Building Heating Systems Research Articles

Dynamic Calibration Method of Sensor Drift Fault in HVAC System Based on Bayesian Inference.

Sensor drift fault calibration is essential to maintain the operation of heating, ventilation and air conditioning systems (HVAC) in buildings. Bayesian inference (BI) is becoming more and more popular as a commonly used sensor fault calibration method. However, this method focused mainly on sensor bias fault, and it could be difficult to calibrate drift fault that changes with time. Therefore, a dynamic calibration method for sensor drift fault of HVAC systems based on BI is developed. Taking the drift fault calibration of the chilled water supply temperature sensor of the chiller as an example, the performance of the proposed dynamic calibration method is evaluated. Results show that the combination of the Exponentially Weighted Moving-Average (EWMA) method with high detection accuracy and the proposed BI dynamic calibration method can effectively improve the calibration accuracy of drift fault, and the Mean Absolute Percentage Error (MAPE) value between the calibrated and normal data is less than 5%.

Distributed Optimal Control for HVAC Systems Adopting Edge Computing—Strategy, Implementation, and Experimental Validation

Internet of Things (IoT) has found increasing applications in industry, including the building automation field. Edge computing, a distributed computing paradigm using the computing resources of edge devices that are close to the sources of data, is an effective means for dealing with the network traffic caused by the centralized structure and rapid growth of IoT devices. This article proposes a distributed optimal control strategy for building heating, ventilation, and air conditioning systems adopting edge computing. The proposed distributed optimal control strategy is designed for the deployment on the smart sensor nodes in the IoT-enabled field control networks of building automation systems. A complex optimization task with high computational complexity is decomposed into several simple tasks, which can be handled by coordinating the agents implemented on the sensor nodes. In this manner, the computing resources of smart sensors are used collectively to optimize the subsystem operation locally. Compared with centralized optimal control strategies, the distributed form of the proposed strategy allows better generality and flexibility. A hardware-in-the-loop simulator is constructed as a realistic test environment for real control devices. Being implemented on a wireless IoT sensor network integrated in the simulator, the applicability and the performance of the proposed strategy are validated and evaluated. The experience and test results show that the IoT sensing network has the capacity to implement the distributed optimal control strategy and handle the decomposed optimization tasks effectively. The energy performance of the proposed distributed optimal control strategy is almost the same as that of the perfect solutions.

Optimization of heating efficiency of buildings above underground coal mines by infrared heaters

Purpose. To optimize the energy and economic efficiency of heating system of ground structures of coal mines with infrared heaters due to the rational choice of technical parameters of heating devices and their operating conditions, namely, the irradiation intensity of the floor q, thermal power of the heater Q, blackness degree of the floor surface and the height of installation H of infrared heaters. To achieve this goal, the task was to conduct theoretical and experimental studies on infrared heaters NL-12R of heating system of a building above underground coal mines during its thermal modernization. Methodology. At applying radiant heating systems, infrared heaters provide local heating of the working area of the buildings above underground coal mines. As a result, the necessary temperature conditions are maintained in the buildings above underground coal mines and there is a possibility of creating a local microclimate. A multifactorial experiment was performed taking into account the interaction of factors. The results of the study are presented in graphical and analytical forms. In addition, an analytical method was used to optimize parameters and operating conditions of the radiant heating system with infrared heaters NL-12R, and their number in the system of combined heating of buildings above underground coal mines is optimized. Findings. According to the experimental results, dependence of the relative floor temperature on the intensity of floor irradiation q, thermal power of the heater Q, blackness degree of the floor surface floor and the height H of infrared heaters location was determined. The results are presented in the form of graphs and nomograms, as well as approximated by their analytical equations. The annual economic effect of the optimal variant of combined heating system due to use the maximum number of infrared heaters NL-12R is 39.4 Euro/year provided that the installation of infrared heaters NL-12R with a power of Q = 1200 W in the number of 5 pcs. Originality. Optimization of energy and economic efficiency of heating system of buildings above underground coal mines by infrared heaters NL-12R, due to the rational choice of technical parameters of heating devices and conditions of their operation, was carried out by the analytical method. Practical value. Results of optimization of thermal and economic parameters of operation of the combined heating system of buildings above underground coal mines with installation of infrared heaters NL-12R with power Q = 1200 W proved the efficiency of combined heating of above-ground structures and the achievement of the annual economic effect of 39.4 Euro/year.

Study of the flat plate solar collector's efficiency for sustainable and renewable energy management in a building by a phase change material: Containing paraffin-wax/Graphene and Paraffin-wax/graphene oxide carbon-based fluids

Energy management in a building is to control and lessen the energy consumption, to decrease both costs and carbon emissions. Building-integrated thermal systems are adept to produce heat. Various scenarios have been planned to integrate heat into a building formation to enhance performance of entity system, supply indoor heating, and provide hot water. Concept backside Flat-plate Solar collector is that Sun warmth the set of tubes. This surface collects as much energy as preferable, and then water heats by this energy. This study aims to compare the Energy efficiency between phase change materials (PCMs) containing Paraffin-wax/Graphene and Paraffin-wax/Graphene Oxide carbon-based nanofluids for renewable, clean, green and sustainable energy systems such as Flat Plate Solar Collectors by applying these PCMs in Buildings heating systems. Results showed that, Thermal conductivity of both Graphene/P-wax and Graphene Oxide/P-wax was enhanced in comparison with Paraffin. Also, trend of Graphene showed better thermal performance.

ОЦЕНКА ВОЗМОЖНОСТИ СОЛНЕЧНОГО ТЕПЛОСНАБЖЕНИЯ С ИСПОЛЬЗОВАНИЕМ ВОЗДУШНОГО ТЕПЛОВОГО НАСОСА

Solar energy is one of the sources of renewable energy. However, during the cold season in Russia, the use of solar energy is difficult due to low outdoor air temperatures. The purpose of this article is to analyze the possibility of energy saving when using solar energy for the heating system of an individual residential building in Vladivostok. The latitude of the city is 43 °, but the estimated temperature for the design of heating is -22 ° C. This greatly complicates the use of solar heat. The possibility of accumulation of low-potential heat in a specially equipped outhouse (greenhouse) for conversion by a thermal air-water pump is analyzed in order to use it for heating needs in the future. Two constructions are considered as the finishing material of the extension: a two-layer polycarbonate with an air layer and an energy-saving double-glazed window. The calculation shows that in the coldest month is January, the potential of solar thermal energy is 14 %–37 % of the required heat demand, depending on the material used in the construction of the extension. In March and April, excess heat is generated. It can be used for hot water supply needs. Thus, for an individual residential building, the use of solar heat accumulated in a greenhouse extension is relevant as an additional source of heat for the heating system.

Using of heat thermal storage of PCM and solar energy for distributed clean building heating: A multi-level scale-up research

Solar energy coupled with electric heat storage is a promising energy saving technology for distributed building heating. Energy saving performance of this technology used in buildings has been widely investigated by prototype-scale experiments and numerical assessments. However, the commercial and economic feasibility of this technology during the operational phase of a real building have not been proven yet. This study conducted a comprehensive techno-economic analysis of the electric heat storage system coupled with solar energy installed in a three-story office building (2000 m2 heating area) in Tianjin, China. The electric heat storage system with the heat storage capacity of 1274.8 MJ was installed on the first floor of the building. The solar collector system with a total heat collection area of 160 m2 was installed on the roof of the building. Meanwhile, the long short-term memory was used to predict the heat gain of solar collector in order to adjust the quantity of storage heat of storage device. The results indicated that the prepared storage medium possessed an excellent thermal property with a total heat storage capacity of 448.8 kJ/kg within 50–150 °C. The average thermal efficiency of vacuum tube solar collector was 51.3%, its heat gain per square meter per day was 6.5 MJ. The use of the solar radiation prediction model stabilized thermal efficiency of heat storage reservoir at 95.8%. The specific operational cost of this system within an entire heating season was 15.6 renminbi (RMB)/m2/season. Distribute clean building heating (DCBH) system can save up to 61% of heating cost compared to the centralized heating. The results showed a great application potential of combination of electric heat storage and solar energy based on solar energy prediction for the heat dispatch of distributed building heating.

A hierarchical layout approach for underfloor heating systems in single-family residential buildings

The layout of an underfloor heating system determines its heating performance. Conventionally, the design is completed by qualified experts using professional software, but this method is inefficient and heavily relies on the experts’ previous experience. This paper proposes a hierarchical approach for the automatic design of underfloor heating systems aimed at residential buildings, and the underfloor heating layout problem is decomposed into a routing problem and coverage path planning problem. First, the routing problem is formulated as a shortest path problem in an appropriate routing graph and solved to provide the routing of pipes for further coverage. Subsequently, a depth-first layout algorithm is developed to recursively simplify the coverage path planning problem to obtain the basic rectangle layout, achieving uniform coverage of the given layout zone. Simulation results on two houses show that, unlike one popular commercial platform, the proposed approach generated feasible solutions in all 13 test instances. The new method improved the minimum temperature, average temperature, average percentage of nodes greater than 30 °C, and total energy efficiency by 3.18 °C, 0.79 °C, 3.23%, and 4.19%, respectively, with a lower variance on average. These improvements lead to a higher coverage rate, more even thermal distribution, and stronger robustness under flexible parameter selection.

Integration of Heat Pumps in Buildings and District Heating Systems—Evaluation on a Building and Energy System Level

The use of heat pumps in buildings is one of the best and often the only option for the decarbonization of the building stock. District heating seems a promising solution in urban areas and in existing buildings when the use of heat pumps is restricted and also technically and economically challenging (source exploitation, space restrictions, sound emissions, etc.). Heat pumps can be integrated in various ways in buildings and district heating systems: large central high-temperature heat pumps in district heating, medium-size heat pumps block- or building-wise or small heat pumps decentral apartment-wise. The best option depends on the individual district heating CO2 emissions and the electricity mix as well as on the perspective of the building owner versus that one of the district heating system and its future development. Austrian examples of district heating systems and different variants of integrating heat pumps are investigated in a comprehensive way by means of an energetic and environmental simulation-based analysis. This assessment includes a detailed investigation of the capabilities of the booster heat pump to increase the PV own-consumption and is also expanded to include various scenarios for the development of the electricity mix and the decarbonisation of district heating.

Data-Enabled Predictive Control for Building HVAC Systems

AbstractModel predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.

Nonlinear model predictive control for the space heating system of a university building in Norway

Space heating accounts for a significant share of a building's energy use; moreover, occupants' desires for better thermal comfort may further increase the energy use and peak load of the space heating system, as well as its total energy cost. The goal of this study was to use an optimal control technique: model-based predictive controller, to improve the energy and economic performance of space heating systems with satisfying indoor temperature. The proposed optimal controller incorporated the dynamic energy prices, the disturbances from weather and occupancy, as well as a nonlinear system dynamic model that considered the building thermal mass as thermal energy storage. The model-based predictive controller was tested by simulation on a university building in central Norway, and its control performance was compared to a conventional rule-based control approach, which is currently employed by the case system. The model-based predictive controller's effectiveness was demonstrated by a 2.8% reduction in heat use, 8.8% shaving in peak load, 3.8% saving in heating cost, and a 59% drop in indoor temperature violation. Furthermore, a sensitivity study revealed that the model-based predictive controller still maintained high energy and economic performance even under lightweight building constructions with decreased building thermal mass.

Method of Planning Repairs of the Installation including Building Waste

Repairs of water supply, sewage and central heating installations in residential buildings should be carried out systematically. However, very often, renovation dates are postponed, which results in installation failures. The failures of water supply, sewage and central heating installations, due to the currently used methods of masking them and running them as under-plaster and under-floor installations, are always connected with the damage and necessity of reconstruction of the building elements. As a result, renovation work has to be carried out to a greater extent and the amount of construction waste is much greater. The analysis of different renovation strategies of water supply, sewage and central heating systems in residential buildings made in traditional technology has been carried out. The article presents the results of the research on the effects of the postponement of the renovation works on the changes in the technical condition of the building and on the scope of renovation works. The aim of the research is to develop a method for planning repairs of the installation taking into account optimization of the amount of construction waste. The aim of the research is also to answer the question: To what extent does the postponed repair of water and sewage installations influence the amount of construction waste? In the proposed method, the Prediction of Reliability according to Rayleigh Distribution (PRRD) model is used. The results of the research indicate the necessity of conducting the renovation works of the installation in a timely manner due to the increasing amount of construction waste and the introduced reduction of its amount with the increase of the recycling rate.

Influence of a Better Prediction of Thermal Satisfaction for the Implementation of an HVAC-Based Demand Response Strategy

Building system operation faces the challenge of reducing energy use and implementing a demand response, which can be defined as a temporary modification in energy loads affecting dynamic energy price and reliability information. The heating, ventilation, and air-conditioning (HVAC) system in buildings provides an opportunity for implementing demand response strategies due to the thermal inertia in building zones. However, an HVAC-based demand response is not a prevalent strategy in actual facility management due to the lack of understanding among building operators of their facilities and occupants. Herein, we focus on developing a better understanding of the occupant side by obtaining a reliable prediction of occupants’ thermal satisfaction. We evaluate the prediction performance of a probabilistic model provided in our previous paper using a case study with a subset of the ASHRAE Global Thermal Comfort Database II. The influence of a better prediction of thermal satisfaction on the implementation of the HVAC-based demand response strategy is further discussed. The conventional method overestimates productivity deterioration due to changes in the thermal environment, making it challenging to implement an HVAC-based demand response strategy aggressively. A robust prediction model using a probabilistic approach can solve this problem, allowing building operators to adopt an aggressive stance for implementing a demand response. The results of this study offer fresh insight into the impact of a probabilistic model in the prediction of thermal satisfaction for establishing an HVAC-based demand response strategy.

An integrated approach of building information modelling and life cycle assessment (BIM-LCA) for gas and solar water heating systems

Buildings are responsible for the energetic consumption and potential greenhouse gas emissions during their life cycle. Water heating system contribute to a building’s energetic consumption, mainly in residential units, throughout the building’s operational phase. Variability in energy sources, reservation and distribution systems of hot water along with the types of construction materials used in these building systems are key decisions to make in the initial design phases of a building project. Often, the definition of the most appropriate water heating system for a building is made via a technical-economic decision. However, the decision is rarely based on natural resource consumption and environmental impact generation throughout the life-cycle of the heating systems and of buildings as a whole. This study presents an application of a specific environmental management tool, based on an integrated Building Information Modelling (BIM) and Life-Cycle Analysis (LCA) method for selection of hot water systems, during the early design building phase. The proposed approach is implemented in the pre-operational phase, in order to enable decision makers to appreciate the resulting environmental performance of water heating systems in buildings. The applicability of the framework is tested via a comparative study of solar heating water systems and natural gas heating water systems for a residential multifamily building to be constructed in Rio de Janeiro, Brazil. For the indicators damage to human health and damage to ecosystem, results indicate that the greatest impact on global warming comes from the natural gas heating system, while for solar heating, free particulate matter was the highest negative contribution. The operation phase for the natural gas system was highest for climate change while for solar heating system, it was the fresh water that was impacted the most during the pre-operational phase of the system’s use.

ANALYSIS OF THE INFLUENCE OF THE INTERNAL HEAT CAPACITY OF A PUBLIC BUILDING ON THE THERMAL COMFORT PARAMETERS OF THE PREMISES DURING THE OPERATION OF THE HEATING SYSTEM IN ALTERNATING MODE

In the climatic conditions of Ukraine, which are characterised by a long heating period, considerable energy requirements for heating lead to an increase in energy efficiency requirements. A substantial reduction in the energy consumption of buildings while ensuring comfort conditions will be facilitated by the inclusion of a model of human thermal comfort in the complex “heat source – fencing” system. The purpose of this study was to find the factors affecting the internal heat capacity and, accordingly, the thermal inertia of the building and further take these factors into account upon assessing the thermal condition and parameters of thermal comfort of building rooms. The object of this study was the educational and administrative building of the National University of Life and Environmental Sciences of Ukraine. Many studies were carried out, namely full-scale measurements of heat flows and temperatures on the surfaces of samples of the building’s wall structure were carried out in a special climate complex that allows artificially creating external and internal thermal conditions of premises. It was found that the insulation of the structure with a layer of expanded polystyrene PSB-15, 100 mm thick, reduces heat losses through the wall panel by almost half. An algorithm for controlling the heat release process was developed, considering the internal heat capacity of the building. Compared to the “linear” dependence, this allows more accurately adjusting the schedule of heat carrier release to the heating system of a public building during the introduction of the alternating mode of its operation. The temperature deviation range is reduced by 4–6 °C, which allowed saving up to 10-12% of the consumed heat energy for the heating needs of the research object, provided that the normalised values of the internal temperature of the premises are maintained. Intermittent operation of the heating system of public buildings, the expediency of which is justified in this study, can be recommended for implementation in the structures of higher educational institutions of Ukraine.

A water and greenhouse gas inventory for hygroscopic building-scale cooling tower operations

Heating, ventilation, and air conditioning (HVAC) systems for large buildings often use water for cooling. The U.S. Department of Defense funded the demonstration of a novel hygroscopic HVAC cooling tower technology with the goal of reducing water usage. We quantified direct and indirect water usage and greenhouse gas (GHG) emissions to analyze the tradeoffs associated with transitioning from a conventional wet-cooling HVAC tower to the hygroscopic system. We quantified GHG emissions for electricity consumed directly by the building-scale cooling system and the indirect electricity associated with water and wastewater conveyance and treatment. We found that the GHG emissions impact of increased electricity consumption in the building-scale cooling system is greater than the GHG emissions impact of indirect energy savings from the decrease in water usage, resulting in a net increase in GHG emissions. The indirect water consumption associated with cooling water for electricity generation was comparatively low when compared to direct water usage volumes that were reduced by switching to the hygroscopic system. The hygroscopic system showed promising water savings ability that is best suited to regions with extreme water scarcity, high water sourcing and treatment energy intensity, and/or when the electricity is sourced from low-carbon sources.

A general multi agent-based distributed framework for optimal control of building HVAC systems

Distributed control is recognized as an effective means for improving energy efficiency of building heating, ventilation, and air conditioning (HVAC) systems. However, existing distributed control methods ignore the physical correlations between components and zones, resulting in heavy computation load for system-level control. This paper proposes a hierarchical architecture with multi layers to describe the tree-like structure of various HVAC systems. It divides the conventional big distributed optimization task into several local optimization tasks, according to the hierarchical structure from the top layer to the bottom layer. A hierarchical alternating direction multipliers method is presented to solve the overall optimization problem layer by layer in a recursive manner. An improved Nesterov acceleration technique is further introduced to improve the convergence rate. Two cases with different cooling load conditions are conducted on a simulated complex HVAC system to validate the performance of the proposed framework by comparing with three conventional control methods. Results show that it achieves a significant energy saving of 4.05% (275.95 kWh) in case 1 and 5.33% (554.99 kWh) in case 2 compared with the centralized control method. Moreover, it can reduce the computation time by 69.55% averagely in the two cases compared to the conventional distributed control method.

Implementation of a strategy for low-temperature operation of radiator systems using data from existing digital heat cost allocators

Low-temperature district heating (LTDH) networks can integrate sustainable energy sources and waste industrial heat towards decarbonisation goals by 2050. LTDH networks can be realised through the low-temperature operation of heating systems in buildings. However, the low-temperature operation of heating systems is obstructed by inefficient radiator control by end-users or other technical errors. This study investigated the implementation of a strategy for low-temperature operation of radiator systems by calculating the minimum supply temperature and using an innovative treatment of data from electronic heat cost allocators to identify radiators not in use and locate the critical apartments with higher heat demands. According to the results, the low-temperature operation of radiator systems is possible. Although, the minimum supply temperature should be calculated based on the higher heat demand of the critical apartment identified to avoid complaints regarding poor thermal comfort. An energy weighted average supply temperature of 55 °C can be achieved, resulting in an average energy weighted return temperature of 31.3 °C in the system. Testing of a reduced supply temperature in the building case highlighted the existence of critical apartments. The investigation highlighted that the increased heat loss to the poorly heated neighbouring apartments heavily influences the critical apartments.

Estimating the Potential of Building Integration and Regional Synergies to Improve the Environmental Performance of Urban Vertical Farming

Vertical farms have expanded rapidly in urban areas to support food system resilience. However, many of these systems source a substantial share of their material and energy requirements outside their urban environments. As urban areas produce significant shares of residual material and energy streams, there is considerable potential to explore the utilization of these streams for urban agriculture in addition to the possibility of employing underutilized urban spaces in residential and commercial buildings. This study aims to explore and assess the potential for developing more circular vertical farming systems which integrate with buildings and utilize residual material and energy streams. We focus on the symbiotic development of a hypothetical urban farm located in the basement of a residential building in Stockholm. Life cycle assessment is used to quantify the environmental performance of synergies related to energy integration and circular material use. Energy-related scenarios include the integration of the farm's waste heat with the host building's heating system and the utilization of solar PV. Circular material synergies include growing media and fertilizers based on residual materials from a local brewery and biogas plant. Finally, a local pick-up system is studied to reduce transportation. The results point to large benefits from integrating the urban farm with the building energy system, reducing the vertical farm's GHG emissions up to 40%. Synergies with the brewery also result in GHG emissions reductions of roughly 20%. No significant change in the environmental impacts was found from the use of solar energy, while the local pick-up system reduces environmental impacts from logistics, although this does not substantially lower the overall environmental impacts. However, there are some trade-offs where scenarios with added infrastructure can also increase material and water resource depletion. The results from the synergies reviewed suggest that proximity and host-building synergies can improve the material and energy efficiency of urban vertical farms. The results provide insights to residential building owners on the benefits of employing residual space for urban food provisioning and knowledge to expand the use of vertical farming and circular economy principles in an urban context.

CntrlDA: A building energy management control system with real-time adjustments. Application to indoor temperature

Rule-Based Control (RBC) and Model Predictive Control (MPC) have been traditionally used to control building heating, ventilation and air conditioning (HVAC) systems. They, however, present shortcomings when faced with efficiently controlling these systems at a larger level. Reinforcement Learning (RL) has recently emerged as a viable alternative, showing promising results compared to previous methods, but still having some difficulties with untrained situations or sudden changes. CntrlDA is our proposal on improving the RL formulation by coupling it with data assimilation (DA), a technique commonly used in numerical weather prediction. Our battery of experiments, in a building simulation environment, shows that training a RL control agent with DA and external data, leads to better performance than training the agent using only the simulation data. The RL control agent with DA maintains the temperature range 15.6% more often than the RL control agent without DA. It is also shown that by including a DA stage in the control process, the agent better deals with unexpected events (which are common in real-life systems and particularly in building energy control scenarios). We show that it maintains the range 15.4% more often than the system without DA with no significant added cost of resources.

Distributed model predictive control for central heating of high-rise residential buildings

Central heating system faults affect building energy consumption and indoor thermal comfort significantly. To aim at the balance between thermal comfortable and energy-saving of the heating system for high-rise residential buildings, this paper proposes a method for the central heating system of high-rise residential buildings based on distributed model predictive control. The method analyzes the coupling factors between adjacent rooms’ temperature. Based on the state space method, a multivariable indoor temperature model is established and verified. The distributed model predictive control method is used to control and optimize the indoor temperature, and the load distribution of the circulating water pump in the heat exchange station is optimized according to the predicted heat demand. The results demonstrate that the indoor temperature after distributed model predictive control can stable near the set value. Compared with the centralized control methods, the proposed methodology can reduce energy consumption by 14.28%. Meanwhile, the efficiency of water pumps is increased by 16.74% after using the distributed control strategy.