Periodic Reporting for period 1 - AI-METHOD (INNOVATION ASSOCIATE FOR RESEARCH, DEVELOPMENT, AND APPLICATION OF INTELLIGENT MODELLING FROM EARTH OBSERVATION DATA)
Berichtszeitraum: 2019-10-15 bis 2020-10-14
The envisaged innovation of geopredict is the development, implementation, and commercialization of a short to medium-term atmosphere forecasting Software-as-a-Service platform (CLIMFOR) for climate and weather dependent applications with energy forecasting as initial markets. The concept relies on original self-organizing modeling technologies using high-resolved space-based earth observation data stored and maintained in a Big Data platform for distributed High-Performance Computing. Our short-term goal is to address unmet needs in the energy industry: (i) Regional energy forecasting and (ii) Regional climate forecasting services for power generation resource assessments.
The main objective of the project was to significantly strengthen geopredict’s resources, knowledge and skills in order to amplify the competitive advantage of our geo-forecasting solutions based on Intelligent Inductive Self-Organizing Modeling and Forecasting technologies. The project played a strong role in the long-term business strategy of geopredict towards a major European player in R&D driven forecasting and self-learning predictive modeling technologies, leveraging the power of earth observation data, for highly-relevant industrial and societal problems.
The key objectives of the project have been achieved successfully. Some results have been implemented, already, and they have shown a positive impact on our CLIMFOR solution applied to one of our pilot users in the energy sector.
The main objective of the project was to significantly strengthen geopredict’s resources, knowledge and skills in order to amplify the competitive advantage of our geo-forecasting solutions based on Intelligent Inductive Self-Organizing Modeling and Forecasting technologies. The project played a strong role in the long-term business strategy of geopredict towards a major European player in R&D driven forecasting and self-learning predictive modeling technologies, leveraging the power of earth observation data, for highly-relevant industrial and societal problems.
The key objectives of the project have been achieved successfully. Some results have been implemented, already, and they have shown a positive impact on our CLIMFOR solution applied to one of our pilot users in the energy sector.
During the project the company organized, supported and run a number of tailored training activities ranging from language courses, basic IT skills, state-of-the-art research topics to classical academic tasks such as paper writing and presentation on conferences. From June 2020 to October 2020 the Innovation Associate participated in three seminars and ten webinars of the Innovation & Business Management Core Training Programme organized by EASME and performed by Ernst&Young, which aimed at acquiring and developing specialist skills in the Innovation Management area.
Main R&D results from this project are: (i) concept development of a storage and query framework for satellite earth observation data in a geospatial big data platform, (ii) design, development and implementation of a sophisticated algorithm for probabilistic forecasting, (iii) design, development and implementation of an algorithm for adaptive forecasting of time processes based on probabilistic forecasts, and (iv) design and development of an algorithm for structural and parametric identification of poly-harmonic functions of optimal complexity.
The obtained results were published in two papers and presented and discussed at two conferences. An extended version of one paper was also accepted for publication in the book series "Advances in Intelligent Systems and Computing" by Springer to be published in 2021.
Main R&D results from this project are: (i) concept development of a storage and query framework for satellite earth observation data in a geospatial big data platform, (ii) design, development and implementation of a sophisticated algorithm for probabilistic forecasting, (iii) design, development and implementation of an algorithm for adaptive forecasting of time processes based on probabilistic forecasts, and (iv) design and development of an algorithm for structural and parametric identification of poly-harmonic functions of optimal complexity.
The obtained results were published in two papers and presented and discussed at two conferences. An extended version of one paper was also accepted for publication in the book series "Advances in Intelligent Systems and Computing" by Springer to be published in 2021.
Our innovation will constitute a clear breakthrough relative to existing forecasting solutions in the field as it fills the known gap in atmosphere forecasting between short-term local weather and long-term global climate forecasts. With this key enabling technology, different industries will benefit from high-resolved spatiotemporal forecasting, which is beyond state-of-the-art models and weather station-data based medium-term forecasting approaches at low spatiotemporal resolutions.
We succeeded with our ESA AI-Kickstart project SEED (https://business.esa.int/projects/seed) on technical and economic feasibility of our system for daily renewables energy production forecasting for the Indian energy market. A development version of our system has been applied for a solar farm company in India. It has been shown that the CLIMFOR system, also based on the work of the Innovation Associate on probabilistic forecasting within this AI-METHOD project, is able to reduce penalty payments of renewable energy producers according to India’s Deviation Settlement Management regulatory framework up to 50% in comparison to existing solutions.
We succeeded with our ESA AI-Kickstart project SEED (https://business.esa.int/projects/seed) on technical and economic feasibility of our system for daily renewables energy production forecasting for the Indian energy market. A development version of our system has been applied for a solar farm company in India. It has been shown that the CLIMFOR system, also based on the work of the Innovation Associate on probabilistic forecasting within this AI-METHOD project, is able to reduce penalty payments of renewable energy producers according to India’s Deviation Settlement Management regulatory framework up to 50% in comparison to existing solutions.