Hoa Nguyen Phuong received her B.Sc. Clipping is a handy way to collect important slides you want to go back to later. His plan in the near future is working on data mining and optimization. She is going to start her master program on Computer Science this September. Now customize the name of a clipboard to store your clips. Huyen Tran Ngoc Nhat has recently graduated from FPT University in Vietnam. Thị Thanh Lâm Vũ is studying Computer Science at University of Engineering and Technology, Vietnam National University as a second-year undergraduate student. Note that, the larger the value of reducedEnergy is, the more the risk of buying energy from grid decreases. And just like those corporations, they are eager to tap into the power of their data. To do so, from the obtained solution, we will find a period p (normally in daytime) in which the battery is consecutively not discharged and (pv_i – load_i ≥ 0) for each step i in p. We then try to charge a quantity of (pv_i – load_i – reducedEnergy) for each step during the period. The company's File Number is listed as 001163634. Peter’s talk addressed how statistics, computer science, and machine learning can be applied to the challenges in the social sector. Here, pv_i and load_i are the forecasting amount of production of PV and consummation of building at step i respectively; reducedEnergy is a fixed amount of energy that is reduced equally in all steps of the period p. We will find the maximal value of reducedEnergy so that the total quantity of charged energy in the considering period is kept the same as in the optimal solution provided by Ortools and the power limit constraint is still satisfied. From the historical data, we could find the probability distribution function for the variance in the forecast of pv and load; and then consider a robust optimization problem. He […] As of this date, Scribd will manage your SlideShare account and any content you may have on SlideShare, and Scribd's General Terms of Use and Privacy Policy will apply. Special thanks to Schneider Electric for a fascinating challenge! Can Data Science Help Us Make Sense of the Mueller Report? Viewed from the demand side, as in the case of smart buildings, time-of-use tariffs incentivize consumers to use energy when it is cheapest (and most abundant). He gave the big-picture context of the data for good movement along with advice on how people from a variety of backgrounds can get involved. Why models fail to deliver value and what you can do about it. 1. The company's filing status is listed as Active and its File Number is C4081947. If your goal is to change the future, it helps to have really good predictions about what that future looks like. Drivendata, Inc. is a Massachusetts Foreign Corporation filed on March 6, 2015. He previously worked as a software engineer at Microsoft and earned a bachelor’s in philosophy from Yale University. You can change your ad preferences anytime. Looks like you’ve clipped this slide to already. Văn Phú Hoàng is a second-year undergraduate student at University of Engineering and Technology, Vietnam National University. Bull holds a master's in Computational Science and Engineering from Harvard’s School of Engineering and Applied Sciences. A complete account of the approach is available in a presentation about the methods in the competition repo. Data for Good Meetup Her main interests are optimization methods applied in transportation and electric production. The binary search is used to compute reducedEnergy. If you wish to opt out, please close your SlideShare account. The talk has two parts: the first is the big-picture context of the data for good movement, how to get involved. Please join the Data for Good meetup group to stay informed about upcoming talks and contact us if you are interested in giving a talk. The second is a new kind of competition where contributing pull requests to an open source project earns competitors points toward a $100k prize pool. Over 40 data do-gooders came to see Peter Bull of DrivenData deliver a wonderful talk titled Crowdsourcing Data for Good. The second is an in-depth case study of the methods which won DrivenData’s recent machine learning competitions. See our User Agreement and Privacy Policy. He decided to follow optimization and data mining after working on the project "Power Laws: Optimizing Demand-side Strategies" of ORLab organized by DrivenData. It might come from generation options, from energy storage or from energy demand. Themes and Conferences per Pacoid, Episode 3, Growing Data Scientists Into Manager Roles, Domino 3.0: New Features and User Experiences to Help the World Run on Models, Themes and Conferences per Pacoid, Episode 2, Item Response Theory in R for Survey Analysis, Benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST, Themes and Conferences per Pacoid, Episode 1, Make Machine Learning Interpretability More Rigorous, Learn from the Reproducibility Crisis in Science, Feature Engineering: A Framework and Techniques, The Past/Present/Future + Myths of Data Science, Classify all the Things (with Multiple Labels), On the Importance of Community-Led Open Source, Model Management and the Era of the Model-Driven Business, Put Models at the Core of Business Processes, On Ingesting Kate Crawford’s “The Trouble with Bias”, Data Science is more than Machine Learning. Crowdsourcing Data for Good: Lessons from the social … His research interests include combinatorial optimization problems such as packing, scheduling and routing. He also talked about two new DrivenData competitions. Check out, please ⇒ www.WritePaper.info ⇐, Customer Code: Creating a Company Customers Love, Be A Great Product Leader (Amplify, Oct 2019), Trillion Dollar Coach Book (Bill Campbell). Flexibility can be produced in different manners. Programmer? It was a very interesting challenge and the only thing that I regret is that I didn't have time to try on the other challenges, because they also seemed quite interesting. Learn more. The importance of structure, coding style, and refactoring in notebooks, Domino Paves the Way for the Future of Enterprise Data Science with Latest Release, Evaluating Ray: Distributed Python for Massive Scalability, Evaluating Generative Adversarial Networks (GANs), Data Drift Detection for Image Classifiers, Announcement: Domino is fully Kubernetes native, Model Interpretability: The Conversation Continues, Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA, On Being Model-driven: Metrics and Monitoring, Themes and Conferences per Pacoid, Episode 13, Exploring US Real Estate Values with Python, Natural Language in Python using spaCy: An Introduction, HyperOpt: Bayesian Hyperparameter Optimization, Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines, Deep Learning Illustrated: Building Natural Language Processing Models, Themes and Conferences per Pacoid, Episode 12, Data Ethics: Contesting Truth and Rearranging Power, Seeking Reproducibility within Social Science: Search and Discovery, A Practitioner’s Guide to Deep Learning with Ludwig, Themes and Conferences per Pacoid, Episode 11, Announcing Trial and Domino 3.5: Control Center for Data Science Leaders, Themes and Conferences per Pacoid, Episode 10, Machine Learning Product Management: Lessons Learned, Announcing Domino 3.4: Furthering Collaboration with Activity Feed, Themes and Conferences per Pacoid, Episode 9.