Prediction of Space Heating Energy Consumption in Cabins Based on Multivariate Regression Modelling

Perera, Degurunnehalage Wathsala U.; Halstensen, Maths; Skeie, Nils-Olav

doi:10.7763/ijmo.2015.v5.493

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Another approach to modelling is the use of black-box models, which relies solely on measurement data without any prior knowledge of the building, e.g. ARMAX [7,8] or PLS-R [9,10] models. These types of models show high prediction accuracy, but do not usually allow the application of physical knowledge to define the model.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for grey-box models of building thermal behaviour

Brastein

Perera

Pfeifer

et al. 2018

Energy and Buildings

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Good models for building thermal behaviour are an important part of developing building energy management systems that are capable of reducing energy consumption for space heating through model predictive control. A popular approach to modelling the temperature variations of buildings is grey-box models based on lumped parameter thermal networks. By creating simplified models and calibrating their parameters from measurement data, the resulting model is both accurate and shows good generalisation capabilities. Often, parameters of such models are assumed to be a combination of different physical attributes of the building, hence they have some physical interpretation. In this paper, we investigate the dispersion of parameter estimates by use of randomisation. We show that there is significant dispersion in the parameter estimates when using randomised initial conditions for a numerical optimisation algorithm. Further, we claim that in order to assign a physical interpretation to grey-box model parameters, we require the estimated parameters to converge independently of the initial conditions and different datasets. Despite the dispersion of estimated parameters, the prediction capability of calibrated grey-box models is demonstrated by validating the models on independent data. This shows that the models are usable in a model predictive control system.

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Section: Introductionmentioning

confidence: 99%

Parameter estimation for grey-box models of building thermal behaviour

Brastein

Perera

Pfeifer

et al. 2018

Energy and Buildings

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“…Analysis on energy consumption in residential buildings and debates regarding energy efficiency in the housing stock are dominated by physical and technical studies due to the complexity of the energy behaviors problematic and the lack of information on domestic energy usages [5]. Since the energy consumption characteristics of the residential sector are complex, different types of models are investigated by the researchers to assess the performance [6]. Based on the research specificity, topic of which includes mostly data gathering and processing focused on a specific building sector, in this case residential sector, it is generally based on quantitative method by collecting specific data.…”

Section: Methodsmentioning

confidence: 99%

Energy consumption by the type of energy carrier used in residential sector in city of Pristina

Ahmeti

Kistelegdi

2019

Pollack Periodica

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Residential sector represents the largest energy consumer in the city of Pristina, as a capital of Republic of Kosovo. This is also the most diverse sector in terms of the energy sources used for space heating. Detailed analysis of different heating energy sources is pivotal for understanding the current situation as well as for any future systematic action in this field. In general, the main energy sources used for heating are known but the determination of their share still remains a challenge.Main focus of the current research is to identify the key heating energy sources, to analyze their share in different zones of Pristina, and to present the heated versus living area per different energy sources within dwellings.Data gathered in the field and presented in this paper are considered to be a first step in comprehensive analysis of the residential sector energy consumption in Pristina.

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“…However, the drawback lies in the difficulty of generalizing such models, as they do not incorporate physical knowledge to define the model structure. Numerous studies have employed the black box approach, utilizing methods such as ARMAX [11], [12] and PLS-R method [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model Predictive Control Scheme for Controlling Temperature in Building Under Uncertainties

Jeong,

Brastein,

Skeie

et al. 2023

IEEE Access

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This paper presents a study on temperature control with a heater in a building using Model Predictive Control (MPC) with a focus on addressing two uncertainties: in model and the weather forecast. In previous works, a grey-box model of the building system was developed, and the values of parameters were estimated by the estimation techniques. In this work, based on the model, simulations are conducted comparing four types of MPC controllers: Deterministic MPC, Multistage MPC, Chance-constrained MPC, and hybrid MPC. The hybrid framework integrates the strengths of the multistage and chance-constrained MPCs to achieve conservative performance and increased robustness in constraint satisfaction. The simulations demonstrate that while deterministic MPC may not always guarantee constraint satisfaction, the hybrid framework offers improved robustness by considering uncertainties in model mismatch and uncertain weather forecasts. The 95% confidence region of model uncertainty is used to assess the robustness of simulations. The results show that the hybrid MPC approach is effective in maintaining temperature in the desired range and ensuring constraint satisfaction in controlling the temperature in a building.

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Prediction of Space Heating Energy Consumption in Cabins Based on Multivariate Regression Modelling

Cited by 8 publications

References 19 publications

Parameter estimation for grey-box models of building thermal behaviour

Parameter estimation for grey-box models of building thermal behaviour

Energy consumption by the type of energy carrier used in residential sector in city of Pristina

Hybrid Model Predictive Control Scheme for Controlling Temperature in Building Under Uncertainties

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