Stepwise calibration for residential building thermal performance model using hourly heat consumption data

Li, Wancheng; Tian, Zhe; Lu, Yakai; Fu, Fawei

doi:10.1016/j.enbuild.2018.10.001

Cited by 37 publications

(12 citation statements)

References 41 publications

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“…The results were encouraging, however these test conditions were very far from real and occupied buildings where the heating system is thermostatically controlled by the user through the set point temperature. In [27], a step wise calibration of a dynamic thermal empirical model of a residential building was performed. The calibration included some user-dependent parameters, such as the air ventilation rates; however, the constraints derived by the set point temperature control were not included in the analysis.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

et al. 2021

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Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1 °C and 2 °C, respectively.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

et al. 2021

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“…In addition, it is possible to close the performance gap through several stages of in situ measurement used for the calibration process [32]. In general, a wide variety of data are applied to building model calibration (e.g., monthly electricity consumption data [33], natural gas consumption [7,34], energy consumption by some equipment or system [35], heat consumption [36], short-term monitoring, an energy audit [37], etc.). Without regard to this, there is growing importance in using such calibrated energy models [31] as the interest in the sustainable development of buildings and improved monitoring capabilities increases.…”

Section: Introductionmentioning

confidence: 99%

“…Their approach could be valuable when limited data are available. Li et al [36] proposed a stepwise calibration method based on the hourly heat consumption data, which provided additional information compared to monthly data. The calibration results were within the limits of ASHRAE guidelines.…”

Section: Introductionmentioning

confidence: 99%

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Development and Analysis of a Dynamic Energy Model of an Office Using a Building Management System (BMS) and Actual Measurement Data

et al. 2021

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Calibration of the energy model of a building is one of the essential tasks required to determine the efficiency of building management systems, and both their own and other systems’ improvement potential. In order to make the building energy model as accurate as possible, it is necessary to collect comprehensive data on its operation and sometimes to assess the missing information. This paper represents the process of developing an energy model for an administrative building and its calibration procedure, using detailed long-term measurement and building management system (BMS) data. Indoor air temperature, CO₂ concentration, and relative humidity were experimentally measured and evaluated separately. Such dual application of data reduces the inaccuracy of the assumptions made and assesses the model’s accuracy. The DesignBuilder software developed the building model. During the development of the model, it was observed that the actual energy consumption needs to be assessed, as the assumptions made during the design about the operation and management of HVAC systems often do not coincide with the actual situation. After integrating BMS information on HVAC management into the building model, the resulting discrepancy between the model results and the actual heat consumption was 6.5%. Such a model can be further used to optimize management decisions and assess energy savings potential.

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“…Therefore, the importance of accurate sensor data over the life cycle of a building is widely recognised as essential for accurate model calibration and the assessment of retrofit measures (Fabrizio & Monetti, 2015). Comprehensive investigations based on complete, ground-truth input data and metered end-user consumption are still needed to better understand the causes of calibration errors, to improve the modelling process and to assess the benefit of accurate building models during the di↵erent phases of the building life, especially when retrofit measures are implemented at scale (Li, Tian, Lu, & Fu, 2018).…”

Section: Introductionmentioning

confidence: 99%

Environmental and economic benefits of building retrofit measures for the residential sector by utilizing sensor data and advanced calibrated models

Pallonetto

Роса

Finn

2020

Advances in Building Energy Research

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The present paper investigates the energy savings associated with the implementation of retrofitting measures on Irish residential buildings. A detached residential dwelling, representative of approximately 40% of the residential stock in Ireland, was selected as experimental test bed. The building was progressively retrofitted to an all electric dwelling. Retrofit measures included the installation of a photovoltaic array, a geothermal heat pump, an electric vehicle charging point, along with building fabric upgrades. The building was equipped with a home area network with more than 30 sensors with 15 minute monitoring resolution. The experimental data collected during the experimental campaign aided the comprehensive calibration of an EnergyPlus model. This model was used to investigate the e↵ectiveness of the implemented retrofit measures in terms of energy savings and CO2 reductions. Real-time data from the Irish power system operator was used to calculate the building carbon footprint for di↵erent levels of renewable energy penetration to the national grid. Results show that the all-electric retrofitted building can achieve energy savings of up to 45%, with CO2 reductions of approximately 29%, compared to the pre-retrofitted building. Implementing the retrofit measures at scale could potentially lead to carbon emission reductions up to 14% for rural areas in Ireland.

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Stepwise calibration for residential building thermal performance model using hourly heat consumption data

Cited by 37 publications

References 41 publications

Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

Development and Analysis of a Dynamic Energy Model of an Office Using a Building Management System (BMS) and Actual Measurement Data

Environmental and economic benefits of building retrofit measures for the residential sector by utilizing sensor data and advanced calibrated models

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