Problem
Energy and asset management have been around for a long time and companies have adopted various methods. - Most companies still use manual tracking methods while some of them use IoT tracking systems. // Higher cost of energy and maintenance operations. - Over 70% of IoT data is never used by companies and even if they use this data, they usually run analyses on excel files with manual data transfers. // A bit better but still higher cost of energy and maintenance operations. - Some of them also benefit from AI-powered analytics. But because of the singularity of the analyses for interconnected structures, the outcome efficiency is still lower than it could be. // Better than monitoring but still not enough to solve micro grid management and grid congestion issues. For example in 2023, EU countries lost € 4,2 Billion because of grid congestions caused by non-integrated energy supply-consumption plans and forecasts.
Çözüm
- AI-powered energy supply and consumption forecasts and autonomous plans to prevent grid congestion. - Energy performance anomaly detection and autonomous saving actions to lower energy cost and carbon footprint. - Malfunction forecasts and autonomous work assignment to maximize uptime. - Field operation and contractor management to maximize operational efficiency and lower operational cost.
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