Multi-zone Building Control System for Energy and Comfort Management

Kan, Ee May; Kan, Siew Leong; Ling, Ngee Hoo; Soh, Yvonne; Lai, Matthew

doi:10.1007/978-3-319-27221-4_4

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…The additional costs that are incurred during the maintenance and rectification of control problems at a later stage of the operation can, thus, be avoided. The building energy system models developed so far use dynamic modeling and undergo simulations to test the detailed design, where the cost of error removal is high [29,30]. Such models have embedded conventional control techniques, such as on-off, PI, and PID control.…”

Section: Literature Surveymentioning

confidence: 99%

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Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability

et al. 2021

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Building energy management system involves the development of control strategies for the heating, ventilation, and air-conditioning (HVAC), as well as lighting, systems. Building energy modeling is a significant part of designing such strategies. In order to analyze the feasibility of a building energy system model for any desired control strategy, a mathematical assessment tool is developed in this paper. A multi-input multi-output (MIMO) building energy system model, consisting of an outdoor wall, an external wall, two partition walls, one roof, and a ceiling, has been considered as the virtual test setup. A methodology for conducting stability and controllability assessment tests on the building energy model is proposed using inverse dynamics input theory (IDIT). IDIT enables the decoupling of control variables so as to enable the conversion of an MIMO system to a number of independent single-input single-output systems. The controllability is assessed based on the design properties for continuous systems: asymptotes and transmission zeros. The results show that the relative humidity and air temperature of the building space were controllable for all operating points; however, in unconditioned situations, where the humidity levels of the building space were greater than that of the outdoor levels, the models were unstable.

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Section: Literature Surveymentioning

confidence: 99%

“…A f loor = −1.115 0.223 0.03936 −0.07085 (29) where A floor = Coefficient matrix for floor wall building envelope model.…”

Section: Stability Analysis Of Building Envelope Modelmentioning

confidence: 99%

Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability

et al. 2021

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“…The set (N − a) is defined by the user and sensory data. The same equation can be used four times to complete one cycle of 24 h, and in each scheduling window, there is a different set of appliances based on the user preferred time and sensor inputs, as can be seen in Equations (20) to (23).…”

Section: Scenario 4: Ems By Using Scenario 2 + Scenariomentioning

confidence: 99%

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

et al. 2017

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Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at "low-priced" hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,"User Comfort Level (UCL)" is formulated on the basis of user preferences on cost saving, tolerance in delay regarding use of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.

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“…Indeed, energy and comfort trade-off in buildings is absorbing interest of many researches around the world. Various aspects of this research domain have been investigated so far [9]- [12]. Enormous attention should be paid to occupants' comfort which is a main concern for building's intelligence.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Cooling Load Prediction Model

Khabthani¹,

Chaâbane²

2018

ijacsa

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In smart buildings, cooling load prediction is important and essential in the sense of energy efficiency especially in hot countries. Indeed, prediction is required in order to provide the occupant by his consumption and incite him to take right decisions that would potentially decrease his energy demand. In some existing models, prediction is based on a selected reference day. This selection depends on several conditions similarity. Such model needs deep analysis of big past data. Instead of a deep study to well select the reference day; this paper is focusing on a short sampling-rate for predicting the next state. So, this method requires less inputs and less stored data. Prediction results will be more close to the real state. In first phase, an hourly cooling load model is implemented. This model has as input current cooling load, current outside temperature and weather forecast to predict the next hour cooling consumption. To enhance model's performance and reliability, the sampling period is decreasing to 30 minutes with respect to system dynamic. Lastly, prediction's accuracy is improved by using previous errors between actual cooling load and prediction results. Simulations are realized in nodes located at a campus showing good adequacy with measurements.

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Multi-zone Building Control System for Energy and Comfort Management

Cited by 4 publications

References 6 publications

Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability

Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability

Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

Development and Validation of a Cooling Load Prediction Model

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