6,884 research outputs found
HVAC system size – getting it right
There is evidence that many heating, ventilating & air conditioning (HVAC) systems, installed
in larger buildings, have more capacity than is ever required to keep the occupants
comfortable. This paper explores the reasons why this can occur, by examining a typical
brief/design/documentation process.
Over-sized HVAC systems cost more to install and operate and may not be able to control
thermal comfort as well as a “right-sized” system. These impacts are evaluated, where data
exists.
Finally, some suggestions are developed to minimise both the extent of, and the negative
impacts of, HVAC system over-sizing, for example:
• Challenge “rules of thumb” and/or brief requirements which may be out of date.
• Conduct an accurate load estimate, using AIRAH design data, specific to project
location, and then resist the temptation to apply “safety factors
• Use a load estimation program that accounts for thermal storage and diversification
of peak loads for each zone and air handling system.
• Select chiller sizes and staged or variable speed pumps and fans to ensure good part
load performance.
• Allow for unknown future tenancies by designing flexibility into the system, not by
over-sizing. For example, generous sizing of distribution pipework and ductwork will
allow available capacity to be redistributed.
• Provide an auxiliary tenant condenser water loop to handle high load areas.
• Consider using an Integrated Design Process, build an integrated load and energy
use simulation model and test different operational scenarios
• Use comprehensive Life Cycle Cost analysis for selection of the most optimal design
solutions.
This paper is an interim report on the findings of CRC-CI project 2002-051-B, Right-Sizing
HVAC Systems, which is due for completion in January 2006
Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis
We describe a social game that we designed for encouraging energy efficient
behavior amongst building occupants with the aim of reducing overall energy
consumption in the building. Occupants vote for their desired lighting level
and win points which are used in a lottery based on how far their vote is from
the maximum setting. We assume that the occupants are utility maximizers and
that their utility functions capture the tradeoff between winning points and
their comfort level. We model the occupants as non-cooperative agents in a
continuous game and we characterize their play using the Nash equilibrium
concept. Using occupant voting data, we parameterize their utility functions
and use a convex optimization problem to estimate the parameters. We simulate
the game defined by the estimated utility functions and show that the estimated
model for occupant behavior is a good predictor of their actual behavior. In
addition, we show that due to the social game, there is a significant reduction
in energy consumption
Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance
Heating, Ventilation and Air Conditioning (HVAC) consumes a significant
fraction of energy in commercial buildings. Hence, the use of optimization
techniques to reduce HVAC energy consumption has been widely studied. Model
predictive control (MPC) is one state of the art optimization technique for
HVAC control which converts the control problem to a sequence of optimization
problems, each over a finite time horizon. In a typical MPC, future system
state is estimated from a model using predictions of model inputs, such as
building occupancy and outside air temperature. Consequently, as prediction
accuracy deteriorates, MPC performance--in terms of occupant comfort and
building energy use--degrades. In this work, we use a custom-built building
thermal simulator to systematically investigate the impact of occupancy
prediction errors on occupant comfort and energy consumption. Our analysis
shows that in our test building, as occupancy prediction error increases from
5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than
that of even a simple static schedule. However, when combined with a personal
environmental control (PEC) system, HVAC controllers are considerably more
robust to prediction errors. Thus, we quantify the effectiveness of PECs in
mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure
Engaging as Partners in Energy Efficiency: A Primer for Utilities on the Energy Efficiency Needs of Multifamily Buildings and Their Owners
The multifamily building sector presents a unique set of challenges and opportunities for utilities seeking to implement effective energy efficiency programs. To deliver successful programs, utilities must understand what motivates building owners to take part in these programs, as well as barriers that may prevent participation.This paper outlines the opportunities to meet energy efficiency goals with multifamily programs. It then describes the benefits that multifamily building owners gain from these programs, and the barriers they face to participation. The paper focuses on rental housing, because these buildings are owned by a single entity and form the largest sector of the multifamily housing market. The paper provides a framework to help utilities develop successful programs that maximize energy savings and create benefits for building owners, tenants, and communities. And lastly, the paper recommends nine program design considerations that can help attract multifamily building owners to utility energy efficiency programs
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Optimizing Radiant Systems for Energy Efficiency and Comfort
Radiant cooling and heating systems provide an opportunity to achieve significant energy savings, peak demand reduction, load shifting, and thermal comfort improvements compared to conventional all-air systems. As a result, application of these systems has increased in recent years, particularly in zero-net-energy (ZNE) and other advanced low-energy buildings. Despite this growth, completed installations to date have demonstrated that controls and operation of radiant systems can be challenging due to a lack of familiarity within the heating, ventilation, and air-conditioning (HVAC) design and operations professions, often involving new concepts (particularly related to the slow response in high thermal mass radiant systems). To achieve the significant reductions in building energy use proposed by California Public Utilities Commission’s (CPUC’s) Energy Efficiency Strategic Plan that all new non-residential buildings be ZNE by 2030, it is critical that new technologies that will play a major role in reaching this goal be applied in an effective manner. This final report describes the results of a comprehensive multi-faceted research project that was undertaken to address these needed enhancements to radiant technology by developing the following: (1) sizing and operation tools (currently unavailable on the market) to provide reliable methods to take full advantage of the radiant systems to provide improved energy performance while maintaining comfortable conditions, (2) energy, cost, and occupant comfort data to provide real world examples of energy efficient, affordable, and comfortable buildings using radiant systems, and (3) Title-24 and ASHRAE Standards advancements to enhance the building industry’s ability to achieve significant energy efficiency goals in California with radiant systems. The research team used a combination of full-scale fundamental laboratory experiments, whole-building energy simulations and simplified tool development, and detailed field studies and control demonstrations to assemble the new information, guidance and tools necessary to help the building industry achieve significant energy efficiency goals for radiant systems in California
HVAC-based hierarchical energy management system for microgrids
With the high penetration of renewable energy into the grid, power fluctuations and supply-demand power mismatch are becoming more prominent, which pose a great challenge for the power system to eliminate negative effects through demand side management (DSM). The flexible load, such as heating, ventilation, air conditioning (HVAC) system, has a great potential to provide demand response services in the electricity grids. In this thesis, a comprehensive framework based on a forecasting-management optimization approach is proposed to coordinate multiple HVAC systems to deal with uncertainties from renewable energy resources and maximize the energy efficiency. In the forecasting stage, a hybrid model based on Multiple Aggregation Prediction Algorithm with exogenous variables (MAPAx)-Principal Components Analysis (PCA) is proposed to predict changes of local solar radiance, vy using the local observation dataset and real-time meteorological indexes acquired from the weather forecast spot. The forecast result is then compared with the statistical benchmark models and assessed by performance evaluation indexes. In the management stage, a novel distributed algorithm is developed to coordinate power consumption of HVAC systems by varying the compressors’ frequency to maintain the supply-demand balance. It demonstrates that the cost and capacity of energy storage systems can be curtailed, since HVACs can absorb excessive power generation. More importantly, the method addresses a consensus problem under a switching communication topology by using Lyapunov argument, which relaxes the communication requirement. In the optimization stage, a price-comfort optimization model regarding HVAC’s end users is formulated and a proportional-integral-derivative (PID)-based distributed algorithm is thus developed to minimize the customer’s total cost, whilst alleviating the global power imbalance. The end users are motivated to participate in energy trade through DSM scheme. Furthermore, the coordination scheme can be extended to accommodate battery energy storage systems (BESSs) and a hybrid BESS-HVAC system with increasing storage capacity is proved as a promising solution to enhance its selfregulation ability in a microgrid. Extensive case studies have been undertaken with the respective control strategies to investigate effectiveness of the algorithms under various scenarios. The techniques developed in this thesis has helped the partnership company of this project to develop their smart immersion heaters for the customers with minimum energy cost and maximum photovoltaic efficiency
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