The duo of AI and energy

October 16, 2025 6:21 pm

There is a significant need for power quality meters to measure power quality, especially at data centres, due to potential load spikes.

The relationship between AI and energy is complex, but there is a chance that energy tech companies can support AI development by using AI. In India, there is a significant need for power quality meters to measure power quality, especially at data centres, due to potential load spikes. Data usage is not as linear as a house, and AI models can cause data usage to be unpredictable. To prevent these spikes, power quality meters and other devices, such as STATCOMs and capacitor banks, are necessary. AI can predict demand spikes by measuring data at high frequencies, enabling early protection of devices to avoid such spikes. This creates a virtuous cycle between AI and energy companies, ensuring the quality of power and lower costs in data centres.

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• The interplay between AI and energy is intricate, with energy tech firms potentially aiding AI development through AI usage.
• In India, there is a crucial demand for power quality meters, particularly at data centers to manage load spikes caused by non-linear data usage.
• AI can forecast demand spikes by monitoring data at high frequencies, helping to protect devices early and maintain power quality, thus reducing costs in data centers.
• Kimble has installed approximately 2 crores of the 25 crores planned smart meters in India, holding a 25% market share in RDSS installations.
• The company has developed a comprehensive smart meter infrastructure, encompassing data acquisition to decision-making, and is creating new AI-enabled grid products.
• A scalable software platform utilizing AI/ML is under development, which includes data pipelines and databases for improved performance.
• Kimble is also creating a high-speed, customizable Fault Ride-Through (FRT) solution and a modular Fast Reclosing Transient Overvoltage (FRTO) system for easy substation upgrades.
• Products for non-intrusive load monitoring leverage ML to identify individual devices from a home’s main smart meter for load disaggregation and fault detection.
• AI is pivotal for the energy transition but needs to be integrated with human judgment to avoid misuse leading to inaccurate results.
• Effective AI modeling should prioritize transparency and openness, using interpretable algorithms and avoiding reliance on black boxes.
• Continuous data validation and clear understanding of AI systems’ inputs and outputs are essential for scalability.
• AI should enhance human capabilities rather than replace them, promoting collaboration between AI and energy to drive reliability and efficiency in the sector.

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