our machine learning algorithms, we can identify technical failures at wind turbines or solar facilities at an early stage or even anticipate them. This makes it possible for clients to minimize loss of revenue and avoid downtimes. Another example is forecasts for financially relevant KPIs which, early on in the process, provide information that a specific goal has not been reached.