Minimising Waste in Commercial Heating Systems

Problem

Large commercial buildings often struggle with inefficient energy usage, leading to high costs and significant environmental impact. Traditional energy management systems typically rely on static schedules and setpoints, which do not account for dynamic factors such as occupancy, weather changes, and varying energy prices. This results in suboptimal pre-heating and pre-cooling strategies, causing unnecessary energy consumption and increased operational costs.

Solution

Causal models provide a detailed understanding of the factors affecting energy consumption in large commercial buildings. By accurately predicting the impact of various variables, and how they interact, CausaDB enables superior pre-heating and pre-cooling strategies. For example, it can adjust heating and cooling schedules based on real-time occupancy data, weather forecasts, and energy price fluctuations. CausaDB's API allows integration with existing building management systems to achieve seamless implementation of these optimised strategies, leading to efficient energy use and reduced costs.

Value

• Reduce energy costs through optimised pre-heating and pre-cooling strategies
• Minimise environmental impact by reducing energy waste
• Improve overall building efficiency and occupant comfort

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