Model Predictive Control of an HVAC System Based on Dynamic Tracking and Optimization of Energy Use

2015 ◽  
Author(s):  
Alfredo Díaz Jácome ◽  
Marco E. Sanjuán ◽  
Victor Fontalvo Morales ◽  
Cinthia Audivet Durán
Keyword(s):  
Model Predictive Control ◽  
Performance Management ◽  
Predictive Control ◽  
Energy Use ◽  
Energy Performance ◽  
Department Of Energy ◽  
Commercial Building ◽  
Air Supply ◽  
Energetic Analysis ◽  
Cooling Coil

U.S. Department of Energy affirms that HVAC systems consume approximately 40% of the total energy used in commercial-building sector. These types of systems are complex because they are composed of a large number of interconnected subsystems. The analysis shown in this paper is established on a building geographically located at the Caribbean coast region of Colombia in a region with tropical savanna climate and it is exposed to constant thermal load changes associated to high wall temperatures and direct sunlight incidence. Under this perspective, an energetic analysis is performed for the HVAC in order to implement a Model Predictive Control (MPC) strategy to enhance the system efficiency under the previously mentioned external conditions. The model predictive strategy is implemented as a system supervisor in order to minimize a cost function that measures the ratio of water consumption to air temperature change in the cooling coil. The strategy manipulates the required temperature of supply water to cooling coil from the chiller, perceiving as input perturbation the outdoor temperature, the desired temperatures for the classrooms and the desired temperature of the air supply to the different zones. The comparison and selection of thermodynamical states for analysis are conducted according to the dynamic characteristics of the entire system and individual components, and the energy assessment is performed including the system transient response. The accomplishment of the supervisory control strategy has demonstrated that dynamic energetic analysis and assessment is an auxiliary tool for HVAC performance management. The analysis performed shows that the supervisory strategy can reduce properly the energy performance index as a consequence, the energy consumption of the fan has a reduction of a 0.65%, while the water required shows a reduction of 66.93%.

scholarly journals A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3608
Author(s):  
Yang Yuan ◽  
Neng Zhu ◽  
Haizhu Zhou ◽  
Hai Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Water System ◽  
Energy Performance ◽  
Effective Control ◽  
New Model ◽  
Chilled Water ◽  
Chilled Water System ◽  
Hydraulic Oscillation

To enhance the energy performance of a central air-conditioning system, an effective control method for the chilled water system is always essential. However, it is a real challenge to distribute exact cooling energy to multiple terminal units in different floors via a complex chilled water network. To mitigate hydraulic imbalance in a complex chilled water system, many throttle valves and variable-speed pumps are installed, which are usually regulated by PID-based controllers. Due to the severe hydraulic coupling among the valves and pumps, the hydraulic oscillation phenomena often occur while using those feedback-based controllers. Based on a data-calibrated water distribution model which can accurately predict the hydraulic behaviors of a chilled water system, a new Model Predictive Control (MPC) method is proposed in this study. The proposed method is validated by a real-life chilled water system in a 22-floor hotel. By the proposed method, the valves and pumps can be regulated safely without any hydraulic oscillations. Simultaneously, the hydraulic imbalance among different floors is also eliminated, which can save 23.3% electricity consumption of the pumps.

Model Predictive Control of a Distributed-Parameter Process Employing a Moving Radiant Actuator

2010 ◽  
Author(s):  
Fan Zeng ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Process Model ◽  
Energy Use ◽  
State Parameter ◽  
Source Model ◽  
Distributed Parameter ◽  
Optimization Approach ◽  
Radiant Intensity ◽  
Process Energy

Many industrial processes employ radiation-based actuators with two or more manipulated variables. Moving radiant actuators, in particular, act on a distributed parameter process where the velocity of the actuator is an additional manipulated variable with its own constraints. In this paper, a model predictive control (MPC) scheme is developed for a distributed-parameter process employing such a moving radiant actuator. The designed MPC controller uses an online optimization approach to determine both the radiant intensity and velocity of the moving actuator based on a linearized process model and a distributed state/parameter estimator. A particular source-model reduction that enables the approach is outlined. The proposed strategy is then demonstrated for a radiative curing process considering different control scenarios with the objective of achieving desired cure level uniformity and minimizing process energy use.

scholarly journals Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 34 ◽  
Author(s):  
Germán Ramos Ruiz ◽  
Eva Lucas Segarra ◽  
Carlos Fernández Bandera
Keyword(s):  
Genetic Algorithm ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Search Space ◽  
Building Energy ◽  
Energy Performance ◽  
Hvac Systems ◽  
Convergence Time ◽  
Computational Time ◽  
Nsga Ii

There is growing concern about how to mitigate climate change in which the reduction of CO2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution.

scholarly journals The New Market Transformation Needed for Commercial Building Energy Efficiency

2016 ◽  
Vol 9 (1) ◽  
pp. 229
Author(s):  
Valerie Patrick ◽  
Leslie A. Billhymer ◽  
William Shephard
Keyword(s):  
Energy Efficiency ◽  
Energy Use ◽  
Building Energy ◽  
Department Of Energy ◽  
Building Energy Efficiency ◽  
Commercial Building ◽  
Innovation Cluster ◽  
Stakeholder Perspectives ◽  
Regional Market ◽  
Market Transformation

The U.S. Department of Energy [DOE] established the Consortium for Building Energy Innovation [CBEI] to address commercial building energy efficiency as an innovation cluster, where the regional market context (Note 1) guides the research agenda for market transformation (Porter, 2001). CBEI develops content to support Advanced Energy Retrofits (AERs), a retrofit which results in 50% or greater reduction in building energy use, in small- and medium- sized commercial buildings (less than 250 000 ft<sup>2</sup>). The challenge is collecting input for a market with many stakeholders so that a strategy emerges to implement AERs. This research applies systems and complexity theories to develop a strategy to promote the emergence of AERs in this market incorporating multiple stakeholder perspectives (Note 2).

Establishing Standard Source Energy and Emission Factors for Energy Use in Buildings

2007 ◽  
Author(s):  
Michael Deru
Keyword(s):  
Life Cycle ◽  
Energy Use ◽  
Performance Metrics ◽  
Energy Performance ◽  
Emission Factors ◽  
Department Of Energy ◽  
Energy Information Administration ◽  
Site Energy ◽  
Full Life Cycle ◽  
Lci Data

Energy use in buildings is most commonly analyzed by using the energy measured at the site. Some analysts also calculate the source energy and emissions from the site energy. Source energy use and emission profiles offer better indicators of the environmental impact of buildings and allow other metrics for comparison of performance. However, there are no standard factors for calculating the source energy and emissions from the site energy. The energy and emission factors used are derived from different data using different methods resulting in wide variations, which makes comparisons difficult. In addition, these factors do not include the full life cycle of the fuels and energies, but only the combustion and transmission portions of the life cycle. The recently available U.S. Life Cycle Inventory (LCI) Database provides LCI data for energy, transportation, and common materials. The LCI data for fuels include all the energy and emissions associated with the extraction, transportation, and processing of the fuels. This paper describes how the LCI data, along with other emissions data and energy consumption data from the Energy Information Administration, were used to generate source energy and emission factors specifically for energy use in buildings. The factors are provided on national, interconnect, and state levels. This effort was part of the U.S. Department of Energy Performance Metrics Project, which worked to establish standard procedures and performance metrics for energy performance of buildings.

Parametric Analysis of Building Elements on Building Energy Use

2015 ◽  
Vol 74 (4) ◽  
Author(s):  
Atefeh Mohammadpour ◽  
Mohammad Mottahedi ◽  
Shideh Shams Amiri ◽  
Somayeh Asadi ◽  
David Riley ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Full Range ◽  
Building Energy ◽  
Energy Performance ◽  
Simulation Software ◽  
Design Parameters ◽  
Building Energy Consumption ◽  
Commercial Building ◽  
Building Shape

Building energy modeling is essential to estimate energy consumption of buildings. Predicting building energy consumption benefits the owners, designers, and facility managers by enabling them to have an overview of building energy consumption and can help them to determine building energy performance during the design phase. This paper focuses on two different shapes of commercial building, H and rectangle to estimate energy consumption in buildings in three different climate zones, cold, hot-humid, and mixed-humid. To address this, DOE-2 building simulation software was used to build and simulate individual commercial building configurations that were generated using Monte Carlo simulation techniques. Ten thousand simulations for each building shape and climate zone were conducted to develop a comprehensive dataset covering the full range of design parameters. 

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