Energy Efficiency and AI Optimization in Food Retail Stores

CAREL has published a new white paper titled "Energy efficiency in food retail stores. Regulatory and technical aspects, and AI tools." The document analyzes energy optimization strategies for the large-scale retail sector, addressing European regulatory frameworks, changing energy tariff models, and the integration of artificial intelligence in facility management.

Regulatory Frameworks and Tariff Dynamics
The publication examines the European Energy Performance of Buildings Directive (EPBD), which mandates the implementation of building automation, digitalization, and efficient control systems. The analysis includes an international perspective on energy performance standards across markets such as the United States, China, and Australia. Furthermore, the document evaluates the impact of transitioning from fixed-price energy tariffs to indexed and dynamic time-of-use models, highlighting the necessity for active consumption monitoring to manage operational overhead.

Refrigeration Optimization and AI Integration
In the food retail sector, refrigeration, HVAC, and lighting systems represent the primary sources of energy consumption. The white paper outlines technical interventions—such as asset retrofitting, advanced control logic, and automation—to optimize existing systems without compromising food safety or operational continuity. Additionally, the document explores the application of artificial intelligence in managing distributed store networks. AI algorithms process field data to facilitate plant monitoring, anomaly detection, and asset classification, while human specialists manage system fine-tuning and critical decision-making. The results of these efficiency measures are verified using the International Performance Measurement and Verification Protocol (IPMVP).

Additional Context: This section details technical specifications not included in the original announcement
In commercial building management, the International Performance Measurement and Verification Protocol (IPMVP) provides a standardized framework for quantifying the results of energy conservation measures (ECMs). Because energy savings represent the absence of energy use, they cannot be directly measured. Instead, IPMVP utilizes mathematical models to establish a baseline of historical energy consumption, adjusting for independent variables such as external weather conditions and store occupancy rates. When artificial intelligence is integrated into supermarket refrigeration systems, it often employs predictive control algorithms to optimize the defrost cycles of display cabinets and adjust the variable-speed compressors in the central refrigeration rack based on real-time grid prices and thermal load forecasts.

"Improving efficiency in food retail stores" white paper

Edited by Lekshman Ramdas, Induportals editor – adapted by AI.

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