Are Energy Management Systems Cost Effective?

Owing to the call for energy efficiency, the need to optimize the energy consumption of commercial buildings-responsible for over 40% of US energy consumption-has recently gained significant attention. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to development of economic model predictive control (EMPC) for building's Heating, Ventilation, and Air Conditioning (HVAC) system. The objective of this paper is to develop a price-sensitive operational model for building's HVAC systems while considering inflexible loads and other distributed energy resources (DERs) such as photovoltaic (PV) generation and battery storage for the buildings. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by building's HVAC system while satisfying the comfort-level of building's occupants. The efficiency of the proposed NL-EMPC controller is evaluated using several simulation case studies.

Reinforcement learning (RL) for controlling building heating, ventilation, and air conditioning (HVAC) systems has shown promise in delivering energy-efficient, energy-flexible and resilient buildings. However, training RL agents in real-time in buildings is generally considered time and cost prohibitive. RL agents can be trained offline in simulated environments, reducing the time to performance once online, but complex models for simulation can be difficult to generate. What level of complexity is required in an offline training model to produce acceptable online results is unknown. This work seeks to determine how simplified models can be used to train RL agents for building HVAC control. The work uses the EnergyPlus simulation environment paired with HVAC systems modeled in Modelica to act as the real-world analogue environment. Simplified reduced-order models of the building were developed using the lumped capacitance method. The results show that an RL agent trained using Deep Q Learning on a simplified building model can be transferable to a more complex building simulation environment with comparable performance. In virtually all cases, the RL agents were able to deliver improved performance over the baseline proportional controller. Online learning gave mixed results in terms of enhancing the policy transfer process. The implementation in this paper was more suitable for improving the performance of poor policy transfer rather than fine-tuning already well-performing policies.

Protecting building occupants against the inhalation of outdoor-origin aerosols

Energy management and demand response with intelligent learning for multi-thermal-zone buildings

This paper presents the CDL-PLC translator, a tool toconvert control sequences for building heating,ventilation, and air conditioning (HVAC) systems expressedin a subset of Modelica called Control Description Language(CDL) which is undergoing standardization via the voluntaryASHRAE Standard 231P into the IEC 61131-10 XML format. Sucha translation is a step in a model-based design workflowand contributes to digitalizing the control deliveryprocess in the building sector. The translation into theIEC 61131-10 XML is the last step in the implementation ofcontrol logic developed in CDL on programmable logiccontrollers (PLCs) standardized in IEC 61131. The paperpresents the details of the translator, an example for thevalidation of a CDL block vs. the corresponding IEC block,and a practical application example for the translation ofa control sequence for a hybrid heat pump plant from a casestudy neighborhood in Belgium. Practical applications, usecases, and future developments are discussed in the contextof building industry processes.

The design of energy-efficient commercial building Heating Ventilation and Air Conditioning (HVAC) systems has been in the forefront of energy conservation efforts over the past few decades. The HVAC systems traditionally run on a static schedule that does not take occupancy into account, wasting a lot of energy in conditioning empty or partially-occupied spaces. This paper investigates the application of non-intrusive techniques to obtain a rough estimate of occupancy from coarse-grained measurements of the sensors that are commonly available through the building management system. Various per-zone schedules can be developed based on this approximate knowledge of occupancy at the level of individual zones. Our experiments in three large commercial buildings confirm that the proposed techniques can uncover the occupancy pattern of the zones, and schedules that incorporate these occupancy patterns can achieve more than 38% reduction in reheat energy consumption while maintaining indoor thermal comfort.

Phthalates and organophosphates in settled dust and HVAC filter dust of U.S. low-income homes: Association with season, building characteristics, and childhood asthma

Wi-Fi based occupancy detection in a complex indoor space under discontinuous wireless communication: A robust filtering based on event-triggered updating

This study investigates the performance of a desiccant wheel configured into two process sections (P1 and P2) and a regeneration section (R), designed to meet the simultaneous demands of humidity control, cooling, and heating in industrial and building environments. The wheel's performance was assessed based on the exit humidity ratio and temperature under three conditions: precooling of P1 streams, precooling of P2 streams, and no precooling, using a two-dimensional mathematical model. Without precooling, P2 demonstrated superior dehumidification, making it ideal for low-humidity industrial processes, while P1 is better suited for moderate-humidity applications, such as air conditioning systems in buildings. To evaluate the impact of sensible precooling, a precooler was first placed before P1, but this setup compromised P2's dehumidification, and P1 failed to meet heating, ventilation, and air conditioning (HVAC) standards. Relocating the precooler to P2 yielded better results, with P2 delivering low-humidity air for industrial applications, while P1 provided air with moderate humidity to meet the humidity control requirements of air conditioning, and after post-cooling, air from P1 can achieve temperatures appropriate for air conditioning. The regeneration section (R) produced warm, humid air, which can be useful for heating industrial or building spaces in winter. This configuration offers a flexible solution for humidity control in industrial environments and energy-efficient HVAC systems for building applications.

Building automation systems (BAS), or building control systems (BCS), typically consist of building energy management systems (BEMSs), physical security and access control, fire/life safety, and other systems (elevators, public announcements, and closed-circuit television). BEMSs control heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings; more specifically, they control HVAC?s primary components such as air handling units (AHUs), chillers, and heating elements. BEMSs are essential components of modern buildings, tasked with seemingly contradicting requirements?minimizing energy consumption while maintaining occupants? comfort [1]. In the United States, about 40% of total energy consumption and 70% of electricity consumption are spent on buildings every year. These numbers are comparable to global statistics that about 30% of total energy consumption and 60% of electricity consumption are spent on buildings. Buildings are an integral part of global cyberphysical systems (smart cities) and evolve and interact with their surroundings (Figure 1) [2]. As buildings undergo years of exploitation, their thermal characteristics deteriorate, indoor spaces (especially in commercial buildings) get rearranged, and usage patterns change. In time, their inner (and outer) microclimates adjust to changes in surrounding buildings, overshadowing patterns, and city climates, not to mention building retrofitting [3], [4]. Thus, even in cases of ?ideally? designed BEMS/HVAC systems, because of ever-changing and uncertain indoor and outdoor environments, their performance frequently falls short of expectations. Unfortunately, the complexity of BEMSs, large amounts of constantly changing data, and evolving interrelations among sensor feeds make identifying these suboptimal behaviors difficult [1], [5]. Therefore, traditional data-mining algorithms and data-analysis tools are often inadequate.

Heating, ventilation, and air conditioning (HVAC) systems in buildings are an emerging application area for model predictive control (MPC) due to the significant cost benefits that can be achieved via load shifting in modern electricity markets. In this paper, we discuss some of the opportunities and challenges associated with applying MPC to commercial HVAC systems. After defining the control problem, a decomposition of the centralized MPC is presented and demonstrated for an example system. Recent work at the Stanford University campus is also highlighted to show these ideas in practice, and an outlook for the field is given.

Heating, ventilating, and air conditioning (HVAC) systems in industrial and commercial buildings constitutes 40% of the world energy consumption. Many recent studies put more emphasize on developing efficient control strategies in order to deliver acceptable occupants' comfort while maintaining optimal energy consumption. This paper introduces a state feedback control approach for heating and air conditioning systems by optimizing set points and operation modes while providing the desired indoor environmen's quality at minimum energy consumption. Simulations have been conducted using the BCVTB framework by developing the interface that allows data exchange between EnergyPlus for building's envelope and MATLAB for integrating the proposed controller. Simulation results are reported to show the potential of the state feedback control approach for saving energy while keeping suitable occupant's comfort.

Two-layer management of HVAC-based Multi-energy buildings under proactive demand response of Fast/Slow-charging EVs

<div><p>Air fresheners or AC (Air Conditioning) systems are increasingly becoming a necessity for urban communities and the industrial world in Jakarta. AC functions to cool industrial rooms, offices and homes. The PT CI Electronic Industry Building, has not yet installed an air conditioning system so that the production room is not comfortable for its employees. The purpose of installing air conditioning in the industrial building is to increase employee comfort and productivity. In installing an air conditioning (AC) system in the industrial building, design data and cooling load analysis are required. Therefore it is necessary to study the design of industrial air conditioners at PT CI by calculating the cooling load, calculating the required electrical power in order to determine the size and type of air conditioner needed. The research methodology starts from determining industrial building data, such as what tools and machines are in the room, the area of the room and the room wall materials, then analyzing the cooling load, determining the power of the cooling machine and determining the cooling system used. From the results of the cooling load analysis in the Electronics Industry building at PT CI, the total load on floors 1 and 2 was 709 kW, or 201,6 TR, including sensible load: 705.83 kW and Latin load: 3.03 kW. The refrigerant used by the AC R 134a engine. The compressor power is 356 HP. Medium large COP 2.8. The cooling system chosen is the central air conditioning system.</p></div>

Residential energy use represents a considerable fraction of the total energy consumption in a region. Therefore, there is an increasing focus on reducing energy use and related greenhouse gas (GHG) emissions by improving the energy efficiency of building envelopes and major household appliances. The residential sector can benefit from the application of environmental impact assessment methods like life cycle assessment (LCA), which can be used to evaluate the environmental impacts of building materials, appliances, and heating, ventilation, and air conditioning (HVAC) units. Results of such assessments can identify and target important areas for improvement, to achieve optimal benefits. In this chapter, two residential HVAC systems in Canada are compared using the ReCiPe method. The systems are evaluated against a number of important environmental impact indicators. The results show that heat pumps present a good option for reducing household energy consumption and GHG emissions. Furthermore, areas for improvement are identified and suggestions are provided which aim at increasing residential energy efficiency and reducing related GHG emissions.