PVBenchmark – UserBenchmark for PV Systems

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Hi HN, I’d like to share a side project I’ve been working on: https://pvbenchmark.de/ . In Germany, small plug-and-play photovoltaic systems, often referred to as “balcony PV systems,” have become increasingly popular. These systems are designed for easy installation on balconies or rooftops, allowing homeowners and renters to feed a small amount of renewable energy back into the grid while reducing their electricity bills. However, one challenge for many users is assessing whether their system is performing well or if there’s room for optimization. Inspired by online communities where users share performance data, I built PVBenchmark, a platform to help small PV system owners benchmark their energy production. Here’s how it works: Upload: Share a screenshot from your inverter app and input basic system data (ZIP code, peak power, orientation, tilt). Analysis: The tool extracts the data, matches it with local weather and site conditions, and calculates the theoretical potential for your setup. Comparison: See how your daily, monthly, or yearly energy generation stacks up against similar systems from other users. Right now, the site is in German (sorry!), as it’s primarily targeting German users who own these small systems. If there’s interest, I’d be happy to work on making it more accessible to a broader audience. I’d love for the HN community to check it out and share any feedback. It’s an early version, and I’m eager to improve it.

Visit product View on Hacker NewsNov 16, 2024

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

62%62% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, monthly, users · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

13%13% 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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