- 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
2022
Publication Types:
Wind energy forecasting with missing values within a fully conditional specification framework
2022ArticleIn press/Available onlineJournal paper
International Journal of Forecasting, in press/available online
Publication year: 2022
Wind power forecasting is essential to power system operation and electricity markets. As abundant data became available thanks to the deployment of measurement infrastructures and the democratization of meteorological modeling, extensive data-driven approaches have been developed within both point and probabilistic forecasting frameworks. These models usually assume that the dataset at hand is complete and overlook missing value issues that often occur in practice. In contrast to that common approach, we rigorously consider here the wind power forecasting problem in the presence of missing values, by jointly accommodating imputation and forecasting tasks. Our approach allows inferring the joint distribution of input features and target variables at the model estimation stage based on incomplete observations only. We place emphasis on a fully conditional specification method owing to its desirable properties, e.g., being assumption-free when it comes to these joint distributions. Then, at the operational forecasting stage, with available features at hand, one can issue forecasts by implicitly imputing all missing entries. The approach is applicable to both point and probabilistic forecasting, while yielding competitive forecast quality within both simulation and real-world case studies. It confirms that by using a powerful universal imputation method based on fully conditional specification, the proposed universal imputation approach is superior to the common impute-then-predict approach, especially in the context of probabilistic forecasting.
Variable heat pricing to steer the flexibility of heat demand response in district heating systems
2022ArticleJournal paper
Electric Power Systems Research 212, art. no. 108383
Publication year: 2022
Trading data for wind power forecasting: A regression market with Lasso regularization
2022ArticleJournal paper
Electric Power Systems Research 212, art. no. 108442
Publication year: 2022
To share or not to share? Alternative views on a future of collaborative forecasting
Distributed data refers to information that flows from different sources and possibly different owners. Getting top value from distributed data requires a paradigm shift towards collaborative forecasting. Alternative frameworks exist to support collaborative forecasting, from collaborative analytics to data markets, and from analytics markets to prediction markets. While we should accept that not all data will be openly shared, rethinking forecasting processes with modern communication, distributed computation, and a market component could yield substantial improvements in forecast quality while unleashing new business models
Stochastic control and pricing for natural gas networks
2022ArticleJournal paper
IEEE Transactions on Control of Network Systems 9(1), pp. 450-462
Publication year: 2022
Reliability-aware probabilistic reserve procurement
2022ArticleJournal paper
Electric Power Systems Research 212, art. no. 108345
Publication year: 2022
Regression markets and application to energy forecasting
Energy forecasting has attracted enormous attention over the last few decades, with novel proposals related to the use of heterogeneous data sources, probabilistic forecasting, online learning, etc. A key aspect that emerged is that learning and forecasting may highly benefit from distributed data, though not only in the geographical sense. That is, various agents collect and own data that may be useful to others. In contrast to recent proposals that look into distributed and privacy-preserving learning (incentive-free), we explore here a framework called regression markets. There, agents aiming to improve their forecasts post a regression task, for which other agents may contribute by sharing their data for their features and get monetarily rewarded for it. The market design is for regression models that are linear in their parameters, and possibly separable, with estimation performed based on either batch or online learning. Both in-sample and out-of-sample aspects are considered, with markets for fitting models in-sample, and then for improving genuine forecasts out-of-sample. Such regression markets rely on recent concepts within interpretability of machine learning approaches and cooperative game theory, with Shapley additive explanations. Besides introducing the market design and proving its desirable properties, application results are shown based on simulation studies (to highlight the salient features of the proposal) and with real-world case studies.
Pandemics and forecasting: The way forward through the Taleb-Ioannidis debate
2022ArticleJournal paper
International Journal of Forecasting 38(2), pp. 410-412
Publication year: 2022
North Sea energy islands: Impact on national markets and grids
Multi-stage linear decision rules for stochastic control of natural gas networks with linepack
2022ArticleJournal paper
Electric Power Systems Research 212, art. no. 108388
Publication year: 2022
Forecasting: theory and practice
2022ArticleJournal paper
International Journal of Forecasting 38(3), pp. 705-871
Publication year: 2022
Editorial: Epidemics and forecasting with focus on COVID-19
2022ArticleJournal paper
International Journal of Forecasting 38(2), pp. 407-409
Publication year: 2022
Continuous and distribution-free probabilistic wind power forecasting: A conditional normalizing flow approach
2022ArticleJournal paper
IEEE Transactions on Sustainable Energy 13(4), pp. 2250-2263
Publication year: 2022
We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its non-affine characteristics. Over the training phase, the model sequentially maps input examples onto samples of base distribution, given the conditional contexts, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually maps samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art.
An asynchronous online negotiation mechanism for real-time peer-to-peer electricity markets
2022ArticleJournal paper
IEEE Transactions on Power Systems 37(3), pp. 1868-1880
Publication year: 2022
A network-aware market mechanism for decentralized district heating systems
A market for trading forecasts: A wagering mechanism
In many areas of industry and society, e.g., energy, healthcare, logistics, agents collect vast amounts of data that they deem proprietary. These data owners extract predictive information of varying quality and relevance from data depending on quantity, inherent information content and their own technical expertise. Aggregating these data and heterogeneous predictive skills, which are distributed in terms of ownership, can result in a higher collective value for a prediction task. In this paper, we envision a platform for improving predictions via implicit pooling of private information in return for possible remuneration. Specifically, we design a wagering-based forecast elicitation market platform, where a buyer intending to improve their forecasts posts a prediction task, and sellers respond to it with their forecast reports and wagers. This market delivers an aggregated forecast to the buyer (pre event) and allocates a payoff to the sellers (post-event) for their contribution. We propose a payoff mechanism and prove that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, we discuss the properties of the forecast aggregation operator and scoring rules to emphasise their effect on the sellers’ payoff. Finally, we provide numerical examples to illustrate the structure and properties of the proposed market platform.