The energy industry and especially in the Renewable Energy Sources (RES) is one of the sectors which creates huge volumes of sensor data. The technical challenges are staggering: a single wind turbine has 7000 sensors that, ideally, we would like to report in subsecond periods (infeasible now due to data management limitations), while manufacturers monitor thousands of turbines. Moreover, the impact on the economy is huge: the RES industry was estimated at 1,469 billion USD in 2017 and even small increases in the productivity and the competitive advantage of the EU companies in the sector will have a significant impact on EU economy. MORE focuses on creating the software tools for data analytics tailored to the needs of RES stakeholders. The main objectives of MORE are:
Objective 1: Creating a platform that can consume billions of streams and 100s of petabytes of data.
MORE will address this challenge with a twofold strategy: a) edge analytics to locally perform fast simple event detection and b) time series modelling and compression, supported by the edge processing. We plan a system where data will be modelled and summarized in the whole data processing pipeline and analytics will be directly applied to the summaries.
Objective 2: Accurate prediction, forecasting and diagnostics.
MORE will achieve its aim for accuracy in prediction and diagnostics by focusing on incremental machine learning algorithms that can scale better than most existing approaches and are updated continuously. Moreover, it will also work on highly parallelizable pattern extraction methods, by focusing on motif extraction techniques which are developed especially for time series data. By extracting motifs from the collected historical data, we can identify patterns that are linked to important events in the RES installation or reveal properties of the components
Objective 3: Reduce the human effort for building complex learning models. Limited computational resources or lack of sophisticated techniques is not the only obstacle for extracting the desired information from time series data. A significant problem is the human effort required to parametrize a machine learning model. AutoML is a set of automation services that help the end user perform feature extraction, model tuning and other time consuming and complex tasks. However, existing AutoML approaches do not cover IMLA algorithms or ML algorithms for time series.
Objective 4: Have a tangible result on RES management
In MORE we strive not to just beat the benchmarks, but also provide solutions validated by our industry partners. The industry partners in the consortium have identified several important goals for RES data analysis. Their goal setting derives not only from their own efforts in data analysis, but also from several other RES sector stakeholders, who need feedback from monitored data analysis. The patterns and features that the RES industry desires, indicate conditions with important operational and financial impact.
MORE has achieved all its objectives and has delivered an open-source platform with software that implements tools for managing RES data with capabilities beyond the state-of-the-art.
