- About me
- Publications
- Ren. in El. Markets
- Module 1 – Fundamentals of electricity markets
- Module 2 – Electricity spot markets (e.g., day-ahead)
- Module 3 – Intra-day and balancing markets
- Module 4 – Ancillary services
- Module 5 – Impact of renewables on electricity markets
- Module 6 – Participation of renewables in electricity markets
- Module 7 – Introduction to renewable energy analytics
- Module 8 – Verification of renewable energy forecasts
- Module 9 – Renewable energy forecasting – First steps
- Module 10 – Renewable energy forecasting – Advanced topics
- Additional industry interviews
- Interviews with former students
- Exercises
- Int. J. Forecasting
- Contact
- Download CV
Pierre Pinson
Chair of Data-centric Design Engineering - Imperial College London
2025
Publication Types:
Privacy-aware data acquisition under data similarity in regression markets
2025ArticleIn press/Available onlineJournal paper
IEEE Transactions on Neural Networks and Learning Systems
Publication year: 2025
Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences as well as data similarity among data owners. Related works have often overlooked how data similarity impacts pricing and data value through statistical information leakage. We demonstrate that data similarity and privacy preferences are integral to market design and propose a query-response protocol using local differential privacy for a two-party data acquisition mechanism. In our regression data market model, we analyze strategic interactions between privacy-aware owners and the learner as a Stackelberg game over the asked price and privacy factor. Finally, we numerically evaluate how data similarity affects market participation and traded data value.
Data are missing again -- Reconstruction of power generation data using k-Nearest Neighbors and spectral graph theory
The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing nearest-neighbour imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only, or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm layout information.