This is a spatio-temporal time series forecasting competition. The data provided in this challenge is brought to you by RTE, the French power transmission company and largest European Transmission System Operator (TSO).
Motivations: Numerous improvements of the efficiency of energy infrastructure are anticipated in the next decade from the deployment of smart grid technology in electricity distribution networks. As we progress in deploying renewable energy harnessing wind and solar power generation, we also expect to see growth in power demand for new applications such as electric vehicles. As is known, electricity is difficult to store. Hence supply and demand on the electricity network must be balanced at all times to the extent possible. Failure to achieve this balance may result in network breakdown and subsequent power outages of various levels of gravity. Principally, shutting down and restarting power plants (particularly nuclear power plants) is very difficult and costly as is a redundant grid infrastructure with multiple layers of standby generation sources that are idle a significant portion of time.
Your task: The goal of this challenge is to engage the scientific community to solve the difficult problem of predicting future network power flows using spatiotemporal data , to help managing better the electricity distribution network. This will facilitate automating progressively the task of human dispatchers (see the video). Achieving this goal will require integrating many sources of spatiotemporal information, including weather forecasts (to predict wind or sun availability) and demand forecasts (for example turning on/off air conditioners or heaters).
Who will benefit: Many consumers, including hospitals and people hospitalized at home as well as factories critically suffer from power outages. Using information technology to balance the network will allow us to make better use of the grid and introduce more natural energy generators alongside nuclear power and other conventional power sources to help fulfill energy demands while reducing CO2 emissions and lower power prices.
This challenge is brought to you by the See4C consortium. 3607887462.
During the feedback phase, the participants must submit code to predict the next 11 time steps of the data sequence, given past ones. We compute the average RMSE (root mean square error) over all predictions made at the point level for each time step, averaged over all predicted time steps (in each phase).
To make entries, go to the "Participate" tab. You must be a Codalab user to participate and you must accept the Terms and Conditions of the challenge and the rules. The rules include all instructions found in this website.
Under the "Participate" tab, you will be able to download sample "public" data to familiarize yourself with the task, a starting kit, and a sample submission. The interface must be respected, for both code and results. Code execution time is limited on Codalab to 6 hours for the whole dataset (N steps, each step corresponding to predicting the next 11 time steps) in the "feedback" phase. Number of samples and duration of execution will be doubled for the final "validation" phase.
To create a submission, just zip the script "predict.sh" together with your code (in the starting kit example you will include "predict.sh", "predictSpatioTemporal.py" and the python files in "sample_code/"). IMPORTANT: zip code without directories.
There are 2 phases:
train/ Xm2/ Xm1/ adapt/The data in the train/Xm2/ and train/Xm1/ directories can be used for training. The data in the adapt/ directory will be used for adaptation and testing. In all directories, samples are named Xn.h5, where n is the file index, running from 1 to N in train/Xm2/ and train/Xm1/ and from 0 to N in adapt/. N is different in each directory.
input name : number of steps output name : number of steps X0.h5 : T0 time steps Y0.h5 : 11 time steps X1.h5 : 11 time steps Y1.h5 : 11 time steps X2.h5 : 11 time steps Y2.h5 : 11 time steps X3.h5 : T3 time steps Y3.h5 : 11 time steps X4.h5 : 11 time steps Y4.h5 : 11 time steps X5.h5 : 11 time steps Y5.h5 : 11 time steps X6.h5 : T6 time steps Y6.h5 : 11 time steps ... X599.h5
train/ Xm4/ Xm3/ Xm2/ Xm1/ adapt/Similarly to the previous phase, data in the train/ sub-directories is for training and data in the adapt/ directory for adaptation and testing. Your performance on the test set will appear on the leaderboard when the organizers finish checking the submissions.
This challenge is brought to you by the See4C consortium. Contact the organizers.
This CONTEST is brought to you by the See4C consortium. Contact THE ORGANIZERS.
No, except accepting the TERMS AND CONDITIONS.
You can enter during the feedback phase only. Registration closes at the end of the feedback phase.
From the "Data" page, under the "Participate" tab. You first need to register and accept the TERMS AND CONDITIONS and the RULES to access data.
Register and go to the "Participate" tab where you find data, and a submission form.
We provide a Starting Kit in Python with step-by-step instructions in a Jupyter notebook. We also provide some tutorial material and fact sheets on benchmark methods.
The prizes will be determined by the European commission.
Yes, participation is by code submission.
No. You just grant to the ORGANIZERS a license to use your code for evaluation purposes during the challenge. You retain all other rights. However, the winners will be required to make their code publicly available under an (306) 804-6967 such as, for instance, Apache 2.0, MIT or BSD-like license, if they accept their PRIZE, see the TERMS AND CONDITIONS.
Yes, please download it [HERE].
You are sharing resources with other users on 2 servers with the following specifications:
|CPU||1||E5-2699v3||36 physical / 72 virtual|
|GPU||2||Nvidia Geforce GTX Titan X||6144 CUDA cores|
GPUs are now available. If you experience unreasonable delay to get back results from your submissions, please contact us. The PARTICIPANTS will be informed if the computational resources increase. They will NOT decrease.
This is not explicitly forbidden, but it is discouraged. We prefer if all calculations are performed on the server. If you submit a pre-trained model, you will have to disclose it in the fact sheets. All data "past" will be available to your program on the server. During the feedback phase, you will have available for training the "public downloadable data" (in data/Xm2) and "training feedback phase data" (in data/Xm1). During the final validation phase, you will have available for training the same data as in the feedback phase plus the data on which you were tested during the feedback phase and additional training data, all in four subdirectories of data/: Xm4, Xm3, Xm2, and Xm1. See the "Data" page for details.
No. Submissions of the feedback phase will be forwarded automatically to the last round. However, you will have 3 days in the validation phase to make one final submission if you wish, which will overwrite the last submission you made in the feedback phase. During that time, no feedback will be provided on the leaderboard. The results on validation data will only be revealed once the jury has deliberated, and at the latest on the data of the workshop (April 22, 2017).
Your execution must run in less than 6 hours in the feedback phase and 12 hours in the validation phase. There are twice as many data sequences to process in the validation phase.
In principle no more than its time budget. We kill the process if the time budget is exceeded. Submissions are queued and run on a first time first serve basis. We are using two identical servers. Contact us if your submission is stuck more than 24 hours. Check on the leaderboard the execution time.
Five per day during the feedback phase (and up to a total of 100). Only ONE during the final validation phase. Please respect other users. It is forbidden to register under multiple user IDs to gain an advantage and make more submissions. Violators will be DISQUALIFIED FROM THE CONTEST.
Yes. Please contact us if you think the failure is due to the platform rather than to your code and we will try to resolve the problem promptly.
Your submission does not get scored. The process gets killed.
We may eventually increase it if the burden on our servers is no too high and we see that this is required to beat baseline results. But do not count on it.
Because of simplicity. Everyone understands RMSE. We are aware that this may not be the best metric for the task. Other metrics will be computed. However the PARTICIPANTS will be ranked with RMSE to determine the winners.
The code was tested under Anaconda Python 2.7. We are running Python 2.7 on the server and the same libraries are available. In addition, we also provide a version of Python 3, Octave, Julia, and many other libraries, which are bundled in a docker.
Yes. Any Linux executable can run on the system, provided that it fulfills our interface (see how to call it from the script "predict.sh" in the starting kit. However, we only prepared a starting kit with Python at this stage and have not tested this option. We also provide an example of submission in Octave.
When you submit code to the See.4C platform using Codalab, your code is executed inside a docker container. This environment can be exactly reproduced on your local machine by downloading the corresponding docker image. The See.4C docker environment contains a large number of pre-loaded programs, including Python 2 and 3 (with libraries such as keras, tensorflow, theano, numpy, scikit-learn), Julia, R, Octave, etc. See /hub.docker.com/r/see4c/user/ for details.
Non GPU users, if you are new to docker, follow these instructions to install docker. GPU users, follow these more detailed instructions.
We will step you through running the starting kit inside the See.4C docker. You can follow a similar procedure to run other code.
If you installed docker in a virtual machine, make sure to start the virtual machine (this will be the case if you have an older Mac and used Docker toolbox; the virtual machine can be launched from the launch pad with “docker quick start terminal” or from the command line with “docker-machine ssh default”). Download and unzip the starting kit from the "Participate" tab. Then copy it to the docker machine.
docker-machine scp see4c_starting_kit default:/home/docker
The run the docker:
docker run -it -p 8888:8888 -v /home/docker:/data see4c/notebook:alpha
Go to a web browser and check that the notebook is running at /[the_IP_address]:8888/ The_IP_address=localhost OR the IP address of your virtual machine obtained with 'docker-machine ip default'.
Then open README.pynb which is found in the directory data/starting_kit in your web browser. WARNING: the default notebook kernel is Python 3, you’ll have to switch to Python 2.
After running all the cells of README.ipynb, you will get a submission file called 'sample_submission*****.zip' in the directory data/, you can click on it to download and submit it to the website.
Yes, a workshop and proceedings will be organized.
Your last submission is shown automatically on the leaderboard. You cannot choose which submission to select. If you want another submission than the last one you submitted to "count" and be displayed on the leaderboard, you need to re-submit it.
This is a file that you should have in your submitted bundle to indicate to the platform which program must be executed and how.
No. If you accidentally register multiple times or have multiple accounts from members of the same team, please notify the ORGANIZERS. Teams or solo PARTICIPANTS with multiple accounts will be disqualified.
You must already have registered and joined the competition (this is achieved by going the the "Participate" tab and accepting the rules). A new "Team" tab should appears. Click on the "Team" tab. Include the information of the team, check “Allow requests”, and submit. Before others can join, the organizer of the competition will need to approve your team. The user who creates a team will be the owner/leader of the team (with management privileges). He can accept/reject requests to join the team and revoke members. Warning, you cannot join someone else's team if you have create you own team.
You cannot. If you need to destroy your team, contact us.
You join a team by requesting enrollment in a team already formed. You must already have registered and joined the competition (this is achieved by going the the "Participate" tab and accepting the rules). A new "Team" tab should appears. Click on the "Team" tab. Select the team you want to join. Click on “Request enrolment” and then submit. The leader of the team must approve your request before you are included in the team. Warning, you cannot join someone else's team if you have already created you own team. You cannot join multiple teams.
You cannot. If you need to leave a team, contact us.
ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS". THE SEE.4C CONSORTIUM, THE EUROPEAN COMMISSION AND/OR OTHER ORGANIZERS AND SPONSORS DISCLAIM ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE, AND THE WARRANTY OF NON-INFRIGEMENT OF ANY THIRD PARTY'S INTELLECTUAL PROPERTY RIGHTS. IN NO EVENT SHALL ISABELLE GUYON AND/OR OTHER ORGANIZERS BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS, MATERIALS, PUBLICATIONS, OR INFORMATION MADE AVAILABLE FOR THE CHALLENGE. In case of dispute or possible exclusion/disqualification from the competition, the PARTICIPANTS agree not to take immediate legal action against the ORGANIZERS or SPONSORS. Decisions can be appealed by submitting a letter to the EXPERT PANEL chair person, and disputes will be resolved by the EXPERT PANEL.
The EXPERT PANEL chair person is:
Hugo Jair Escalante, Computer Science Department National Institute of Astrophysics, Optics and Electronics Luis Enrique Erro num 1, Tonantzintla, 72840, Puebla, Mexico firstname.lastname@example.org
For questions of general interest, THE PARTICIPANTS should post their questions to the forum.
This challenge is brought to you by the See4C consortium. 709-754-6919.
Start: Dec. 18, 2016, midnight
Description: DEVELOPMENT: Create a predictor and submit the code to the platform.
Start: Jan. 1, 2017, midnight
Description: FINAL: Your LAST submission of the development phase is evaluated on NEW data.
May 1, 2017, midnight
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