Maximise Potential of
Your Renewable Energy Plants
Renewable energy operators know that choosing the right site for their projects is critical for long-term success. That’s why we offer powerful renewable energy site monitoring and analytics, helping operators assess the availability of essential resources and infrastructure as well as possible risks to critical assets.
Spottitt MF analytics for solar site selection identify the optimal location for a solar power plant based on a range of factors, including solar irradiance, shading, land cover, proximity to power infrastructure and more. By leveraging satellite data and advanced machine learning techniques, developers get the most up-to-date and cohesive insights on the suitable sites for solar power plants, allowing maximize energy output and ROI.
Similarly, Spottitt MF supports wind turbine operators in identifying the most favorable locations based on factors such as wind speed, turbulence, and topography. This data can be used to optimize wind farm placement and increase energy production, ultimately driving better project performance and profitability.
If you’re a renewable energy operator looking to explore the latest technology to improve your site selection process, Spottitt is here to help.
A Few of Our Renewable Energy Clients
Wide Variety of Use Cases
Unlock Condition and Risk Based O&M, Starting Today
The Easy Way
Boost your risk awareness and mitigation with actionable insights derived from multiple risk metrics measured. Enjoy your analyses in our on-demand platform and integrate them easily into your existing asset management and GIS systems for better decision making.
Accurate and Near Real-Time Results
With Spottitt, you get high-resolution satellite images and the most advanced artificial intelligence to get accurate geospatial data on every meter of your network, updated as frequently as every day. We offer the optimal solution customised to your specific needs.
At Scale, In Hours
Inspect at scale in hours, fully remote. Our monitoring solutions require no hardware, no flight permits, no time walking or driving, and then passing and standardising the data. No subjective measures prone to human errors. Also, important, our monitoring method doesn’t pollute the environment.
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Stay on Top of Recent News
Behind the Spottitt MF Curtains: Role of Machine Learning in Analyzing Satellite Imagery
Spottitt is known for utilising satellite data to offer infrastructure owners valuable insights into the diverse external risks affecting their assets. But how precisely do we derive these insights from satellite imagery? The answer is – machine learning, and today we’re inviting you behind the curtains of our product to unveil the whole process.
Spottitt Metrics Factory Advances to the Next Level
As more organizations turned to Spottitt for monitoring growing numbers of assets, we recognized that the traditional geospatial platform’s map view, which was effective for small-scale monitoring, became cumbersome when dealing with larger datasets. So, we embarked on the development of Spottitt Metrics Factory. Since its beta launch in late 2022, ten organizations from around the world have been delighting in the ability to view and interact with their asset monitoring data using the product.
Top Tips for Choosing the Right Satellite Analytics Provider for Your Needs
Satellite-derived analytics is rapidly gaining ground as an essential tool for monitoring and managing risks in the power, utility, and infrastructure industries. It has been recognised as a key tool to improve safety, reliability, and optimal performance of critical assets by providing insights into their health, risks, and other key operational parameters.
Geospatial Data for Selecting Optimal Renewable Energy Sites
Solar and wind power plants have become increasingly important as the world continues to move towards renewable energy sources. However, choosing the right location for these power plants can be a complex process involving various factors, from available land and solar irradiation to wind speeds and topography.
