Heating system performance estimation using optimization tool and BEMS data

Djuric, Natasa; Novaković, Vojislav; Frydenlund, Frode

doi:10.1016/j.enbuild.2007.12.006

Cited by 27 publications

(9 citation statements)

References 15 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To define those rules, a domain query language with lexicon and grammar of their statements should be introduced first. The query, for example, may ask for the position of the valve on the supply water pipe connected to the reheat coil in the terminal box [38,39]. The contextual information items used in the query language for HVAC systems can be categorized into three general groups: entities, properties, and relationships.…”

Section: The User-defined Logic Rulementioning

confidence: 99%

Automatically Generating a MEP Logic Chain from Building Information Models with Identification Rules

Xiao

2019

Applied Sciences

View full text Add to dashboard Cite

In mechanical, electrical, and plumbing (MEP) systems, logic chains refer to the upstream and downstream connections between MEP components. Generating the logic chains of MEP systems can improve the efficiency of facility management (FM) activities, such as locating components and retrieving relevant maintenance information for prompt failure detection or for emergency responses. However, due to the amount of equipment and components in commercial MEP systems, manually creating such logic chains is tedious and fallible work. This paper proposes an approach to generate the logic chains of MEP systems using building information models (BIMs) semi-automatically. The approach consists of three steps: (1) the parametric and nonparametric spatial topological analysis within MEP models to generate a connection table, (2) the transformation of MEP systems and custom information requirements to generate the pre-defined and user-defined identification rules, and (3) the logic chain completion of MEP model based on the graph data structure. The approach was applied to a real-world project, which substantiated that the approach was able to generate logic chains of 15 MEP systems with an average accuracy of over 80%.

show abstract

Section: The User-defined Logic Rulementioning

confidence: 99%

Automatically Generating a MEP Logic Chain from Building Information Models with Identification Rules

Xiao

2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Coefficient a j is 1 if flow is entering the observed node and À1 if flow is exiting the observed node. The model calibration was done using optimization method as shown in [1,14], and the objective function was Due to poor estimation or faults, it occurs that water flows do not fulfiled Equation (1).…”

Section: Mass Balance Inspection Algorithmmentioning

confidence: 99%

“…The real water flows were estimated by calibrating the model in Equation (1) to the data from the LTC procedures. The model calibration was done using optimization method as shown in [1,14], and the objective function was…”

Section: Mass Balance Inspection Algorithmmentioning

confidence: 99%

“…Further, integration of these measurements and mathematical methods encourage building performance estimation. For example, heating system performance can be analyzed using BEMS data and sequential quadratic programming [1,14]. Since measurement data can be corrupted by errors, use of data fusion technique can help to estimate real performance data as shown in [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lifetime commissioning as a tool to achieve energy-efficient solutions

Djuric

Novaković

Huang

2011

Int. J. Energy Res.

Self Cite

View full text Add to dashboard Cite

SUMMARY Quality control of the complete energy system is necessary if energy‐efficient solutions are to be met. To perform good building operation and quality control of a given system, it is necessary to have information about building systems and assessment tools. The paper presents Norwegian lifetime commissioning (LTC) procedures that are enabling follow‐up of the building performance during the building lifetime by establishing a generic framework on building performance data. Further, three developed assessment tools are presented: inspection algorithm for ventilation system, mass balance inspection algorithm for consumer substation, and advanced method for improved measurement of heat pump performance based on data fusion technique. The LTC procedures were tested on two case buildings. The results showed that 20% of all the defined building performance data can be monitored by BEMS. Using the mass balance inspection algorithm, it was found that fault in mass balance prevented implantation of desired temperature control for floor heating system. For heat pump performance, measurement of differential water temperature can be very erroneous. Hence, use of compressor electrical signal can give more precise data on heat pump performance. Comparative analysis showed that detailed monitoring system helps tracking energy use and fault detection in operation. Yearly and hourly profiles of energy consumption with separated use and energy carriers are given in the paper. Copyright © 2011 John Wiley & Sons, Ltd.

show abstract

“…There are different approaches for the calibration of building heating models, and these are explained in [20]. The MATLAB model used in this study is calibrated manually by varying the most uncertain parameters, such as the overall heat transfer coefficients and thermal diffusivities, in order to have a good match between the predicted and measured inside temperature profiles during the period 24 to 28 November 2014.…”

Section: Model Calibration and Error Analysismentioning

confidence: 99%

Comparison of Space Heating Energy Consumption of Residential Buildings Based on Traditional and Model-Based Techniques

Perera

Skeie

2017

Buildings

View full text Add to dashboard Cite

This paper presents a comparison of different scenarios in controlling the space heating systems in residential buildings. The space heating energy consumption of a three-storey residential building is estimated using traditional control methods (fixed-temperature schedule and fixed-time schedule) and a mathematical model-based control strategy. The model-based control technique takes the usage pattern of the building into account and operates the heaters based on the calculated heating time of the building. The results from the experiments confirm that the use of a model in heating control is the best option, which can save up to 1400 kWh and 320 kWh per year compared to a fixed-temperature schedule and fixed-time schedule, respectively.

show abstract

Heating system performance estimation using optimization tool and BEMS data

Cited by 27 publications

References 15 publications

Automatically Generating a MEP Logic Chain from Building Information Models with Identification Rules

Automatically Generating a MEP Logic Chain from Building Information Models with Identification Rules

Lifetime commissioning as a tool to achieve energy-efficient solutions

Comparison of Space Heating Energy Consumption of Residential Buildings Based on Traditional and Model-Based Techniques

Contact Info

Product

Resources

About