Building Heating Systems Research Articles

A mixed integer linear programming-based simple method for optimizing the design and operation of space heating and domestic hot water hybrid systems in residential buildings

The hybridization of energy systems is based on the combined integration of both renewable and non-renewable technologies and thermal energy storage. These hybrid installations improve cost effectiveness and energy efficiency when they are correctly designed and the operation strategy is suitable. Despite the relevance of achieving the optimal configuration, sizing and control strategy of hybrid thermal systems, there is no simple and generic methodology which allows this type of installations to be optimized in the project phase. In response to this issue, in this work, a mixed integer linear programming-based simple model is carried out with the aim of obtaining the optimal design, sizing and operation of thermal energy systems in residential buildings. To do so, a superstructure is defined that includes the main technologies commercialized for thermal energy systems in buildings. Technical, economic, environmental and legal constraints are determined in the proposed generic model. In order to validate the method, it is applied to a central space heating and domestic hot water installation of a residential building located in a cold climate in Spain. Optimal solutions are obtained considering three different perspectives —economic, environmental and multicriteria— and are compared to the current installation. According to the results, the overall cost of the economic optimal configuration is reduced by 15%, whereas the greenhouse gas emissions decrease by 56% in the environmental optimal solution. It is thus demonstrated that the proposed generic and simple model is a useful tool for determining the optimal hybridization of the plant and for analysing the technical, economic and environmental feasibility of these systems in the project phase.

Daytime Radiative Cooling: A Perspective toward Urban Heat Island Mitigation.

Traditional cooling and heating systems in residential buildings account for more than 15% of global electricity consumption and 10% of global emissions of greenhouse gases. Daytime radiative cooling (DRC) is an emerging passive cooling technology that has garnered significant interest in recent years due to its high cooling capability. It is expected to play a pivotal role in improving indoor and outdoor urban environments by mitigating surface and air temperatures while decreasing relevant energy demand. Yet, DRC is in its infancy, and thus several challenges need to be addressed to establish its efficient wide-scale application into the built environment. In this Perspective, we critically discuss the strategies and progress in materials development to achieve DRC and highlight the challenges and future paths to pave the way for real-life applications. Advances in nanofabrication in combination with the establishment of uniform experimental protocols, both in the laboratory/field and through simulations, are expected to drive economic increases in DRC.

Co-optimization of passive building and active solar heating system based on the objective of minimum carbon emissions

Co-optimization of passive building and active solar heating system based on the objective of minimum carbon emissions

Insulated concrete form foundation wall as solar thermal energy storage for Cold-Climate building heating system

Employing green energies for building energy sector decarbonization has captured the world’s attention in the current century. However, the imbalance between energy demand and availability necessitates designing reliable and cost-effective energy storage systems. The present study aims to propose an innovative building-integrated solar thermal storage method using insulated concrete form (ICF) foundation walls for residential buildings in cold climates such as that of Canada. Surplus solar thermal energy is stored inside the ICF wall, which has a high thermal capacity and mass and is integrated into the building envelope. The ICF wall and solar thermal collectors are coupled with a water-to-water heat pump to meet building space heating load and domestic hot water demand. Different configurations integrating the ICF wall are modeled and simulated in TRNSYS software to perform a yearly transient analysis. It is shown that a system with ICF walls has an 11% higher solar fraction (SF) than a similar system with a large water thermal storage tank. By replacing the solar thermal collector with a hybrid photovoltaic/thermal collector, the overall system solar fraction can increase to 20% above that of a similar system with a large water thermal storage tank system. An ICF-based system with a 16 m2 solar collector can completely cover nine months of space heating and domestic hot water load for a single-family house in London, Ontario. A detailed sensitivity analysis shows that the proposed ICF-based systems achieve an optimum solar fraction at high tilt angles (65-70°), unlike a similar design with an extensive water thermal storage tank system. Because of their ability to achieve a high SF at high tilt angles, ICF systems are suitable for vertically mounted solar systems typically required by high-rise buildings due to limited roof area. As part of the building envelope, ICF foundation walls have no additional cost and can be considered a feasible strategy for reducing the residential sector’s carbon footprint..

An uncertainty-tolerant robust distributed control strategy for building cooling water systems considering measurement uncertainties

Distributed control is receiving increasing attention in the domain of building heating, ventilation, and air-conditioning (HVAC) systems, due to its charactristics of high generality, scalability and flexibility. However, the existing studies rarely took the impacts of measurement uncertainties into consideration which are common and inevitable in HVAC systems. It would cause the distributed control strategies fail to achieve the desired optimization performance. This paper therefore proposes an uncertainty-tolerant robust strategy to reduce the impacts of measurement uncertainties on the performance of distributed control of building cooling water systems. Firstly, the uncertain parameters are modeled as probability distributions determined by sensor biases and measurement noises. An improved adaptive Monte-Carlo method is then presented to estimate the expected performance (i.e., the expected optimization objective) under multiple measurement uncertainties. Finally, a multi agent-based structure is proposed for HVAC systems to perform the robust distributed control task. Research results indicate that the proposed strategy can achieve similar optimal control results as the ideal uncertainty-free strategy in the presence of measurement uncertainties. The average deviation of optimal control results is reduced from 0.55 K to 0.28 K in comparisons to the conventional distributed control strategy without considering measurement uncertainties. The deviation of optimal control results can still be within 0.6 K even when the degree of measurement uncertainty is nearly 30%.

Coupling a road solar thermal collector and borehole thermal energy storage for building heating: First experimental and numerical results

The adoption of more efficient technologies that integrate renewable resources for heating buildings is a key action for increasing sustainability in the residential sector in the European Union. Borehole thermal energy storage and road thermal collector systems, which have mainly been integrated in colder countries to develop renewable systems, aimed at preventing the freezing of road surfaces in winter, could also be used in warmer countries to develop sustainable heating systems for buildings. In this experimental–numerical study, the possibility of integrating these two systems for the heating of buildings located in the Mediterranean region is investigated for the first time. To this end, a pilot plant was built at the facility test site of the University of Palermo, with the aim of demonstrating the possibility of storing solar energy in summer and recovering it in winter. A new method is proposed to characterize both the thermal conductivity and diffusivity of the different materials in the design phase of the plant. The results of simulations conducted with a validated numerical model show that the proposed system, characterized by an average annual collector efficiency of 10% and a seasonal storage efficiency of 80%, can reduce the length of borehole heat exchangers by about three times compared with a conventional geothermal heat pump plant.

Comprehensive evaluation and analysis of a nearly zero-energy building heating system using a multi-source heat pump in severe cold region

Comprehensive evaluation and analysis of a nearly zero-energy building heating system using a multi-source heat pump in severe cold region

Distributed MPC-ILC Thermal Control Design for Large-Scale Multi-Zone Building HVAC System

With building heating, ventilation, and air conditioning (HVAC) systems accounting for 50% the energy consumption in the building sector in the United States, there is a need to develop and implement optimal control strategies for building HVAC systems that reduce energy consumption while achieving thermal comfort for users. In the author's previous work, an integrated model predictive control (MPC) and iterative learning control (ILC) design approach was presented that took advantage of both controllers. It did not rely on model accuracy compared to conventional MPC and reduced the learning curve compared to conventional ILC. Albeit the previous numerical results showed fast convergence in most of the VAV subsystems, the gap between room air temperature and its set point remains noticeable in several zones. This paper further proposes an approach to extend the centralized MPC-ILC controller to take into account the distributed factor and the spatial distribution of the thermal zones of the VAV system. The improved control strategy allows all VAVs to interact with each other and contribute collectively to the overall convergence of the whole system. The proposed controller is implemented on a thirty-two zone VAV reheat system and compared with different controllers, including our previous MPC-ILC design. The outcomes show that the proposed controller results in faster and closer convergence among all zones even when the number of subsystems is large.

Low-cost optimization of geothermal heating system with thermal energy storage for an office building

Using geothermal energy to meet office building heating is an effective measure to reduce energy consumption. The heat demand of an office building features a significant intermittent profile; however, the geothermal submersible pump should not be started and stopped frequently within a heating season, which makes it difficult to match the continuity of heat source with the intermittent heat load and thus resulting in a serious waste of geothermal resources. The utilization of thermal energy storage (TES) technology can solve the problem of the mismatch between the supply and demand of the geothermal heating system for an office building. In this paper, aiming at obtaining a low-cost system while promoting the energy supply and demand balance, the geothermal heating system with TES for an office building located in Cangzhou, Hebei province, China is optimized based on a two-stage approach considering the time-of-use tariffs. The results show that the geothermal heating system including TES can effectively meet the heat load of office buildings without a backup facility. Compared with the benchmark system, although the addition of TES leads to an increase in the system capital investment, the system operation cost reduces by 38.10% or so under the optimal operation strategy. Besides, reasonable operation control of the TES as well as the heat pump is the key measure to reduce the system cost. Accordingly, the optimal operation strategy and corresponding equipment selection parameters of the geothermal heating system with TES are determined.

Design and thermal performance evaluation of a new solar air collector with comprehensive consideration of five factors of phase-change materials and copper foam combination

The low thermal conductivity of phase-change materials (PCMs) reduces the heat storage capacity, heat release efficiency, heat peak migration capacity, and heat collection efficiency of PCM-based solar air collectors (SACs) in building heating systems, thereby affecting the comprehensive heating requirements of building users. To solve this problem, in this study, a new SAC was designed with PCMs and copper foam (Type 2: PCMACFC-SAC model) by comprehensively considering five factors, namely thermal conductivity, heat storage capacity, heat release efficiency, heat peak migration capacity, and heat collection efficiency, based on a pure PCM-based SAC (Type 1: PCM-SAC model). Thereafter, the two models were compared. Comparative results of the thermal-performance evaluation index revealed that the thermal conductivity of the Type 2 test block was 2.2–2.3 times that of the Type 1 test block, indicating significantly improved thermal conductivity of the copper foam and PCM composite test block. Although the PCMACFC-SAC displayed better heat storage speed rate, time, and quantity, its nighttime heat release time was approximately 19.64% less than that of the PCMs-SAC; PCMACFC-SAC had a lower output temperature and smaller temperature fluctuation range over the solar day, which can increase indoor thermal comfort. The daily average heat collection efficiency of the PCMACFC-SAC decreased by 12.77%, indicating a stronger heat peak migration ability, which can transfer a greater amount of heat energy to be released at night.

Physics-guided LSTM model for heat load prediction of buildings

The thermal process of heating buildings is characterized by the multivariable, nonlinear, and large time lag. Accurately predicting building heat loads is crucial for regulating building heating systems. However, the current black-box method of heat load prediction has challenges such as extensive data usage and weak integration with the building's heat transfer process. To address these challenges, a physics-guided long short-term memory (LSTM) model for dynamic heat load prediction in the next seven days based on data-driven and mechanism analysis was proposed. The data for the model were collected from in-situ measurement and numerical simulation. Specifically, the thermal mechanism was introduced to the training loss function of LSTM and the selection of input variables. The influence of the adaptive data time step on prediction accuracy was investigated. The main conclusions obtained from the current data in this paper are drawn as follows. 1) Refining solar radiation intensity calculation for each orientation reduced mean absolute percentage error (MAPE) of heat load prediction results by 0.02%–1.8% compared to horizontal solar radiation intensity. 2) Improving the training loss function decreased MAPE of heat load prediction results by 0.04%–0.43% compared with the original loss function. 3) Adaptive data time step lowered the MAPE of heat load prediction results by 0.08%–1.8% compared with fixed time step. 4) Mean absolute errors corresponding to those three aspects decreased by 25 kJ–558 kJ, 6 kJ–157 kJ, and 41 kJ–1881 kJ. In the future, wider promotion and application of the prediction model will lead to better performance and be more beneficial to energy saving and carbon reduction of the heating system.

Heating System Control with Neural Network

Building performance has significant impact on humans’ life and ecology as buildings account for 39% of total greenhouse gas emissions and consume about 40% of total global energy consumption. Smart building control is one of the key points to archive high energy efficiency. Each year, the complexity of building state control grows due to the increase in the number of controlled elements that are used to achieve better indoor climate. Therefore, in the manual analysis and implementation of the building control program, an error can easily appear due to the human factor. Artificial intelligence (AI) algorithms could be used as an alternative solution as they could evaluate building dynamics independently. One of strategies for automatic building control adaptation to its dynamic is model based predictive control where neural network is used for different control strategies evaluation. Performance of such control technique is highly dependent on control strategies evaluation accuracy. To achieve top accuracy, several hyperparameters of neural network could be tuned as well as data set for specific construction must be prepared. Preparation of data set could be a problem as random control of building for generation of dataset could be not unacceptable for building users as well as it could damage construction.In this paper authors process optimization of experimental building heating system control algorithm to achieve smaller fluctuations of temperature indoors. For dataset generation were used several data from weather station as well as heating system parameters and temperature indoors while building was controlled by thermostat with build in PID regulation. For evaluation of building dynamics were used temporal convolutional neural network. To achieve high accuracy results of control strategies evaluation, several hyperparameters of neural network were tested. Finally resulting model were tested on physical building. Results indicate that in some cases developed control model could prevent temperature fluctuations which could be caused by limits of heating system power.

Climate-neutral municipal building stock - life cycle assessment of large residential building stocks based on semantic 3D city models

This publication presents the results of applying Life Cycle Assessment (LCA) methods to the entire residential building stock of Germany's third largest city, the City of Munich. The focus of the LCA is on the technical components of the building heating system. For the analysis, we use our developed tool urbi+, which takes 3D city models in the CityGML standard enriched with building specific information as input parameters. Thus, we investigate both the status quo of Munichs' building stock and two different development scenarios with the goal of climate neutrality by the year 2035. Development scenario 1 focuses on the use of heat pumps and scenario 2 on the expansion of the district heating network. The year 2035 has been defined by the City of Munich as the official goal for achieving climate neutrality. Within the scope of the assessment, we address the parameters of energy demand, greenhouse gas emissions and costs. The results show a multidimensional assessment space between these parameters and how ambitious it is to achieve the goal of climate neutrality by 2035. The results are intended to help political and urban planning decision-makers validate climate targets and make informed decisions about measures to achieve climate neutrality.

Residential Buildings Heating and Cooling Systems: The Key Role of Monitoring Systems and Real-Time Analysis in the Detection of Failures and Management Strategy Optimization

Nineteen percent of global final energy consumption is used to generate electricity and heat in buildings. Therefore, it is undisputed that the building sector needs to cut consumption. However, this reduction needs to be driven by data analysis from real building operations. Starting from this concept and with the aim of proving the benefits deriving from the installation of a monitoring system in a real operating environment, in this work a monitoring system has been installed to monitor the centralised heating and cooling system of a residential building composed of 57 residential units. The data acquired from the installed sensors are collected and subsequently analysed in an ad hoc tool to detect anomalies, performance decay, malfunctions, and failures of the machines, as well as to understand if the implemented management strategy is appropriate in terms of energy and cost savings. The results show the key role of the data acquired by the monitoring system and analysed by the developed tool in terms of ability to detect failures and malfunctions in both the heating and cooling modes, as well as to help both in finding the proper management strategy and in identifying the performance deviation precursors of machine failure.

A novel methodology based on artificial intelligence to achieve the formost Buildings’ heating system

This study investigates the possibility of creating an artificial intelligence system in the form of a predictive model that can recommend the foremost building heating system and their optimum performances for a building based on desired life cycle costs by the project manager and the permitted CO2 emission rate. Accordingly, the developed intelligent system would be able to recommend the most appropriate heating system when the building specifications are given to the system. Therefore, experts' knowledge can be substituted to recommend the foremost heating systems for a building. In this framework, a database is produced using a Multi-objective Particle Swarm Optimization (MOPSO) for the buildings with various numbers of floors, built areas, and the number of residents. Pareto front data for each building is fitted with a curve, and the coefficients of the curves are given to a Multi-Layer Perceptron (MLP) to create a predictive model for optimum performances. Moreover, all optimization data are assigned to a Support Vector Machine (SVM) to determine the classification lines between different heating systems with the lowest risk. The coefficients of separator lines are given to another multi-layer perceptron to create the predictive model for available HVAC systems. The models’ performance was improved by evaluating different structures for the Artificial Neural Networks (ANN) and implementing single-objective models instead of multi-objective ones. The proposed models' performance is acceptable considering the requirements of ASHRAE guideline 14.

An Overview of the State of the Art and Challenges in the Use of Gelling and Thickening Agents to Create Stable Thermal Energy Storage Materials

Building cooling and heating, solar-powered energy production, energy recovery, and other energy-consuming industries have all seen an increase in the use of cold/hot latent thermal energy storage (LH-TES). Through energy recovery, LH-TES that uses phase-change materials (PCMs) as a storage medium helps to close the energy supply and demand gap and raises the possibility of energy savings. However, the stability, thermal, physical, and chemical properties of the PCM play a major role in how effectively it can be used. In recent years, adding gelling and thickening agents (GTAs) has gained popularity apart from the nanoparticles (NPs) and nucleating triggers (NTs), particularly for the creation of stable PCMs. Therefore, the current work’s goal is to provide an overview of how GTAs are used in the process of developing reliable PCMs for TES applications. It has been found that using GTAs not only increased stability but also decreased sedimentation, leakage, and the supercooling degree (SCD). It was noted that the addition of a GTA with a weight percentage of 2–15% resulted in excellent stability with a negligible leakage rate and latent heat reduced by 3.6–35% after only 200 cycles. Furthermore, PCMs for solar-thermal and building heating systems in the medium-temperature range (21–61 °C) were mostly studied for their performance with GTAs, but no study for a cool TES application was reported. Most works have studied inorganic PCM components with GTAs, and a few reports are available for paraffin. However, the GTA blending resulted in reduced thermal performance due to a decrease in thermal conductivity, latent heat, and a rise in viscosity. Further, NTs and NPs with small amounts were seeded into the PCM-GTA for eradicating the SCD with enhanced TC and accelerated energy transfer.

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.

ANALYSIS OF HOT WATER HEATERS FOR INDIVIDUAL HEATING STATION WHEN THE TEMPERATURE CHART OF THE DISTRIBUTION HEAT NETWORK IS CHANGED

The aspects of the functioning of centralized heating systems of residential urban districts after additional insulation of this district existing buildings were considered. The influence of the heat carrier temperature in the district distribution heating network on the heat transfer surface of the heat exchangers that are installed in the individual heat station schemes for hot water supplying was analyzed. Estimates were made for single-stage connection of the heat exchangers to the heating systems. When determining the heat transfer surface, known criterion equations for plate heat exchangers of heat supply systems were used. The range of changes in the heat transfer surface of hot water heaters and the consumption of network water is determined depending on the selected temperature chart of the distribution heat network. The proposed recommendations can be useful in developing a heat supply quality control schedule for a district buildings heating systems in a district when the building thermal insulation process is completed. Possible approaches to the thermal design of heat exchangers of the hot water supply system for insulated buildings individual heating stations are considered when changing the temperature chart of the district heating supply system of the residential urban districts. Estimates were made to understand the influence of the reduced temperature chart for heat supply of the districts on the network water consumption for hot water heating and the surface of heat exchangers of insulated buildings heaters. It was shown that the increase in network water consumption for building hot water supply can be 1.5 to 3 times higher. The analysis presented for the surface of the heat exchangers of heating installations for insulated buildings hot water supply shows that it can be even double increase of heaters heat transfer surface depending on the temperature chart. The results obtained should be taken into account when planning the temperature chart of the heat carrier for a distribution heating network. Keywords: district heating, heat supply systems reforming, individual heating station, hot water heaters, plate heat exchanger, heat exchanger thermal design.

A review of data-driven fault detection and diagnostics for building HVAC systems

With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.

Evaluation of the Smart Readiness Indicator for Educational Buildings

The Smart Readiness Indicator (SRI) is an assessment scheme for the intelligence of buildings, which was introduced by the European Commission in the directive for the Energy Performance of Buildings in 2018. Since its introduction, many activities related to the maturation and employment of the SRI have been initiated. One of the adaptation needs of the SRI, revealed through public consultation with relevant stakeholders, is the requirement for a tailored SRI for different types of buildings. The aim of this study is to analyze possible scenarios to optimize the smartness performance, as addressed by the SRI score, in educational buildings. The subject of this study concerned campus buildings of the Kaunas University of Technology, in Lithuania. For the definition of the SRI, the calculation sheet developed by the European Commission was used. The effect of the improvements in the smartness performance of buildings on their energy efficiency was examined with the use of a whole-building, BIM-based energy assessment tool (IDA-ICE). The findings of this study revealed that despite the improvement in the automation and control levels of the building heating system, the maximum SRI values achieved deviate significantly by a high-smartness level. This study revealed the importance of services at a city level towards achieving the optimal smartness levels at a building unit level. It also delivered useful findings related to the linkage between energy and smartness performance of a building. The policy implication of the study findings also covers topics relevant to utilities management at a district level, as well as on the need for tailored SRI services catalogs for different types of buildings.