OpenPV – Photovoltaic Potential in Bavaria (and Beyond?)

Hacker News

Photovoltaic potential calculated live and privacy-friendly in your browser via WebGL. Using open LoD2 and laser point data from the Bavarian Public Agency for Digitization, High-Speed Internet and Surveying. Including laser points for tree occlusions and other non-LoD2 objects. Try it with the Bavarian Hall of Fame next to Oktoberfest: Hall of Fame, Munich, Bavaria Note that the point clouds might be a lot of data to download if enabled (~50 MB+). Currently limited to Bavaria, Germany. Thanks for your feedback!

Visit product View on Hacker NewsOct 12, 2023

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

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best fitHighest predicted score across all platforms for this description.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: using, open · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% 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

24%24% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% 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

9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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