The renewable energy cannot be tapped fully without storage and without a huge and massive break-through in our ability to link it with transmission and distribution and make massive improvements. Energsoft is focused on dramatically increasing ambition to tackle the climate crisis by a transition to 100% renewable with software and artificial intelligence for battery and energy storage. If we will keep pushing there is no reason why we cannot solve this problem.
Inspired by Leonardo DiCaprio
We are now experiencing what can only be called a planetary crisis — a convergence of accelerating climate change, unprecedented loss of biodiversity, and increasing human health issues caused by a toxic environment. Since 1970, we have lost one-third of the world’s wildlands, and in that time 50% of all vertebrate land animals have vanished. One-third of the world’s coral reefs, the “nurseries” of the ocean, have died and another third are expected to perish by 2030.
In 2020 the world will generate 50 times the amount of data as in 2011 and 75 times the number of information sources (IDC, 2011). Within these data are huge opportunities for human advancement. But to turn opportunities into reality, people need the power of data at their fingertips.
Recent estimates indicate that we need 10 times the level of environmental funding to fund projects that help stabilize ecosystems, giving ourselves the best chance of survival as the world gets hotter and climate impacts become more severe. We believe that software and analytics can improve research and development for batteries. Battery management systems can make batteries smarter and more intelligent. The only problem is to do incremental changes and to track our progress with data and smart tools. EnergSoft helps people see and understand data.
Energsoft products are transforming the way people use data to solve problems. We make analyzing data fast and easy, beautiful and useful.
Software as a service analytics platform that ensures performance, predictability and reliability for every battery-powered systems. We built a software that can collect current and voltage data, visualizations for it and statistical inference. When we have enough data to tackle it with deep learning we can change the world. It’s not enough to be on a mission that matters. You must have the ideas and technologies to match. We believe helping people to see and understand renewable energy data is one of the most important missions of the 21st century.
Making electricity is responsible for only 25% of all greenhouse gas emissions each year. So even if we could generate all the electricity we need without emitting a single molecule of greenhouse gases (which we’re a long way from doing), we would cut total emissions by just a quarter. They’re not wrong. Renewables are getting cheaper and many countries are committing to rely more on them and less on fossil fuels for their electricity needs.
How do we know that the global surface temperature rose between 0.6 and 0.9 degrees between 1906 and 2005? How do we know the rate of temperature increase has nearly doubled in the last 50 years?
The main types of questions that arise from examining time series can vary. They depend on the environmental context, but also on the data that have been gathered.
Where we describe the main features of the time series, such as is the series increasing or decreasing, or are there any seasonal patterns? (e.g. higher temperatures in summer and lower in winter). Where we predict future values of the time series from the current values, and also quantify the uncertainty in these predictions. where we detect when changes in the behaviour of the time series have occurred, such as sudden drops in precipitation, or in pollution levels.
Start off by looking at data collected at Manua Loa; one of the five volcanoes that form the island of Hawaii. The observatory near the summit of the Mauna Loa volcano has been recording the amount of carbon dioxide in the air since 1958. This is one of the longest continuous recordings of direct measurements of CO2 and shows an increasing trend from year to year.
The basic formula to figure out time series predictions:
Time series (Xt) = Trend (mt) + Sesonal component (st) + Unexplained error (ϵt)
1. Decrease battery test cycle times by identifying test issues as soon as they occur.
2. Increase battery reliability and safety by identifying manufacturing issues with real-time.
3. Meet all design specifications with less burden by streamlining internal and external reporting.
4. Secured and highly efficient battery management systems by improving validation at the cell level.
5. Perform device linking to maximize system performance by monitoring the health of battery assets in real time.
6. Determine battery degradation rates, in-use trends and performance to warranty.
7. Accelerate testing of new materials, chemistries and manufacturing processes.
8. Analyze data from different cycling and impedance machines from multiple locations.
9. Perform pre-production tuning and/or commissioning.
10. Reduce need to overbuild energy storage.
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