IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling
In case any of the links in the documentation section are not working, get the full competition pack here.
2021-09-18 We have added a new section on this page with clarifications about Phase 2, and we have added an FAQ to the documentation section. Alternatively get it here.
2021-08-20 The leaderboard was not updating for some days but is now back and working.
2021-07-30 We had technical problems which made that the web page was unavailable over the last day or so. Sorry for that. All should be back to working properly again.
2021-07-04 In case any of the links in the documentation section are not working, get the full competition pack here.
One of the most important challenges to tackle climate change is the decarbonisation of energy production with the use of renewable energy sources such as wind and solar. A challenge here is that renewable energy cannot be produced on demand but the production depends literally on when the wind blows and when the sun shines, which is usually not when demand for electricity is highest. Storing energy is costly and normally associated with loss of energy. Thus, with having more and more renewable energy in the grid, it becomes increasingly important to forecast accurately both the energy demand and the energy production from renewables, to be able to produce power from on-demand-sources (e.g., gas plants) if needed, to shed loads and schedule demand to certain times where possible, and to optimally schedule energy storage solutions such as batteries. In particular, a nowadays common setup is a rooftop solar installation and a battery, together with certain demand flexibilities. Here, we need to forecast the electricity demand, the renewable energy production, and the wholesale electricity price, to be able to then optimally schedule the charging and discharging of the battery, and to schedule the schedulable parts of the demand (when to put the washing machine, when to use the pool pump, etc.). In this way, we can charge the battery with overproduction of solar energy, and use power from the battery instead of power from the grid when energy prices are highest, as well as schedule demand according to energy availability.
Researchers from the IEEE Computational Intelligence Society (IEEE-CIS) want to improve solutions to this complex problem of predict+optimize, in this particular application of scheduling in the context of renewable energy. IEEE-CIS works across a variety of Artificial Intelligence and machine learning areas, including deep neural networks, fuzzy systems, evolutionary computation, and swarm intelligence. Today they’re partnering with Monash University (Melbourne, Australia), seeking the best solutions for battery and load scheduling, and now you are invited to join the challenge. Monash University is committed to achieve Net Zero emissions by 2030, within the Monash Net Zero Initiative. As part of this initiative, Monash has set up a microgrid with rooftop solar installations and a battery for energy storage. The challenge will use data from the Monash microgrid and you will develop an optimal schedule for the Monash battery and lecture theatres. From a machine learning point of view, the provided data poses an interesting time series prediction problem, with multiple seasonality, use of external data sources (weather, electricity price), and the opportunity for cross-learning across time series on two different prediction problems (energy demand and solar production). Then, from an optimization point of view, uncertainty in the inputs needs to be addressed together with a couple of constraints, to achieve a good solution. If successful, you will not only help making renewable energy more reliable and affordable, thus playing your part in the fight against climate change, but the proposed technical challenge may be applicable in many other fields facing similar problems of optimal decision-making under uncertain predictions. Please report any issues or feedback to firstname.lastname@example.org
Data description and submission requirements
The goal of this competition is to develop an optimal battery schedule and an optimal lecture schedule, based on predictions of future values of energy demand and production. In particular, in this project, we have the following data available. Energy consumption data recorded every 15 minutes from 6 buildings on the Monash Clayton campus, up to September 2020. Solar production data, again with 15 minutes of granularity, from 6 rooftop solar installations from the Clayton campus, also up to September 2020. Furthermore, weather data is available from the Australian Bureau of Meteorology and electricity price data is available from the Australian Energy Market Operator. The goal of Phase 1 of the competition is now to optimally schedule a battery and timetabled activities (lectures) for the month of October 2020. In real life, the battery scheduling would usually happen on a daily basis, with day-ahead forecasting. For the competition the test set cannot be disclosed, so that a whole month needs to be forecasted. However, with the availability of weather data, this task is still close to the real world application, with the assumption of having perfect 1-day-ahead weather forecasting and having perfect electricity price forecasting. Phase 1 of the project includes a public leaderboard where participants submit forecasts and the leaderboard shows the evaluation of the forecasts. Then, in Phase 2 of the competition, data for October 2020 is released to the participants, and they are now asked to perform the same forecasting and optimisation exercise for November 2020. Now, only minimal feedback is provided to the participants about the quality of their submissions. Solely Phase 2 of the competition is relevant to determine competition winners and prizes.
The 3 main competition prizes will be awarded to the schedules that lead to the lowest cost on the Phase 2 test set. An additional prize will be awarded to the team that achieves the most accurate forecasts on Phase 2.
In addition to scheduling solutions and forecasts, you are requested to submit a draft description of your methodology (up to 1000 words). Note that If you are working in a team you should indicate this clearly in your final submission. Only one submission for each team will be considered during the shortlisting.
Full descriptions of the data and submission requirements, as well as the optimisation problem, are available in the data description guide (in the DOCUMENTATION section of this page).
We are interested in obtaining a schedule for the activities and the battery that leads to the lowest cost of energy over the test period, while fulfilling all constraints of the scheduling problem. In addition to that, there will be a secondary prize for the team that achieves the lowest forecasting error, as measured by the Mean Absolute Scaled Error (MASE) across all series in the competition, over the Phase 2 test set. See the Leaderboard and all submissions at the bottom of the page.
Details about the evaluation can be found in the evaluation guide (in the DOCUMENTATION section of this page).
Note that the Leaderboard table is updated every 5 minutes. In case you submit a wrongly formatted submission, you will be listed at the top of the submissions' table with an empty row.
At the end of the competition, and looking at the aspects explained above, the Technical and Scientific Committees will shortlist the top 5 submissions. Shortlisted authors will be asked to provide a final description of their methodology (4 pages in IEEE format, more details will be provided at the time).
Final submissions will be carefully assessed according to the following criteria:
Performance of the final submission, in terms of cost (for the Predict+Optimize prizes), or in terms of forecasting error (for the forecasting prize).
Novelty of the proposed approach and appropriate use of Computational Intelligence techniques if any (not required!). The Scientific committee will be asked to rank the shortlisted independently and this will be used to compute a score.
Note that use of data other than the one provided is not allowed a priori. If you want to use additional data, write to the organisers who will decide if the additional data source is adequate, and will then make this data source available to all participants in the competition. Otherwise, we will not consider your submission.
Phase 2 of the competition
At the end of Phase 1 and beginning of Phase 2, we will release 10 new instances for the optimisation, together with the test set of Phase 1 for the forecasting. The leaderboard will then switch to evaluate towards these new instances and the new forecasting time frame. The leaderboard will then not show anymore exact numbers, but only if the MASE is above or below the MASE of the sample solution, and if the energy cost is above or below the sample solution, and if the solution is valid or invalid. The last valid submission per team will be used as their final submission.
July 1st, 2021 – Competition starts
October 4th, 2021 – Phase 1 closes: Final team merger deadline - you need to notify the organisers via email by this date if you participate as a team
October 25th, 2021 – Phase 2 closes: Final submission deadline
November 1st, 2021 – Shortlisting announcement
November 29th, 2021 – Deadline final description report
December 4th-7th, 2021 – Shortlisted solution presentations at the 2021 IEEE Symposium Series on Computational Intelligence (SSCI). The conference will be held virtually and registration for the 5 shortlisted submissions will be covered
December 7th, 2021 – Awards ceremony
All deadlines are at 11:59 PM (AoE – Anywhere on Earth) on the corresponding day unless otherwise noted. The competition organisers reserve the right to update the contest timeline if they deem it necessary.
Awarded by the Committee based on the criteria mentioned above.
1st Prize: $7,000 (USD)
2nd Prize: $5,000 (USD)
3rd Prize: $3,000 (USD)
Forecasting Prize: $2,000 (USD)
All teams, regardless of place, are also strongly encouraged and invited to publish a manuscript of their solution (and open source their code, if possible).
Technical Challenge Committee
Christoph Bergmeir (Chair, Senior Research Fellow, Monash University)
Frits de Nijs (Assistant Lecturer, Monash University)
Scott Ferraro (Program Director, Net Zero Initiative, Buildings and Property Division, Monash University)
Luis Magdalena (CIS VP TA, Universidad Politécnica de Madrid)
- Peter Stuckey (Professor, Monash University)
- Quang Bui (Research Fellow, Monash University)
Rakshitha Godahewa (PhD student, Monash University)
Priya Galketiya (Net Zero Engineer, Monash University)
Robert Glasgow (Lead Digital Architect, Monash University)
We are very grateful to the Department of Data Science and Artificial Intelligence of Monash University for their sponsorship. Furthermore, we would like to extend our thanks to Prof Ariel Liebman from Monash University. OikoLab kindly provided us ERA5 weather data.