Development of air-conditioning control algorithm for building energy saving

Yamada, Fumio; Yonezawa, Kenzo; Sugawara, S.; Nishimura, Nobutaka

doi:10.1109/cca.1999.801207

Cited by 18 publications

(18 citation statements)

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“…Their proposed system therefore needs to be improved in its training level. Yamada et al [97] also considered only the temperature as an indicator for comfort level. Such works on comfort level may consider other aspects of indoor comfort, such as humidity and air speed.…”

Section: Other Techniquesmentioning

confidence: 99%

“…Their system can predict the number of occupants in order to estimate building performance to achieve energy savings and high comfort levels for indoor conditions. However, neural network-and fuzzy system-based models typically need a training process, and for Yamada et al's [97] developed tool, this training process needs a considerable amount of time. Their proposed system therefore needs to be improved in its training level.…”

Section: Other Techniquesmentioning

confidence: 99%

“…Yamada et al [97] developed a system that combines neural networks, fuzzy systems, and predictive control in order to control air-condition systems. Their system can predict the number of occupants in order to estimate building performance to achieve energy savings and high comfort levels for indoor conditions.…”

Section: Other Techniquesmentioning

confidence: 99%

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A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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Abstract:Buildings currently account for 30-40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States' energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants' energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings' energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants' energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants' energy-consuming behaviors in order to assess and predict such characteristics' impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change OPEN ACCESSEnergies 2015, 8 10997 occupants' energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

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Section: Other Techniquesmentioning

confidence: 99%

Section: Other Techniquesmentioning

confidence: 99%

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A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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“…Fuzzy logic has also been used to reduce energy consumption by itself rather than modeling PID controller with fuzzy rules, and membership functions. Yamada et al modeled the PMV index with neural network by using its six variables to estimate the current PMV value [92]. In order to reach target PMV value, they proposed a FLC, which input is PMV error, and output is temperature set point.…”

Section: Energy Conservationmentioning

confidence: 99%

Energy Efficiency of Distributed Environmental Control Systems

Khalifa¹,

Işık²,

Dannenhoffer³

2011

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Wilcoxen of the Syracuse University Maxwell School, and three engineering graduate students who performed the analytical and computational work under the supervision of Professors Khalifa, Isik, Dannenhoffer and Wilcoxen, namely: Mr. Ian Cosden who developed and implemented the thermal model of the building and its environmental control system; Mr. Seckin Ari who developed the comfort model and performed the optimization and control analyses; and Mr. Xuanhang (Simon) Zhang, who developed the CFD model of intra-and inter-cubicle air and energy transport and refined the thermal and comfort models accordingly.Report No. DOE\ER63694-1 iii A. ABSTRACTRecent research has indicated that allowing building occupants to customize their own local environment increases satisfaction and workplace performance. However, concern about the possible increase of energy consumption associated with the implementation of distributed localized environmental control has limited the widespread adoption of such systems. In this report, we present an analytical evaluation of the potential of occupant-regulated distributed environmental control systems (DECS) to enhance individual occupant thermal comfort in an office building with no increase, and possibly even a decrease in annual energy consumption. To this end we developed and applied several analytical models that allowed us to optimize comfort and energy consumption in partitioned office buildings equipped with either conventional central HVAC systems or occupant-regulated DECS. Our approach involved the following interrelated components:1. Development of a simplified lumped-parameter thermal circuit model to compute the annual energy consumption. This was necessitated by the need to perform tens of thousands of optimization calculations involving different US climatic regions, and different occupant thermal preferences of a population of ~50 office occupants. Yearly transient simulations using TRNSYS, a time-dependent building energy modeling program, were run to determine the robustness of the simplified approach against time-dependent simulations. The simplified model predicts yearly energy consumption within approximately 0.6% of an equivalent transient simulation. Simulations of building energy usage were run for a wide variety of climatic regions and control scenarios, including traditional "one-size-fits-all" (OSFA) control; providing a uniform temperature to the entire building, and occupantselected "have-it-your-way" (HIYW) control with a thermostat at each workstation. The thermal model shows that, un-optimized, DECS would lead to an increase in building energy consumption between 3-16% compared to the conventional approach depending on the climate regional and personal preferences of building occupants. Variations in building shape had little impact in the relative energy usage. The model also allowed us to study the effect of the thermal resistance of the partitions between adjacent workstations, which can have a significant influence on the energy cost (3% to 78% increas...

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“…al. [24] has developed a neuro-fuzzy BMS to predict weather parameters and the number of occupants in a building. This predictive information are then used to profile the energy flow for the building in order to minimise energy consumption and to maintain thermal comfort at an acceptable level.…”

Section: Multi Agent Systemmentioning

confidence: 99%

Intelligent multi-agent system for building heat distribution control with combined gas boilers and ground source heat pump

Mokhtar

Stables

Liu

et al. 2013

Energy and Buildings

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Intelligent energy system refers to the use of intelligent control strategies with the energy system, which can help minimise energy waste and loss, optimise load control, increase and improve energy efficiency and/or maximise the benefits of renewable energy. Renewable energy and intelligent controls are interlinked and correlated with each other. Without intelligent controls, the full benefits of renewable energy technologies may not be achievable, specifically the Ground Source Heat Pump (GSHP) technologies. This was observed, especially by the University of Central Lancashire (UCLan) that had incorporated a GSHP to one of its building. The GSHP is under performing due to the inefficient controls implemented with the GSHP. This paper proposes an intelligent multiagent building management system (MAS BMS) that aims to tackle this issue. Intelligence is provided by an ARTMAP, a type of artificial neural network that provides incremental learning inspired by how human process memory. Simulation results show the proposed intelligent MAS BMS is able to maximise the use of the GSHP effectively by profiling, predicting and coordinating its usage with other energy resources. The proposed method has performed better than the existing control strategies for the GSHP.

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Development of air-conditioning control algorithm for building energy saving

Cited by 18 publications

References 0 publications

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Energy Efficiency of Distributed Environmental Control Systems

Intelligent multi-agent system for building heat distribution control with combined gas boilers and ground source heat pump

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