Solar Radiation

• Gather real-time data from solar radiation sensors to assess heat gain due to sunlight. This directly impacts the cooling load, especially in buildings with significant glass exposure.

Human Occupancy

• Install occupancy sensors in key areas to measure the number of people in different spaces. Occupancy levels directly affect the cooling demand due to body heat and CO₂ levels, which can drive ventilation needs.

Thermal Load of Production Equipment

• Monitor equipment heat output via sensors installed on or near production machinery. Production equipment often generates significant heat, affecting both the cooling load and air quality.

Building Management System (BMS)

• Integrate all sensor inputs into a centralized BMS or a dedicated controller for your HVAC system. This system will continuously monitor the environment and operational conditions in real-time.

Data Analytics and Machine Learning

• Use data analytics and machine learning algorithms to predict the future cooling load based on historical data, environmental conditions (solar radiation), human occupancy patterns, and the heat generated by production equipment. The algorithm learns from previous trends to anticipate peaks in cooling demand.

Chiller System

• Adjust the chiller’s temperature set point based on the predicted cooling load. When high solar radiation or occupancy is detected, lower the set point to handle the increased load in advance. During low-demand periods (e.g., nighttime or weekends), raise the set point to save energy.
• Integrate variable frequency drives (VFDs) into the chiller system, which can modulate the chiller’s capacity based on actual demand, improving efficiency by reducing unnecessary energy consumption during low-demand periods.

AHUs (Air Handling Units)

• Use the collected data to dynamically adjust the supply air temperature set point. For example, if solar radiation or occupancy is high, the AHU would supply cooler air, while during low-demand periods, it could reduce the cooling effort, improving energy efficiency.
• Implement demand control ventilation (DCV), which modulates ventilation rates based on real-time CO₂ levels and occupancy data, ensuring that only the required amount of fresh air is introduced, further reducing unnecessary cooling and fan energy use.

VFD Control

• Use VFDs to adjust the fan speeds in the AHUs, chillers, and cooling towers based on real-time demand. VFDs save energy by allowing HVAC equipment to operate at variable speeds instead of running at full capacity continuously. For instance:
• When human occupancy and thermal loads are low, reduce the speed of fans, pumps, and compressors.
• Increase the speed when the predicted demand rises, driven by solar radiation, equipment heat load, or occupancy.

• Based on real-time and historical data, the system can predict cooling demand for the next few hours or even days. This allows the HVAC system to operate more efficiently by pre-cooling spaces when electricity is cheaper (demand response strategies) or gradually ramping up equipment instead of rapid starts, which consume more energy.
• Predictive control also helps to prevent system over-sizing or under-sizing by anticipating the exact cooling load needed, preventing unnecessary energy consumption during peak hours.

CO₂ and Demand-Controlled Ventilation

• Human occupancy sensors and CO₂ data allow the system to precisely adjust the amount of fresh air intake, ensuring ventilation is provided only when needed. This reduces the cooling load by limiting the amount of outside air that needs to be cooled.

Thermal Load Balance

• By dynamically adjusting the AHUs and chiller based on real-time equipment heat output, the system ensures that only the necessary cooling power is used, avoiding overcooling or excess ventilation.

Estimated Savings

• With such a predictive and adaptive system, energy savings can be significant:
• • Up to 30% savings can be achieved by combining demand-based control ventilation (DCV), VFDs, and smart set point adjustments based on real-time data.
  • Peak demand charges can be reduced, as the system better matches cooling capacity with actual needs, preventing overuse of chillers during low-demand periods.

• Continuously monitor the performance of the system and collect operational data. The predictive algorithm can be fine-tuned over time to further improve efficiency.
• Incorporate performance reporting, such as monthly energy savings, to track efficiency gains and justify further investment in automation technology.

By using solar radiation, human occupancy, and thermal load data in an integrated, predictive manner, your chiller and AHU systems can adjust to the specific cooling requirements at any given time, significantly improving energy efficiency while maintaining optimal comfort levels in your facility.