mathematics

Pierre Pinson

Pierre Pinson is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Pierre Pinson rather than just read about it. In short: Pierre Pinson (born 28 March 1980) is a French applied mathematician known for his work on forecasting, optimisation and management science for energy systems, e.g., including probabilistic forecasting, participation of renewable energy generation in electricity markets, market-based coordination of energy systems, peer-to-peer energy markets, as well as data markets. He is a professor at the Technical University of…

Key takeaways

Pierre Pinson belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Pierre Pinson to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Pierre Pinson from memory before moving on to harder problems.

Reference excerpt

Pierre Pinson (born 28 March 1980) is a French applied mathematician known for his work on forecasting, optimisation and management science for energy systems, e.g., including probabilistic forecasting, participation of renewable energy generation in electricity markets, market-based coordination of energy systems, peer-to-peer energy markets, as well as data markets. He is a professor at the Technical University of Denmark and has been Editor-in-Chief of the International Journal of Forecasting from 2019 onwards. Pierre Pinson grew up near Poitiers, France and moved to Toulouse at the age of 17. He studied applied mathematics at the Institut National des Sciences Appliquées, graduating with an MSc in applied mathematics in 2002. He then moved to Sophia Antipolis, France to complete a PhD with the Ecole des Mines de Paris in 2006, with a doctoral dissertation, "Pinson P. Estimation of the uncertainty in wind power forecasting" (Doctoral dissertation, École Nationale Supérieure des Mines de Paris).[2] That year, he moved to Denmark to start with the Technical University of Denmark. After a period with the Department of Applied Mathematics and Computer Sciences, he was a professor with the Department of Electrical Engineering between 2013 and 2020. Since 2021, is a Professor of Operations Research with the Department of Technology, Management and Economics at the same university. Previously, he was a visiting researcher at the University of Oxford Mathematical Institute and the University of Washington, a scientist at the European Centre for Medium-range Weather Forecasts (ECMWF, UK), a visiting professor at Ecole Normale Supérieure (Rennes, France), as well as a Simons Fellow at the Isaac Newton Institute for Mathematical Sciences (Cambridge, UK).

Most cited publications Pinson P, Chevallier C, Kariniotakis GN. Trading wind generation from short-term probabilistic forecasts of wind power. IEEE Transactions on Power Systems. 2007 Jul 30;22(3):1148-56. According to Google Scholar it has been cited 631 times. Wan C, Xu Z, Pinson P, Dong ZY, Wong KP. Probabilistic forecasting of wind power generation using extreme learning machine.IEEE Transactions on Power Systems'. 2013 Nov 14;29(3):1033-44. According to Google Scholar, this article has been cited 521 times Pinson P, Madsen H, Nielsen HA, Papaefthymiou G, Klöckl B. From probabilistic forecasts to statistical scenarios of short‐term wind power production. Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology. 2009 Jan;12(1):51-62. According to Google Scholar, this article has been cited 490 times He G, Chen Q, Kang C, Pinson P, Xia Q. Optimal bidding strategy of battery storage in power markets considering performance-based regulation and battery cycle life. IEEE Transactions on Smart Grid. 2015 May 13;7(5):2359-67. According to Google Scholar, this article has been cited 284 times

References

External links Professor Pierre Pinson Personal website Personal page at the Technical University of Denmark Google Scholar

Illustrations

Worked examples

Example 1 — a first encounter with Pierre Pinson

Start with the simplest possible case. Write down what Pierre Pinson claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Pierre Pinson before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Pierre Pinson ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Pierre Pinson

In research: Pierre Pinson appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Pierre Pinson in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Pierre Pinson is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1980 births, 21st-century French mathematicians, Academic staff of the Technical University of Denmark, so understanding it makes those chapters shorter.
In everyday life: Look for Pierre Pinson outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Pierre Pinson” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Pierre Pinson in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Pierre Pinson means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Pierre Pinson out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Pierre Pinson in simple terms?

Why does Pierre Pinson matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Pierre Pinson?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Pierre Pinson.