Building equipment with predictive control
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Solution Overview
Problem
Central energy facilities (CEFs) face challenges in minimizing power consumption and reducing energy costs due to the high power consumption of HVAC equipment like chillers, boilers, cooling towers, and pumps, which are not efficiently managed by existing technologies.
Innovation Solution
Implementation of a predictive control system that includes a battery unit and a predictive controller to optimize energy usage by determining the optimal amount of energy to purchase from the grid and store or discharge from the battery unit, considering energy pricing data and demand charges, to power HVAC components such as chillers, cooling towers, pumps, and valves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing technologies are used to manage HVAC equipment, then equipment operation is maintained, but power consumption is not minimized and energy costs are not reduced
Solution Approach 1:
The predictive control system performs preliminary actions by forecasting future energy prices and equipment performance, then proactively scheduling HVAC operations to execute during periods of lower energy costs. The system predicts optimal operating windows in advance and pre-cools or pre-heats spaces before peak pricing periods occur, thereby reducing overall energy costs without requiring complex real-time adjustments during high-price periods.
Solution Approach 2:
The control system dynamically adjusts HVAC equipment operation based on real-time energy pricing data, weather forecasts, and predicted equipment performance. Instead of static scheduling, the system continuously adapts operating parameters such as temperature setpoints, equipment runtime, and load distribution to respond to changing energy prices and environmental conditions, optimizing energy cost reduction while managing system complexity through adaptive algorithms.
2Reliability
If HVAC equipment operates continuously to meet building needs, then building comfort is maintained, but peak power draw increases and demand charges increase
Solution Approach 1:
The system performs preliminary cooling or heating of building spaces before anticipated peak demand periods or high pricing windows. By pre-conditioning spaces during off-peak hours when power draw is lower, the system reduces the need for continuous high-power operation during peak periods, thereby maintaining building comfort while reducing peak power draw and associated demand charges.
Solution Approach 2:
The predictive control system incorporates feedback loops that continuously monitor actual energy consumption, compare it against predictions, and adjust future scheduling decisions. The system uses real-time data on power draw, equipment performance, and environmental conditions to refine its predictions and optimize the balance between maintaining building comfort and reducing peak power demand through adaptive control strategies.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The predictive control system effectively reduces energy costs by optimizing energy usage based on real-time pricing and demand, thereby minimizing peak power draw and demand charges, leading to more efficient and cost-effective operation of HVAC equipment.
Implementation Method 1
The battery unit is configured to store electric energy from an energy grid and discharge the stored electric energy for use in powering the powered CEF components
Implementation Method 2
The CEF includes one or more photovoltaic panels configured to collect photovoltaic energy
Data Source
AI summary
A central energy facility (CEF) includes a plurality of powered CEF components, a battery unit, and a predictive CEF controller. The powered CEF components include a chiller unit and a cooling tower. The battery unit is configured to store electric energy from an energy grid and discharge the stored electric energy for use in powering the powered CEF components. The predictive CEF controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from the energy grid and an optimal amount of electric energy to store in the battery unit or discharge from the battery unit for use in powering the powered CEF components at each time step of an optimization period.