If you are not able to create a predictive algorithm, don’t worry!
You will still be able to win a grand prize of 5K for each challenge!
BUT! Don’t underestimate the importance of the algorithm,
as it’s still a large part of the scoring of the general challenge.
So much so that, If you are able to create a predictive algorithm AND incorporate it into your project, you will be eligible for the extra prize of 2K (each challenge will have this option to win the extra cash prize)!
2.5 K in the form of working with InnoEnergy to get your solution ready for the next stages of development. Two teams will each receive this price
2K for best tech achievement per challenge
1K in kind for the winners of the drone race, buzzer quiz, best tweet, and the solution that has the best link with the exhibition.
Antoni Martinez is the Senior Advisor of InnoEnergy
For 16 years he was the CEO of Ecotècnia, a wind turbine manufacturer that was acquired by ALSTOM in 2007 and nowadays is GE.
He was also Director of the Catalonia Institute for Energy Research (IREC).
Henrik Stiesdal is one of the pioneers of the modern wind industry. He built his first wind turbine in 1976 and in 1978 designed one of the first commercial wind turbines, licensed by Vestas in 1979. Stiesdal worked with Vestas until 1986 and joined Bonus Energy, later Siemens Wind Power in 1987. In 1988 he was appointed Technical Manager, and in 2000 Chief Technology Officer. He retired at the end of 2014.
During his 40 years in the wind industry Stiesdal has worked with all aspects of wind turbine technology, including fundamental research, turbine design, manufacturing, sales, project implementation, service and quality management. Post-retirement activities include floating wind turbines, energy storage, carbon-negative fuels, Lidars, and avian deterrent technologies.
Henrik Stiesdal is an associate professor at DTU Wind Energy and at University of Maine.
Major in Information and communication engineering, expert on Machine learning Algorithms, like Boosting, SVM, DNN, Time Series, Big Data For Machine Learning Optimization, Parallel computing.
Rich experience on renewable energy big data algorithms, be responsible for EnOS Prediction Engine design, building time series forecast models, customer profile models and energy device models for EnOS; also expert on Wind Prognostics & Health Management, e.g.: Wind turbine health and fault prediction models, Wind Forecast models and service.
Hugo Fanlo Virgós is an Analyst at Performance Management department in EDP Renewables
since 2015, where he designs, develops and makes use of different tools aimed at improving the power generation of wind turbines.
Graduated from the University of Oviedo in 2004, with a master degree in Chemistry and a PhD from the University of Groningen, he is currently interested in predictive maintenance including software-based condition monitoring tools, vibration monitoring and oil analysis.
Cleaner Energy @ EDP Inovação
Tiago is a project engineer at the Cleaner Energy group of EDP Inovação. Holds a Master in
Mechanical Engineering since 2010 and a post-graduation degree in Sustainable Energy Systems by the MIT Portugal Program. He has been with EDP Inovação since 2013, working on several innovation projects in onshore and offshore wind.
His interests include modeling of wind turbines, predictive maintenance and floating wind.
Since time is money, anticipating failures in wind turbines could reduce the amount of time spent on maintenance and production (due to unavailability), thus resulting in lower, levelized costs of clean energy. How many failures can you anticipate?
It is up to you to use the provided data to develop a solution that will facilitate a reduction in failed wind turbines.
The team who detects the most early stage failures will enable the highest cost savings―thus, will be named the winner of the challenge. Files including sensor data from wind turbines and previously detected damages will be provided to help you create and train your models. Finally, your models will be put to the test to confirm exactly how much you can save.
Due to the design of turbine distribution and the complex terrain that often surrounds wind farms, it becomes increasingly difficult to predict wind speeds on an individual turbine level. Your goal is to find new solutions that use these predictions and put them to use in effective and practical solutions.
You will need to use the provided data to create a solution for the wind energy sector that takes into account wind speeds, weather prediction and/or relative geography.
Wind forecasting predicts the speed of wind turbines based on predicted weather conditions for a wind farm and a wind turbines’ relative geography. In this competition, competitors are required to predict the wind speed series over the next 24 hours with a time interval of 1 hour for each turbine.
You will be scored according to the following criteria:
The predictive algorithm created (35% of points)
UI/UX design of your solution (20% of points)
Presentation of your solution (20% of points)
Viability/business case (25% of points)
Ph. D. , EnOS Chief Architecture
Over 17 years research & development experience in data related field. Majoring in data platform architecture, data modeling, data product design, knowledge discovering, data warehouse & BI and data mining.
He used to work in IT & internet companies, leading the HPC & in-database analytic projects, building typical internet big data warehouse to support business analysis & user profiling system. Currently, working in the streaming data platform & data visualization products, focusing on the research & development for big data management & open platform in IoT industry.
Isaac Justicia holds a doctoral degree in Chemistry and doctoral degree in Materials Science from the UAB as well as an MBA from ESADE. He is a well-recognized professional in the wind power sector.
Isaac has over 10 years of experience in the wind power sector holding positions of Project Manager and Key account Manager at ALSTOM WIND (2006-2008), Operations Coordinator at RWE Innogy (2008-2009), Business Development Manager at SINOVEL (2012-2013) and Business Development Manager at Wind Power Transmissions (2013-2014).
Currently, Isaac is collaborating with Smartive as a Business Developer Manager. He has wide experience in commercial, sales and operations activities.
Jordi Cusidó i Roura (CEO), MSc & PhD in Industrial Engineering from the Universitat Politècnica de Catalunya (’05 – ’08).
He did his thesis on the study of signal processing techniques for the diagnosis of rotating electrical machines, awarded as the best thesis (UPC ’10).
His business background stems from postgraduate courses at Stanford University (’11-’12) in Negotiation, European Patent law, project management, team management and business development.
He has almost 10 years of job experience and his strengths are in the management of Intellectual Property and Business Development. Jordi has cofounded 3 startups including SMARTIVE.