Thermal mass and energy recovery utilization for peak load reduction

Fadejev, Jevgeni; Simson, Raimo; Kurnitski, Jarek; Bomberg, Mark

doi:10.1016/j.egypro.2017.09.628

Cited by 12 publications

(14 citation statements)

References 2 publications

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“…Moreover, we need a model of the time response of the building. As we discussed elsewhere [1][2][3][4][5][6][7][8][9], the EQM technology does not rely on the currently existing energy models but develops a neural network-based model (see later text). Since having a well-designed BACS is the key to optimizing the energy performance of the building, one may ask the question-what are the requirements for building an automatics control system?…”

Section: Requirements For Building Automatics Control System (Bacs)mentioning

confidence: 99%

“…During the review of current technology [7][8][9] the shortcomings of the current energy models became clear. Amongst many reasons one can highlight the uncertainty in measurement of the global temperature that is key to operating temperature [10], the effect of interactions that is not reflected in the energy models and costs [11,12], difficulties with applying multi-criterial analysis [13], and interaction of building with a city [14].…”

Section: Introductionmentioning

confidence: 99%

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A Neural Network for Monitoring and Characterization of Buildings with Environmental Quality Management, Part 1: Verification under Steady State Conditions

2020

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Introducing integrated, automatic control to buildings operating with the environmental quality management (EQM) system, we found that existing energy models are not suitable for use in integrated control systems as they poorly represent the real time, interacting, and transient effects that occur under field conditions. We needed another high-precision estimator for energy efficiency and indoor environment and to this end we examined artificial neural networks (ANNs). This paper presents a road map for design and evaluation of ANN-based estimators of the given performance aspect in a complex interacting environment. It demonstrates that in creating a precise representation of a mathematical relationship one must evaluate the stability and fitness under randomly changing initial conditions. It also shows that ANN systems designed in this manner may have a high precision in characterizing the response of the building exposed to the variable outdoor climatic conditions. The absolute value of the relative errors ( M a x A R E ) being less than 1.4% for each stage of the ANN development proves that our objective of monitoring and EQM characterization can be reached.

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Section: Requirements For Building Automatics Control System (Bacs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Neural Network for Monitoring and Characterization of Buildings with Environmental Quality Management, Part 1: Verification under Steady State Conditions

2020

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“…Incorporating hygrothermal models into the energy calculations is important because moisture buffering not only modulates the indoor relative humidity but also reduces peak energy loads (Fadiejev et al, 2017; Simonson et al, 2004). Uncontrolled relative humidity affects both indoor air quality (IAQ) and durability of building materials.…”

Section: A Transition To Sustainable Buildingsmentioning

confidence: 99%

“…In part one of this article, we have highlighted the significance of thermal mass in the reduction of energy use (Fadiejev et al, 2017). We need to highlight two aspects of the modern architecture that contributed mostly to the elimination of mass effect on IE:…”

Section: Description Of the Eqm Technologymentioning

confidence: 99%

Buildings with environmental quality management: Part 4: A path to the future NZEB

Romanska-Zapala

Bomberg

Yarbrough

2018

Journal of Building Physics

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The previous part of this article starts 100 years ago, at the time of the humble beginnings of building science, and brings us to the current stage of the net zero energy buildings (NZEB). We see how, over the years, knowledge from the observed failures of buildings has accumulated to become the basis for current building science. The strong interactions between energy efficiency, moisture management, and indoor environment and the need for their simultaneous analysis led to the concept of environmental assessment. More than 40 years of experience with passive houses (the first 10 were built in Canada in 1977) in process that would collect those developments into the mainstream of NZEB technology permits extrapolation to the future. As the first priority, we see a need for a fundamental change in the approach to NZEB—instead of improving the separate pieces of the puzzle before assembling them, we need first to establish the conceptual design of the whole system. Only after determination of the basic requirements for each subsystem and each assembly may materials that would fulfill the specific requirements of this assembly be selected. In this design process, the actual climate and socio-economic conditions (including construction cost) vary, so we must deal with a set of design principles rather than a description of a specific construction technology. A guiding set of considerations is presented below to establish a system of environmental quality management (EQM).

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“…All these factors are particularly important in the context of the prevention of overheating across climate and building use [8]. A building with an increased amount of thermal 2 of 13 mass is able to time-shift and flatten out heat flow fluctuations [9,10]; this is referred to as the thermal inertia of a building [8]. Guglielmini et al [11] introduce the term "passive thermoregulation effect" to describe the effect with which a building's thermal inertia is able to reduce the fluctuations of the inside air temperature.…”

Section: Introductionmentioning

confidence: 99%

Peripheral Isothermal System of Heat Gain Storage for Thermal Stability in Low-Energy Buildings

2019

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The problem of heat storage in low- or ultra-low-energy houses is becoming a crucial issue. The general purpose of this study was to determine the potential for utilising heat gain recovery in a building. The proposed solution is based on an auxiliary latent heat storage tank using paraffin RT24. The tank is connected to an integrated heat recovery system that supplies heat from the internal loop of a mechanical ventilation system. The storage capacity of the tank was determined using the proposed parameter “excess of heat gains” of the thermal zone, and was obtained by measurement. The detailed construction of the tank, the phase change material properties and the quantity were proposed. The data that was collected allowed for the calculation of the temporary charging level as well as the overall seasonal energy stored in the tank. It was shown that during the heating season, the temperature could rise above the set-up value of 20 °C by as much as 8 K at maximum. Although the analysed building was characterised by heavy construction and high thermal mass, the additional heat could be effectively stored and utilised to cover the energy demand of the zone at the level of 88 MJ/a and 208 MJ/a, depending on the airflow rate between the rooms and the heat exchanger, for 140 and 420 m3/h, respectively. The expected energy effect for a low thermal mass construction, e.g., a timber frame was much higher and the results obtained by using the numerical simulation were 116 MJ/a for 140 m3/h, and 273 MJ/a for 420 m3/h, respectively.

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Thermal mass and energy recovery utilization for peak load reduction

Cited by 12 publications

References 2 publications

A Neural Network for Monitoring and Characterization of Buildings with Environmental Quality Management, Part 1: Verification under Steady State Conditions

A Neural Network for Monitoring and Characterization of Buildings with Environmental Quality Management, Part 1: Verification under Steady State Conditions

Buildings with environmental quality management: Part 4: A path to the future NZEB

Peripheral Isothermal System of Heat Gain Storage for Thermal Stability in Low-Energy Buildings

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