Solar intelligence to optimise your operations
Accurate insights and forecasts for solar farms, virtual power plants, and energy traders
Our Ecosystem of partners, programs, and clients
Our Ecosystem of partners, programs, and clients
<span data-metadata=""><span data-buffer="">Using AI to distribute resources evenly
Increasing visibility of solar generation through cloud movement forecasting
Solutions
State-of-the-art insights for advanced solar energy management
Improving profitability through AI-enhanced satellite-based solar forecasts
Profitability
Optimally bid into energy markets and avoid expensive penalties or grid firming services
Visibility
Understand the geospatial distribution and performance of your solar PV customer base
Control
Anticipate major supply fluctuations and prepare for price spikes with confidence
Solar Energy Forecasting
Increase bidding revenue in the spot market
Avoid under-supply penalties
Reduce spend on third-party firming capacity
Optimise energy storage dispatch
Anticipate periods of curtailment
Rooftop Solar PV Insights
Accurately forecast the output of a site, network region, or postcode
Quantify the impact of solar curtailment
Identify and diagnose poor performance of individual systems
Anticipate and prepare for major cloud cover events
Consulting
Custom solutions for your solar energy management needs
Extract unique and actionable insights from large datasets
Leverage the latest machine learning and AI techniques
Benefit from a highly experienced, PhD-qualified team
Our Team
Meet the founding team
A long track record of extensive industry and research experience
Dr. 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. Prior to founding Solstice AI, he spent six years as a senior research scientist at IBM Research, where he worked on renewable energy forecasting and optimal control of energy storage.
Peter Ilfrich is an experienced full-stack software engineer and architect with a German Diploma in computer science. He has 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.
Dr. Maneesha Perera completed her PhD at the University of Melbourne in 2023. Her research focused on improving solar power forecasting using artificial intelligence. She has also held prior roles at IBM Research and as a full stack software engineer at Sysco Systems.
Valentin Muenzel is Co-Founder and ex-CEO of Relectrify, a world leader in battery control, backed by leading global investors incl global utility EDP and Toyota Ventures, and Asia Pacific Company of the Year in the Global Cleantech100.
FAQ
Questions and Answers
Everything you need to know about Solstice AI and what we do
How do your solar energy forecasts help solar farm operators?
If you’re running a solar farm, you likely need to bid some percentage of your farm’s generation capacity into an energy market. More accurate short-term forecasts enable you to bid more accurately, ensuring that none of your energy is wasted, and eliminating any penalties for under-supply (such as FPP in Australia). We also help you to avoid having to procure expensive grid firming services, and if you have a co-located battery, our forecasts will enable you to maximise your system’s profitability.
What’s different about your forecasts?
Using a combination of artificial intelligence and cloud motion vectors, we examine real-time satellite images and forecast how clouds will form and move in the next 2-3 hours. Each artificial intelligence model is trained specifically on the region of interest. This is important, since local factors such as coastlines, hills, or mountains can affect cloud formation. The resulting solar generation forecasts are more accurate than global irradiation models used by most solar forecast providers.
Since we rely on satellite imagery and weather data, there is no need for costly installation and maintenance of any hardware. We can deploy our forecasts quickly, in almost any global location.
Our forecasts have a higher resolution, both geospatially and temporally, than any other forecast provider that we know of.
For longer horizons, such as 24h ahead, we create an optimal blend with weather-based models.
How do you help virtual power plants?
Virtual power plants (VPPs) usually consist of fleets of rooftop solar PV and energy storage systems. We can provide forecasts to VPP operators that are site-specific, postcode-specific, or region-specific, depending on the operator’s needs. We can learn the unique profile of every individual system using only a small amount of historical data. This enables us to provide valuable system diagnosis information, or alerts when a system is underperforming. We can also determine how much generation is lost due to curtailment, enabling VPP operators to accurately assess the profitability of different control strategies.
How do you help energy traders?
Solar energy is becoming a significant part of the energy generation mix in many global regions (and continues to be the fastest growing form of electricity generation). As a result, solar volatility is having an increasing impact on energy market dynamics. On the one hand, large volumes of solar generation in the middle of the day can lead to extended negative price intervals. On the other hand, sudden cloud events passing over solar-dense regions can lead to sudden, massive supply shortfalls, resulting in price spikes.
We can provide solar generation forecasts of all solar PV – both utility-scale and rooftop – across extended regions – helping energy traders to reduce their risk and maximise trading profit.
What kind of consulting do you do?
We have an extensive background in providing state-of-the-art solutions to the energy sector.
Every region is different and may have different needs and incentives when it comes to analysing and understanding solar generation or other large sources of data (such as smart meter data).
Our PhD-qualified team can assist with applying state-of-the-art machine learning and artificial intelligence methods to industry problems to help our clients reduce their costs and increase revenue.