Solar energy management using Artificial Intelligence and Data Science.

AI for a solar powered future

We find all solar PV systems and forecast their output

Our Ecosystem of partners, programs, and clients

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Using AI to distribute resources evenly

Increasing visibility of solar generation through cloud movement forecasting


State-of-the-art insights for advanced solar energy management

Identification and forecasting for individual sites or entire regions

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Find the precise locations of all solar PV

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Understand your customers

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Be ready for the unexpected

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Solar Identification

Identifying distributed solar PV systems

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Determine the exact sizes, locations, and orientations

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Understand and predict uptake

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Differentiate between different asset types

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Solstice AI uses artificial intelligence and data science to forecast solar PV to manage energy distribution within a region
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Solar Generation Forecasting

Forecasting solar energy generation

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Predict cloud formation and movement using AI

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Obtain forecasts specific to a site, network region, or postcode

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Prepare for major shortfalls

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Custom solutions for your solar energy management needs

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Extract unique and actionable insights from large datasets

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Leverage the latest machine learning and AI techniques

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Benefit from a highly experienced, PhD-qualified team

Solstice AI provides consulting solutions for clients using Solstice AI's expertise in data science for solar and renewable energy

Get in touch

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Our Team

Meet the founding team

A long track record of extensive industry and research experience

Julian de Hoog
Co-Founder & CEO

Julian de Hoog completed his PhD in Computer Science at the University of Oxford, and a postdoc at the University of Melbourne studying the impact of electric vehicles on distribution networks. Most recently he spent 6 years as a senior research scientist at IBM Research, where he worked on renewable energy forecasting and optimal control of energy storage.

Peter Ilfrich
Co-Founder & CTO

Peter Ilfrich is an experienced full-stack software engineer and architect with a German Diploma in computer science. He worked in multiple domains (e-commerce, banking, healthcare, energy) and is familiar with a broad spectrum of technologies, computing infrastructure and methodologies. He previously worked as senior software engineer for IBM Research.

Maneesha Perera
Co-Founder & Head of Research

Maneesha Perera is a PhD candidate at the University of Melbourne. Her research focuses on improving distributed solar power forecasting using data driven techniques with a particular focus on Deep Learning methods. She previously worked as a part-time research intern at IBM Research Australia. Before starting her PhD, she also worked as a full stack software engineer at Sysco Systems.

Valentin Muenzel

Valentin Muenzel is Co-Founder of Relectrify, recently named the Asia Pacific Company of the Year in the 2022 Global Cleantech100.


Questions and answers

Everything you need to know about Solstice AI and what we do

Can you use satellite images to detect solar?

The best available satellite imagery comes at resolutions of 30-50cm per pixel, which makes it very difficult to detect smaller systems. The best results are achieved with aerial imagery, recorded through flyovers of smaller planes and drones, at a resolution (GSD) of 10-15cm per pixel.

This depends highly on the availability of local aerial imagery. There are a few global providers of data, which focus on the western world, including Europe, North America and Australia/New Zealand. Some countries and regions have their own local data providers, but this generally has to be checked on a case-by-case basis. If data with sufficient resolution is available, we can procure it and analyse it.

This depends highly on the resolution and quality of the aerial imagery. For the highest quality imagery, our successful detection rate is 97% with 3% false positives (things identified as solar that aren’t solar). 

Questions and answers

Everything you need to know about Solstice AI and what we do