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Gathering data from various sources, including historical energy prices, production and demand, historical battery temperature, current and voltage and battery technical parameters.
Utilizing advanced Machine Learning models to develop predictive algorithms to forecast State of Charge (SoC), State of Health (SoH), Depth of Discharge (DoD). Our models continuously learn and adapt, providing accurate optimization recommendations.
Crafting customized optimization strategies for your company, we prioritize predictive maintenance and charging/discharging strategies. This approach aims to minimize energy waste and enhance the lifetime of the battery, contributing to both efficiency and longevity in energy storage.
Collaborating closely with energy companies, we seamlessly integrate these strategies into their existing infrastructure. Simultaneously, we continually gather new data to ensure ongoing accuracy and improvement of algorithms. This iterative process enhances the adaptability and effectiveness of our strategies over time.