Building Thermal Systems Research Articles

TdT: A tool for building thermal systems analysis and comparison

This work describes the tool “Towards Dynamic Thermoeconomics” (TdT), which has been designed to analyse the thermal systems of buildings from a dynamic point of view for further thermoeconomic analysis, since HVAC systems consume a significant part of the global energy and have a significant environmental impact. TdT is presented as a versatile, open-source application developed in Python, which provides dynamic energy and exergy performances of components, as well as the most vulnerable points in the system to be optimised. As a novelty TdT introduces a new methodology for analysing inertial systems that defines non-periodic time intervals in which the tank temperature is Ttank,rep, thus avoiding misleading cost results. Two case studies are presented as examples of typical applications: a domestic hot water system and a heating system, analysed with TdT in eight different climatic locations in Spain. The corresponding results are extracted and analysed, with a particular focus on the influence of the outdoor temperature (T0) on both the exergy and energy efficiency of the components, the identification of irreversibilities and the total exergy consumption. It becomes evident that as the T0 increases by 38 %, the exergy efficiency of the boiler decreases in both systems, with an average yearly exergy efficiency decrease of 1.4 % on the DHW system. Furthermore, the boiler is the component with the highest irreversibilities, followed by the inertial and distribution components, information that could not be obtained from energy analysis. Ultimately, the TdT provides valuable insights and a novel methodology for analysing inertial components, which will improve the efficiency and sustainability of building thermal systems.

Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering

A customized and cost-effective building thermal control system is critical for accommodating thermal performance differences within the building, as well as satisfying the individual thermal comfort needs of occupants. Moreover, incorporating a building indoor thermal simulation procedure into the thermal control system can reduce the necessity of installing various expensive sensors (e.g., wearable sensors for personal thermal comfort management) in individual offices, as well as the requirement of extensive computing facilities without rendering the control performance, resulting into more sustainable building operations. An important step in achieving the above-mentioned goal is understanding how different offices/rooms behave differently given the same outdoor weather conditions. This study proposes a smart building indoor thermal profiling system to identify underlying physical factors that affect thermal performance in different seasons and to track dynamic cluster trajectories of considered offices to suggest indoor thermal optimization strategies. A consensus-based clustering approach is adopted to robustly cluster offices into different groups based on their hourly indoor temperature profiles for different seasons. Experimental results showed that our proposed approach could effectively discover more indoor thermal patterns in the buildings and is able to identify distinct dynamic cluster trajectories across four seasons (i.e., eight distinct dynamic trajectories in our case study). The data-driven analysis conducted in this study also indicated promising applications of the proposed smart building indoor thermal profiling system in effectively guiding the design of customized thermal control strategies for buildings. It also suggested that the proposed approach could be applied to a wide range of other applications, such as customized building energy management, energy pricing, as well as the economic benefit analysis of building retrofits and design.

Hourly global solar radiation prediction based on seasonal and stochastic feature

Accurate and detailed solar radiation data play a crucial role in the simulation of building thermal and photovoltaic systems. However, developing a highly precise and dependable solar radiation model using cost-effective data has proven challenging. This work proposes a new attenuation solar radiation model formed by conducting a comprehensive analysis of existing models and gaining new insights into solar radiation's seasonal and stochastic properties. Meanwhile, the model is constructed using easily obtainable surface meteorological parameters. The results demonstrate that the proposed model exhibits good performance in terms of prediction accuracy. Moreover, the majority of existing hourly solar radiation models have been primarily developed for clear-sky conditions. However, there is a growing demand for solar radiation hourly estimations that can uphold a high level of accuracy and reliability even in different weather state. Conversely, the proposed model is developed and validated by more than twenty year's meteorological data encompassing various weather conditions in Japan. It effectively captures the stochastic nature of solar radiation by utilizing turbidity parameters, even on cloudy and rainy days. Additionally, the inclusion of interaction variables significantly enhances its interpretability.

Experimental test of a building thermal system for preventive maintenance based on thermoeconomic analysis

An analysis of the sectorial structure of energy consumption shows that residential and tertiary sector buildings are the third-highest consumers, responsible for 29.5% of a city’s final energy consumption. The Building Quality Control Laboratory of the Basque Government aims to promote quality, innovation, and sustainability in buildings. To accomplish this goal, it has constructed an experimental facility with different energy generation technologies and a very versatile control system for testing different energy systems and operation modes. In this study, we tested a facility for supplying domestic hot water and heating for a multi-family house by means of a condensing boiler and an aerothermal heat pump (together with the corresponding control). This installation could reproduce the thermal demands required to be satisfied by the generation equipment through a programmed operation of the installation based on real demands. Additionally, this installation was analyzed using thermoeconomics (TE) to solve problems unable to be solved using traditional energy analyses based on the First Law of Thermodynamics. These problems include: (1) Determining the costs of the products of the installation based on physical criteria, (2) detecting the places where losses actually occur, evaluating their costs, and proposing cost-effective improvements, and (3) diagnosing issues in the installation. As a result, this paper suggests a solution to the preventive maintenance problems confronting the technical maintenance personnel for thermal installations in buildings by applying TE knowledge and using real data collected from sensors.

From Building Information Model to Digital Twin: A Framework for Building Thermal Comfort Monitoring, Visualizing, and Assessment

The existing building stock is globally responsible for 17.5% of greenhouse gas emissions due to their operation to achieve occupant satisfaction, thus requiring a vast intervention. However, reducing building stock emissions and optimizing building energy performance cannot be considered independently by the users’ well-being. The thermal comfort conditions and their monitoring represent a central issue that could optimize building energy usage while achieving good indoor environmental conditions. This document describes the first findings of ongoing research focused on the development of a building monitoring system, based on the integration of Building Information Modeling tools and sensor technology through Dynamo Visual Programming. Starting from the development of an Asset Information Model, which represents the virtual replica of a building that currently hosts the administrative offices of the municipality of Cagliari, the first step presented in this contribution shows a thermal comfort monitoring system, scalable and modular, that allows effective gathering and elaboration of data about comfort levels in each of the building’s rooms. The system proves to be a helpful support for facility managers to control building thermal comfort conditions and HVAC systems to assure their best operative status or plan suitable interventions to achieve it.

Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction

Accurate supply temperature prediction plays a vital role in achieving meticulous management of heating station. However, there are relatively few studies on ultra-short-term supply temperature prediction at present. This paper comprehensively applied 4 feature selection methods and 3 prediction algorithms to estimate hourly secondary supply temperature. Taking a floor radiant heating system as the case, the correlation analysis (CA) based on the back propagation neural network (BPNN) model and the support vector regression (SVR) model shows that outdoor temperature, supply and return temperatures are the main input feature categories. This paper novelty proposed the categorical principal component analysis (CPCA) method, compared with the traditional principal component analysis (PCA), this method can reduce the root mean square error (RMSE) of BPNN model and SVR model by an average of 18.6% and 19.7%, respectively. The comparison of 4 historical input lengths for the long and short-term memory (LSTM) model shows that historical 12 h can fully consider the influence of building thermal inertia and heating system thermal delay for floor radiant. Further comprehensive comparison shows that the BPNN model based on correlation analysis has the best performance.

Electricity Consumption Optimization Using Thermal and Battery Energy Storage Systems in Buildings

Energy storage system (ESS) plays a key role in peak load shaving to minimize power consumption of buildings in peak hours. This paper proposes a novel energy management unit (EMU) to define an optimal operation schedule of ESSs by employing metaheuristic and mathematical optimization approaches. The proposed EMU uses a thermal energy storage system (TESS) and a battery energy storage system (BESS) to store the energy in off-peak periods and discharge it in high load demands. We formulate the charging/discharging schedule of TESS and BESS as an optimization problem. Then, particle swarm optimization (PSO) is employed to obtain the optimal schedule due to its computational time efficiency. The mathematical approach is also applied to prove the convexity of the problem and the uniqueness of the solution. Due to the different characteristics of the building loads, this paper divides the total load into shiftable and fixed loads. Moreover, to model the building components and loads, grey-box modeling is adopted. Results show that employing a combination of TESS and BESS achieves peak load shaving while reducing 42.2% of the required BESS capacity compared with the case where the BESS is only used. In addition, the results indicate the effectiveness and robustness of the proposed algorithm.

A Network-Based Strategy to Increase the Sustainability of Building Supply Air Systems Responding to Unexpected Temperature Patterns

As real-time indoor thermal data became available, the precision of the building thermal control systems has improved, but the use of resources has also increased. Therefore, it is imperative to examine the optimized point of energy use and thermal dissatisfaction for their efficient control. The aim of this research is to find an energy-efficient thermal control strategy to suppress the increase in thermal dissatisfaction. An adaptive control model utilizing the artificial neural network and the adjustment process of initial settings is proposed to examine its performance in controlling thermal supply air in terms of indoor thermal dissatisfaction and energy use. For a clear comparison, the standard deviation of each thermal dissatisfaction value and the weekly heating energy transfer are used. The proposed model successfully performs in reducing the indoor thermal dissatisfaction level and energy use. In comparison with two deterministic models, the performance is improved in terms of the constancy of suppressing thermal dissatisfaction levels by 72.1% and the improvement in energy efficiency by 18.8%, respectively. The significance of this study Is that it is possible to improve control precision by adding only a few modules without replacing the entire existing system, and that the model’s sustainability is increased by reducing the possibility of hardware and software retrofitting in the future.

Compact Thermal Storage with Phase Change Material for Low-Temperature Waste Heat Recovery—Advances and Perspectives

The current interest in thermal energy storage is connected with increasing the efficiency of conventional fuel-dependent systems by storing the waste heat in low consumption periods, as well as with harvesting renewable energy sources with intermittent character. Many of the studies are directed towards compact solutions requiring less space than the commonly used hot water tanks. This is especially important for small capacity thermal systems in buildings, in family houses or small communities. There are many examples of thermal energy storage (TES) in the literature using the latent heat of phase change, but only a few are commercially available. There are no distinct generally accepted requirements for such TES systems. The present work fills that gap on the basis of the state of the art in the field. It reviews the most prospective designs among the available compact latent heat storage (LHS) systems in residential applications for hot water, heating and cooling and the methods for their investigation and optimization. It indicates the important characteristics of the most cost- and energy-efficient compact design of an LHS for waste heat utilization. The proper design provides the chosen targets at a reasonable cost, with a high heat transfer rate and effective insulation. It allows connection to multiple heat sources, coupling with a heat pump and integration into existing technologies and expected future scenarios for residential heating and cooling. Compact shell-tube type is distinguished for its advantages and commercial application.

Impact of exothermic chemical reaction on MHD unsteady mixed convective flow in a rectangular porous cavity filled with nanofluid

The rectangular enclosure filled with nanofluid has several technical applications including building thermal management systems and photovoltaic thermal management systems. This investigation theoretically investigates the transport features of the time-dependent, mixed convective, electrically conducting nanofluid flow inside a cavity saturated with permeable medium. The impact of a uniform magnetic field applied in the transverse direction to the fluid flow and Arrhenius kinetics theory based exothermic chemical reaction are considered. The Buongiorno's model is adopted in the present analysis to characterize the nanofluid. The constitutive partial differential equations are solved by using the Marker and Cell (MAC) technique. The impact of the nondimensional numbers on transport phenomena of the present study is elucidated graphically. The outcomes disclose that the pertinent parameters such as the Reynolds number (Re), Hartmann number (Ha), Darcy number (Da), Lewis number (Le), Richardson number (Ri), thermophoresis parameter , Brownian motion parameter , and Frank-Kamenetskii number (Fk) play a main role on regulating the transport characteristics of the present study. Results demonstrate that the isotherms increase with escalating Frank–Kamenetskii number values. Higher Richardson number values improve the heat transfer inside the enclosure.

A Network-based Model to Improve the Constancy of Thermal Comfort and the Use of Thermal Energy during Sudden Changes in Outdoor Temperature

Purpose: As real-time indoor thermal data became available, the precision of building thermal control systems has been improved. However, resource usage has also increased. Therefore, examining the optimized point of energy use and thermal dissatisfaction for efficient control is imperative. This study aimed to find an energy-efficient thermal control strategy to suppress the increase in thermal dissatisfaction. Method: An adaptive control model utilizing an artificial neural network and the adjustment process of initial settings is proposed to examine the performance in controlling thermal air supply in terms of indoor thermal dissatisfaction and energy use. The standard deviation of each thermal dissatisfaction value and the weekly heating energy transfer are used for a clear comparison. Results: The proposed model successfully reduced indoor thermal dissatisfaction levels and energy use. Compared with the two deterministic models, the performance is improved in terms of the constancy of suppressing thermal dissatisfaction levels by 95.3% and the improvement of energy efficiency by 3.7%. The model can improve the sustainability of the old thermal system without replacing the overall system.

Economic and greenhouse gas analysis of regional bioenergy-powered district energy systems in California

There is an urgent need for building energy services that are not only low-carbon but reliable. District energy systems (DES) have the potential to meet building power, heating and cooling needs efficiently and reliably, but further research is needed to understand their coupling with local renewable energy sources such as biomass. Here we assess the cost and greenhouse gas implications of using bioenergy from local organic waste to power DES in California communities. We describe a set of possible scenarios for bioenergy integration into DES, including fuel switching and retrofitting of existing systems and DES expansion through new projects. In all locations, DES have higher combined capital and operating expenses (ranging from $5–12 million) than conventional fossil fuel building thermal systems. However, the net amortized annual cost of DES can reach as low as 70% that of the conventional system from the sale of excess electricity. Bioenergy-powered DES can offer a cost of carbon abatement on par with distributed solar-powered building thermal systems (50% less to 34% more). Furthermore, upgrading existing DES from fossil to renewable natural gas represents an immediate economical path to decarbonize buildings.

Performance evaluation of a dynamic wall integrated with active insulation and thermal energy storage systems

Thermal energy storage systems in buildings can store cooling/heating energy during non-peak load hours or when renewable energy sources are available for later use when demanded. Using the building envelope as a thermal battery can help to shave the peak load, reduce the burden in electric grids, and enhance the occupant's thermal comfort. Sensible energy storage on wall systems such as thermally activated building systems can provide an active thermal storage strategy. However, most of the stored energy is used through passive means directed by the thermal lag, which can impede the on-demand release of the stored energy. Integrating active insulation systems with building thermal storage systems can increase the flexibility of charging and discharging time and duration. In this study, a wall system equipped with an active insulation system and thermally activated storage system was designed, and its performance on active cooling energy contribution was studied. The performances of energy storage (charging), release (discharging) of the thermal energy storage energy, and the active insulation system were studied separately and together as an integrated system. Results showed that the thermal properties of the thermal energy storage core material and the pipe spacing of both embedded pipes in the thermal energy storage and hydronic pipes used in the active insulation system affected the wall performance the most out of all the tested wall system parameters. During discharging, the heat flux into the wall was as high as 81.92 W/m2. The active insulation system's dynamic R-value varied between less than 1ft2•°F•h/BTU (0.18 m2•K/W) to 98% of the equally thick foam insulation's R-value.

Procedure for modelling and calibrating operating thermal systems in buildings

Before applying energy improvement strategies to real energy supply facilities, it is necessary to have an accurate model of their dynamic energy behaviour in order to predict and optimize the results. However, contrary to experimental facilities, in real operating facilities, budget constraints and fast access to recorded data limit the number of installed sensors to the minimum required for control and maintenance purposes. The aim of this work is to validate an energy model based on a real thermal facility with the ASHRAE validation criteria. The database analysis reveals that the recorded data are limited and do not have the same sampling interval, varying from 15 min to 24 h. Then, the facility's energy consumption is simulated for typical days in TRNSYS and a novel application combining TRNSYS and Matlab software is used to iteratively calibrate the model. The calibration process applied reduces the uncertainty of the model from the initial values of 26.60 ± 0.78% and 60.76 ± 19.53% for the statistical indices, mean bias error (MBE) and coefficient of variation of the root mean square error (CV(RMSEP)), to the final values −1.89 ± 0.61% and 55.92 ± 17.89% respectively. The methodology developed in this work overcomes the difficulties derived from the limited data in the model design and calibration processes, resulting in a procedure that balances the little available building information and the computational effort, with satisfactory results. Therefore, it could be applicable in models with also high uncertainty and any facilities configuration.

Dynamic Simulation-Based Surrogate Model for the Dimensioning of Building Energy Systems

In recent decades, building design and operation have been an important field of study, due to the significant share of buildings in global primary energy consumption and the time that most people spend indoors. As such, multiple studies focus on aspects of building energy consumption and occupant comfort optimization. The scientific community has discerned the importance of operation optimization through retrofitting actions for on-site building energy systems, achieved by the use of simulation techniques, surrogate modeling, as well as the guidance of existing building performance and indoor occupancy standards. However, more knowledge should be attained on the matter of whether this methodology can be extended towards the early stages of thermal system and/or building design. To this end, the present study provides a building thermal system design optimization methodology. A data set of minimum thermal system power, for a typical range of building characteristics, is generated, according to the criterion of occupant discomfort in degree hours. Respectively, a surrogate model, providing a configurable correlation of the above set of thermal system dimensioning solutions is developed, using regression model fitting techniques. Computational results indicate that such a model could provide both desirable calculative simplification and accuracy on par with existing respective thermal load calculation standards and simplified system dimensioning methods.

BIM-based analysis of energy efficiency design of building thermal system and HVAC system based on GB50189-2015 in China

Abstract Recently, the application of building information modeling (BIM) in building energy efficiency design programs has been emphasized in research. In China, faced with requirements in building energy efficiency design standards and specifications, researchers have been using BIM as a platform for breaking up bottlenecks in energy simulation and design software, and BIM is considered the key to improve the efficiency of building energy efficiency design. Firstly, based on the trade-off energy consumption judgment method proposed in the ‘Public Building Energy Efficiency Design Standard (GB50189-2015)’, this study combines the BIM platform with an orthogonal simulation design adopting an experimental building construction project in a cold region of China, to thereby study the influences of different envelope structural parameters on cooling and heating loads. Then, this study analyzes the continuity and consistency of data exchange between different BIM-related software, through the modeling software Revit and the sustainable analysis software Ecotect. Finally, through the experiment, the critical factors of the optimized design of the building envelope are investigated and applied for the optimized design of the building thermal system. In addition, this study uses BIM to complete an energy efficiency design for a laboratory heating, ventilation and air conditioning (HVAC) system. According to the experimental results, the following conclusions can be drawn: this study proposes a BIM-based optimized design method of building thermal system, which complies with various Chinese building energy efficiency standards and polices and can be used as a reference for Chinese designers. Also, the issues and challenges in building consumption simulation based on BIM technology and the application of BIM technology in the design of building thermal HVAC systems are discussed, and the development trends of BIM technology in China are analyzed. Building thermal system and HVAC system design are the key points of building efficiency design. The optimized design method proposed in this paper is of great significance to this system design. The optimized design method can improve the design efficiency of designers while complying with China’s standards and policies. Therefore, it can save costs, improve the economic benefits of the projects and, at the same time, obtain an ideal building efficiency design.

An IoT Framework for Modeling and Controlling Thermal Comfort in Buildings

Humans spend more than 90% of their day in buildings, where their health and productivity are demonstrably linked to thermal comfort. Building thermal comfort systems account for the largest share of U.S energy consumption. Despite this high-energy cost, due to building design complexity and the variety of building occupant needs, addressing thermal comfort in buildings remains a difficult problem. To overcome this challenge, this paper presents an Internet of Things (IoT) approach to efficiently model and control comfort in buildings. In the model phase, a method to access and exploit wearable device data to build a personal thermal comfort model has been presented. Various supervised machine-learning algorithms are evaluated to produce accurate personal thermal comfort models for each building occupant that exhibit superior performance compared to a general model for all occupants. The developed comfort models were used to simulate an intelligent comfort controller that uses the particle swarm optimization(PSO) method to search for optimal control parameter values to achieve maximum comfort. Finally, a framework for experimental validation of the new proposed comfort controller that interactively works with the HVAC element has been introduced.

Application of Thermoeconomics in HVAC Systems

In order to achieve a sustainable society, the energy consumption in buildings must be reduced. The first step toward achieving this goal is to detect their weak points and analyze the energy-saving potential. to detect the units with higher consumption and cost. Exergy is very useful for analyzing pieces of equipment, systems or entire buildings. It measures not only the quantity of energy but also its quality. If the exergy is combined with economic analysis, this gives rise to thermoeconomics, and the system can be checked systematically and optimized from the perspective of economics. In this work, exergy methods and thermoeconomic analysis were applied to a building thermal system. Due to its complexity, it is necessary to adapt some concepts to translate the exergy application from industry to buildings. The purpose of this work is to overcome these shortcomings and to deal with energy-saving actions for buildings. To this end, a thermoeconomic study of a facility that covers the heating and domestic hot water (DHW) demands of 176 dwellings in Vitoria-Gasteiz (Basque Country) using two boilers and two cogeneration engines was analyzed. The irreversibility associated with each piece of equipment was quantified, and the costs associated with resources, investment and maintenance were calculated for each flow and, consequently, for the final flows, that is, electricity (11.37 c€/kWh), heating (7.42 c€/kWh) and DHW (7.25 c€/kWh). The results prove that the boilers are the lesser efficient components (with an exergy efficiency of 15%). Moreover, it is demonstrated that micro-cogeneration engines not only save energy because they have higher exergy efficiency (36%), but they are also economically attractive, even if they require a relatively high investment. Additionally, thermoeconomic costs provide very interesting information and underscore the necessity to adapt the energy quality in between the generation and demand.

A model for predicting the solar reflectivity of the ground that considers the effects of accumulating and melting snow

ABSTRACTSimulation tools for predicting building thermal performance and solar system performance must accurately calculate solar irradiance to surfaces of arbitrary orientation. This is imperative to correctly predict passive solar gains to buildings and to accurately estimate thermal and electrical production of solar collectors. In cold climates, where snow covers the ground for long periods of time, ground reflected radiation can represent a substantial fraction of the total incident irradiance to highly tilted and vertical surfaces (e.g. windows). A new model has been developed to improve the calculation of ground-reflected radiation in simulation tools. The model is based upon empirical observations taken at a measurement site in Ottawa (Canada), and has been validated using disjunct data from the measurement site, and with published data from two other sites in the USA. The model was found to increase the accuracy of ground reflectivity predictions for cold and humid climates.

Advanced Exergy Analysis in the Dynamic Framework for Assessing Building Thermal Systems.

This work applies the Dynamic Advanced Exergy Analysis (DAEA) to a heating and domestic hot water (DHW) facility supplied by a Stirling engine and a condensing boiler. For the first time, an advanced exergy analysis using dynamic conditions is applied to a building energy system. DAEA provides insights on the components’ exergy destruction (ED) by distinguishing the inefficiencies that can be prevented by improving the quality (avoidable ED) and the ones constrained because of technical limitations (unavoidable ED). ED is related to the inherent inefficiencies of the considered element (endogenous ED) and those coming from the interconnections (exogenous ED). That information cannot be obtained by any other approach. A dynamic calculation within the experimental facility has been performed after a component characterization driven by a new grey-box modelling technique, through TRNSYS and MATLAB. Novel solutions and terms of ED are assessed for the rational implementation of the DAEA in building energy installations. The influence of each component and their interconnections are valuated in terms of exergy destruction for further diagnosis and optimization purposes.