Abstract
The digital world is spreading to all sectors of the economy, and Industry 4.0, with the digital twin, is a reality in the building sector. Energy reduction and decarbonization in buildings are urgently required. Models are the base for prediction and preparedness for uncertainty. Building energy models have been a growing field for a long time. This paper proposes a novel calibration methodology for a building energy model based on two pillars: simplicity, because there is an important reduction in the number of parameters (four) to be adjusted, and cost-effectiveness, because the methodology minimizes the number of sensors provided to perform the process by 47.5%. The new methodology was validated empirically and comparatively based on a previous work carried out in Annex 58 of the International Energy Agency (IEA). The use of a tested and structured experiment adds value to the results obtained.
Highlights
With the continuing challenges posed by climate change, a growing number of countries around the world are implementing measures to reduce energy consumption and greenhouse gas emissions
In terms of building energy models (BEMs), and Lamberts [18] differentiated between the following types depending on the physical relevance of the parameters:
We used the data provided in Annex 58 for the validation of an adjustment methodology where the calibration model is based on using fewer, less intrusive, and low-cost sensors
Summary
With the continuing challenges posed by climate change, a growing number of countries around the world are implementing measures to reduce energy consumption and greenhouse gas emissions. RC models are often used when MPC solutions are to be proposed due to their high calculation speed, whereas dynamic models, based on high-fidelity physics, can provide a more accurate analysis of energy performance [23] They require a large amount of input data, some of which are difficult to obtain, resulting in large uncertainty [36,43,44,45]. Chong et al proposed a bayesian calibration framework in an EnergyPlus model of the cooling system of a ten story office building located in Pennsylvania with hourly energy CV(RMSE) of 6% by using ten parameters and without information about temperature in building thermal zones.
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