An interactive map showing live wind farm generation in Great Britain

Hacker News

GB Renewables Map an energy experiment created entirely in my free time (day job is visualisation at Octopus Energy). It's an interactive map showing live generation for major wind farms in Great Britain, showing what each wind farm is generating both now and in the past, and where that generation is physically located. Animated weather data is from WeatherLayers and shows current and historic wind conditions on the map, providing context to wind generation around the country. History mode allows you to go back in time and see wind generation and weather conditions for a particular date and time. It's great for exploring days of record generation, such as the 21.6GW record on January 10th, 2023! Prediction mode lets you see what wind farms are estimated to be generating using current wind conditions and model based on historic generation and wind speeds. Is a wind farm generating as you expect, or is there something to look into? An experimental feature allows you to see what future wind farms could be generating today (or in the past!) if they were already built and operational. If you click the "sparkle" button on the map you'll get to see what the upcoming 3.6GW Dogger Bank wind farm is estimated to generate if it was operational today. There's an "About" section on the site that goes into detail on the various public data sources and how some of the features work. I also document a lot of this on my Twitter @robhawkes if you're curious. This is just the start and there are many more features to come! Please let me know your comments and suggestions.

Visit product View on Hacker NewsApr 26, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, context · Missing: mac, agents, macos

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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