A simple building energy model in form of an equivalent outdoor temperature

Cholewa, Tomasz; Siuta-Olcha, Alicja; Smolarz, Andrzej; Muryjas, Piotr; Wolszczak, Piotr; Anasiewicz, Rafał; Balaras, Constantinos A.

doi:10.1016/j.enbuild.2021.110766

Cited by 30 publications

(14 citation statements)

References 34 publications

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“…As mentioned above, the KPI module allows one to calculate KPIs with simulated data or other data, including monitored data. Focusing on the energy signature, this KPI supports data-driven building-energy-use models that account for the influences of outdoor conditions on heat consumption, to allow fast heating responsessee, for example, studies on data-driven model definitions [36,37]-but also will allow comparing design-driven signatures with monitored ones.…”

Section: Scenario Amentioning

confidence: 99%

A New Tool for Building Energy Optimization: First Round of Successful Dynamic Model Simulations

2021

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Several tools and pieces of software support building energy modelling for optimization, certification and comparisons of different scenarios and usages. Nevertheless, the consistent rise in accessible computational power and the expansion of ICT are pushing the development of new software functionalities and tools able to support cross-disciplinary work on smart building optimization. This paper introduces a new platform (under development) that combines the EnergyPlus dynamic simulation tool with extra-functionalities and pre-defined usage scenarios based on automatic actions to manage massive simulations and correlation analyses. The tool’s utility was tested in three experiments, with goals that we consider to be fundamental requirements: comparing simple retrofit actions to reduce net energy needs; analyzing the free-running potential of a demo building and the impacts of different low-energy technologies in terms of increasing thermal comfort (shading and ventilative cooling); and comparing measured sensor data indicators with simulated ones under real weather conditions for model verification. The results demonstrate the ability of the tool to automatically generate hundreds of EnergyPlus input building models by acting on building geometry; we focused on the most common uses for parametric dynamic simulations. Additionally, the way in which the tool combines the automatic modification of the building’s design and the parallel launching of multiple simulations allows the labor to be reduced. The user can execute complex tasks without spending any time working with model editing software and aggregating the results from multiple simulations.

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Section: Scenario Amentioning

confidence: 99%

A New Tool for Building Energy Optimization: First Round of Successful Dynamic Model Simulations

2021

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“…On the basis of the narrowed range of input data (see Section 3.1), the correction of the outdoor temperature due to solar insolation in J/cm 2 (S_insol) and cloudiness in % (Cloud_1) was determined separately for each building, because each building may react differently to specific weather conditions. An example of determining the outdoor temperature correction both in terms of wind speed and solar insolation has already been presented by the authors in the article [20].…”

Section: Correction Of the Outdoor Temperature Due To Solar Radiationmentioning

confidence: 99%

On the Influence of Solar Radiation on Heat Delivered to Buildings for Heating

et al. 2021

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Nowadays, the attention of designers and service providers is especially focused on energy efficiency and integration of renewable energy sources (RES). However, the knowledge on smart devices and automated, easily applicable algorithms for optimizing heating consumption by effectively taking advantage of solar heat gains, while avoiding overheating, is limited. This paper presents a simple method for taking into account the influence of solar heat gains in the form of solar radiation for the purposes of forecasting or controlling thermal power for heating of buildings. On the basis of field research carried out for seven buildings (five residential buildings and two public buildings) during one heating season, it was noticed that it was justified to properly narrow down the input data range included in the building energy model calculations in order to obtain a higher accuracy of calculations. In order to minimize the impact of other external factors (in particular wind speed) affecting the heat consumption for heating purposes, it was recommended to consider the data range only at wind speeds below 3 m/s. On the other hand, in order to minimize the impact of internal factors (in particular the impact of users), it was suggested to further narrow down the scope of the input data to an hour (e.g., 10–14 in multi-family residential buildings). During these hours, the impact on users was minimized as most of them were outside the building.

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“…(2) the behavior of its occupants; (3) construction materials; and (4) the consumption strategy during its operation. Additionally, the outdoor conditions are crucial, especially in the case of heating building interiors [7]. These factors are typically considered during the building design stage.…”

Section: Introductionmentioning

confidence: 99%

HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings

et al. 2022

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Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

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A simple building energy model in form of an equivalent outdoor temperature

Cited by 30 publications

References 34 publications

A New Tool for Building Energy Optimization: First Round of Successful Dynamic Model Simulations

A New Tool for Building Energy Optimization: First Round of Successful Dynamic Model Simulations

On the Influence of Solar Radiation on Heat Delivered to Buildings for Heating

HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings

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