Operation Of Heating System 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%.

Enhancing the detection performance of a vision-based occupancy detector for buildings

Occupant behaviour is one of the key parameters that significantly impact the operation of heating, ventilation and air-conditioning (HVAC) systems and the energy performance of buildings. Detailed occupancy information can improve HVAC operation and utilisation of building spaces. Strategies such as vision-based occupancy detection and recognition have recently garnered much interest. This study investigated the performance of a vision-based deep learning detection technique for enhancing building system operations and energy performances. The model used was the Faster region-based convolutional neural network with Inception V2. Two occupancy detection model configurations were developed, tested and evaluated. Both models were analysed based on the application of the detector within a selected case study building, along with an evaluation based on different evaluation metrics. The results suggest the occupancy detector using model 1 provided an overall accuracy of 95.23%, while model 2 (the occupancy activity detector) provided an accuracy of 89.37%. In addition, the F 1 score, which represents the harmonic mean between the precision and recall values, was 0.9756 for model 1 and 0.8298 for model 2. Building energy simulation was used to evaluate and compare the impact of such an approach on indoor occupancy heat gains. The study highlighted the potential of the detection approaches, but further development is necessary, including optimisation of the model, full integration with HVAC controls and further model training and field testing.

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.

Optimal Scheduling of Regional Combined Heat and Power System Based on Improved MFO Algorithm

Due to the inflexibility of cogeneration power plants and the uncertainty of wind power production, the excess power of the distribution network brings challenges to the power grid operation. This paper introduced an improved moth-flame optimization algorithm to meet the challenge of energy complementary dispatching. The proposed algorithm adopts three effective strategies, namely inertia weight, unified initialization, and the spiral position update strategy, which maintains a strong global search ability and a potent compromise between global and local search. The effectiveness of the proposed method was evaluated by benchmark functions. Furthermore, the proposed method was applied to combine heat and power system operation problems and economic dispatch in light load and wind power unpredictability. In order to verify the robustness of the algorithm and solve the complex constraints of power systems under extreme conditions, three different cases had been discussed. The experimental findings indicate that the proposed algorithm shows better performances in terms of convergence speed, ability to escape from a local optimum solution, and population diversity maintenance under different complexity conditions of engineering problems.

Evaluation and Improvement of the Comprehensive Cost-Benefit of Investment and Operation of New Energy Heating System

The on-spot consumption scheme of new energy can improve the utilization efficiency of energy and provide references for the transformation from the conventional regional coal heating mode to the more efficient electric heating mode. To figure out the economic benefit brought by the new energy heating mode, it’s necessary to evaluate its cost-benefit and construct reasonable model to study the dynamic market response. Therefore, this paper aims to evaluate the comprehensive cost benefit of the investment and operation of new energy heating (NEH) system and find ways to improve the said cost benefit. At first, this paper built an investment and operation model for the said NEH system, and constructed an objective function with the minimization of new energy disuse rate as the objective in the commissioned operation scheme for new energy on-spot consumption system connected with large electric heating boiler and large-capacity heat accumulator. Then, the paper gave the structure of the NEH system, and analyzed the main participants, investment and operation modes, and economic benefit of the NEH system. At last, corresponding experimental results and analysis results of an example case were given, which had verified the effectiveness of the proposed model.

Study on the impacts of occupant distribution on the thermal environment of tall and large public spaces

In tall and large (T&L) public spaces, occupant distribution and its variation may impact the thermal environment and thus the operation of heating, ventilation, and air conditioning (HVAC) systems. Such studies are rare in previous literature, so in-situ tests were conducted to demonstrate dynamic occupant distribution characteristics and the potential impact on the thermal environment in the waiting room of a high-speed railway station. The building energy model (BEM) and the computational fluid dynamics (CFD) model were then co-simulated to analyze the impacts under different occupant distribution scenarios. To improve the indoor thermal environment affected by occupant distribution, the thermal coupling relationships between different subzones were considered and identified, and a supply air reallocation optimization based on occupant distribution was proposed for T&L-space public buildings. The results showed that both the temporal and spatial variations of occupants in different subzones easily led to the thermal non-uniformity of an entire large space with a difference over 1.5 °C. Meanwhile, the thermal coupling effect between different subzones should not be neglected in the distribution of supply air since the thermal uniformity of the case building was improved by 15.89%–20.03%, effectively reducing local overcooling and overheating.

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.

Optimization study of a novel district heating system based on large temperature-difference heat exchange

Decreasing the return water temperature of heat network is crucial for increasing the feasibility of district heating. On the one hand, the pipelines investment and the electricity consumption are reduced, due to a larger temperature-difference between supply and return water; on the other hand, it can provide a favorable condition for recovering the low-temperature waste heat from the combined heating and power plant. This paper studies a novel district heating system based on the large temperature-difference heat exchange, which features two types of absorption heat pumps, driven by hot water and natural gas, respectively. Considering the economics of investment and operation of the new heating system, the key parameters of two absorption heat pumps under different working conditions (primary supply water temperature τ1 and peak-shaving ratio β) are optimized. For example, when β is 0.3 and τ1 is 120 °C, the novel heat exchange system can decrease the primary return water temperature τ2 to about 15 °C. Compared with the conventional system, the comprehensive exergy efficiency ψ is improved by 29.2%, the comprehensive costs per unit heat output are decreased by 18.6%, and the heating capacity of the combined heating and power plant is enhanced by 47.5%.

Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling

The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications, such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer mainly originates from (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study’s database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both shallow and deep learning algorithms from the Python libraries Scikit-Learn and Keras are evaluated. A Long Short-Term Memory (LSTM) model achieves the best results (the median of R2 is 0.94). The ex post model architecture makes the model suitable for anomaly detection in energy monitoring systems.

Analysis and evaluation of heat source data of large-scale heating system based on descriptive data mining techniques

The on-demand parameters of heat source are the precondition for ensuring the safe, stable and energy-saving operation of heating system. For large-scale heating system, the existing predictive methods are not applicable due to the complexity of modeling, while the design heating load index method has many influencing factors, resulting in a low accuracy of obtaining accurate values. In this paper, firstly, the simplified mathematical model of the heating substation is built, and the calculation methods of under-demand rate (η) and energy-saving rate (ς) are proposed for diagnosis thermal balance and evaluation energy-saving potential. Secondly, the variation relationship among heat source parameters is analyzed and the input parameters of the analysis process are determined, then cluster analysis is adopted to identify the operation strategy, and non-on-demand clusters are eliminated from the perspective of professional knowledge. Thirdly, the data in the remaining clusters are discretized, association analysis is used to obtain the frequent item-sets of each cluster, and on-demand heating parameters of each cluster are obtained. Finally, η and ς are used to evaluate the object heating system. This application reveals that the descriptive data mining techniques combined with professional knowledge can successfully identify the on-demand parameters from the historical data of heat source.

Energy cost minimization of a multifarious water heating system with energy recovery: A case of a healthcare institution

The energy usage intensity of healthcare institutions is extensive. The recorded relative typical energy consumption of hospitals has been noted to be in the range of 43 to 93 kWh/bed/day. Currently, with the global COVID 19 pandemic, the overall energy consumption of these hospitals has proportionally increased with these higher patient occupancy rates. The demand for hot water is directly proportional to occupancy rates and contributes to a large proportion of the energy consumed. Reducing the energy and associated costs of water heating processes may prove highly beneficial to the currently severely overstrained healthcare sector.In this paper the operation of a multifarious water heating system with energy recovery was optimized in order to minimize the grid energy costs based on the ToU tariff and maximum demand charges from the utility, while the required hot water temperature was maintained.A life cycle cost analysis between two baseline systems, consisting of the multifarious water heating system with and without energy recovery was compared to the optimal control approach. The results indicate that the energy recovery system with optimal control in 5.3 years. Retrospectively, the proposed initiatives may result in a potential saving of 68.23%, over a 20 year project lifetime.

Optimization of the solar space heating system with thermal energy storage using data-driven approach

The employment of solar space heating is a significant measure to achieve carbon neutrality. However, the design of the solar space heating system usually leads to the dilemma of a trade-off between the economic benefit and environmental protection, which cannot be solved by single-objective optimization. In this work, in order to design a solar space heating system of a bungalow equipped with radiant floor heating, multi-objective optimization of the solar collector area and the volume of the water thermal energy storage (TES) tank is performed by coupling TRNSYS simulation, machine learning and genetic algorithm (GA). Simulation for the entire winter operation of the solar space heating system is conducted in TRNSYS, and a database with various combinations of the design variables are generated, based on which an artificial neural network (ANN) is constructed to capture the mapping of the design variables and the system performance indicators. After proper training and validation, the ANN model with satisfactory forecasting precision is utilized as the objective function for optimizing the system. GA is employed for searching the solutions of the multi-objective optimizations. This data-driven approach combining ANN and GA is helpful to achieve the optimal solutions. For the solar space heating system, the CO2 emission reduction can benefit most from the enlargement of the solar collector area and the TES tank volume, while the payback period reaches its minimum when both the solar collector area and the TES tank volume are small. By comparing single-objective with multi-objective optimizations, it is found that the single-objective optimization cannot reveal the interactions among the CO2 emission reduction, annualized life cycle cost (ALCC) and payback period (PBP) and that the optimization focusing on a single objective probably leads to apparent defects and inapplicability in reality. Better balance among the CO2 emission reduction, ALCC and PBP can be achieved from the Pareto optimums of the multi-objective optimization rather than the single-objective optimization. According to the TOPSIS evaluation, the most appropriate design scheme is obtained from the multi-objective optimization and has the potential to reduce the carbon emission by 5480.6 kgCO2eq/year while the ALCC and payback period are only 33.40 ¥/(m2·year) and 3.70 years, respectively.

Study of an Integrated Control Method for Heating Substations Based on Prediction of Water-Supply Temperature and Indoor Temperature

The refined control of heating substations is of great significance for on-demand heating provision and for the efficient operation of district heating systems (DHSs). This paper proposes an integrated control strategy for substations based on the prediction of the water-supply temperature and indoor temperature. Firstly, online sequential extreme learning machine (OS-ELM) is used to predict the water-supply temperature. Then, a linear prediction model is established to predict the indoor temperature. Finally, the integrated regulation strategy is established with the goal of minimizing operational costs, aiming at ensuring heating quality and meeting the limits of the flow rate and of the supply- and return-water temperatures. The heat-saving rate, power-saving rate and indoor-temperature satisfactory rate are introduced to evaluate the regulation effect of the proposed method. The field study results show that the performance index of operation executed with the regulation strategy proposed in this paper is 9.31%, 16.33% and 20.87% higher than that without our energy-saving regulation strategy respectively. The fluctuations in the water-supply pressure and differential pressure of the secondary network are significantly reduced, and the energy-saving effect is obvious.

Modeling and Coordination of Commercial Buildings in Distribution Systems

Commercial buildings spend vast amount of money every year on their electricity consumption. Though optimal scheduling and control of commercial buildings had been widely discussed in the literature, coordination of multiple buildings connected to power networks still need further consideration. Privacy issue is a major consideration to building managers, which makes the coordination problem very challenging. This article first provides a convex formulation for the optimal operation of heating, ventilation, and air conditioning systems in commercial buildings. Then, this article proposes the use of the alternating direction method of multiplier to enable privacy-based coordination of multiple commercial buildings in the electric grid. The formulated problem considers the thermal discomfort of the occupants and the building power consumption on the one hand, and the power losses of the power network on the other hand. The algorithm uses minimal amount of exchanged data to avoid any privacy invasion. A case study of multiple commercial buildings is done on an IEEE 15-bus distribution system to verify the proposed problem formulation. The results showed that the algorithm can achieve comparable performance to the centralized case in terms of minimizing the thermal discomfort, power consumption, and power losses.

Low-temperature operation of heating systems to enable 4th generation district heating: A review

District heating networks increasingly rely on heat pumps, condensing biomass boilers, and excess heat in the transition to sustainable energy systems. Accordingly, district heating operators seek to reduce their networks’ supply and return temperatures to maximise production efficiencies, minimise heat losses from distribution pipes and allow greater utilisation of renewable heat sources and excess heat. Experts have predicted that investing in solutions that reduce heating temperatures in buildings will yield a return on investment of 300% for district heating operators. Therefore, expecting incentives, building operators should identify methods to reduce supply and return temperatures to enable a rapid, widespread transition to low-temperature district heating. Ample research has investigated and documented the feasibility of low-temperature heating in buildings, and this paper presents the first comprehensive review. It synthesises available literature and adds new perspectives to help guide future implementation, research and development of low-temperature heating. The energy and temperature demands of various heating systems provides a background, leading to a review of typical malfunctions and their impacts. The article subsequently reviews the obtainable supply and return temperatures before and after renovating the building envelope and heating systems. It further identifies and summarises vital measures for decreasing heating system temperatures. Ultimately, the authors recommend minimising heating system temperatures using automatic balancing of space heating and ventilation systems, novel solutions for safe domestic hot water supply, and digitally-enabled performance monitoring and optimal control.

Fault detection and diagnosis using tree-based ensemble learning methods and multivariate control charts for centrifugal chillers

Fault detection and diagnosis (FDD) of centrifugal chillers plays an essential role in reducing energy consumption and ensuring safe operation of heating, ventilation and air conditioning systems. In the existing chiller FDD strategy, Data-driven based system modelling methods and statistical analysis-based detection techniques have been widely discussed and applied in recent years. This paper studies the performance of tree-based ensemble learning methods in chiller modelling and analyses multivariate control charts' detection effect. Predictions from the reference model are used in tandem with multivariate control charts to isolate faulty behavior from normal behavior, and then specific failures are identified through the diagnosis model when anomalies are detected. Firstly, in order to establish the optimal chiller reference model and diagnosis model, three tree-based ensemble learning algorithms, namely (i) random forest (RF), (ii) extreme gradient boosting (XGBoost), and (iii) light gradient boosting machine (LightGBM), are evaluated and compared with commonly used support vector machine (SVM). Secondly, Hotelling's T2, multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) are introduced and their performance in chiller fault detection is tested. Finally, the proposed LightGBM-MEWMA method is validated by the experimental data of ASHRAE RP-1043. Results show that the proposed method effectively identifies seven common chiller faults. Especially at low severity levels, the average detection rate is 88.71%, and the diagnosis accuracy rate is 82.3%.

From fault-detection to automated fault correction: A field study

A fault detection and diagnostics (FDD) tool, as addressed by this study, is a tool that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Although FDD tools can inform operators of building operational faults, currently an action is always required to correct the faults to generate energy savings. Fault auto-correction integrating with commercial FDD technology offerings can close the loop between the passive diagnostics and active control, increase the savings generated by FDD tools, and reduce the reliance on human intervention. This paper presents the field study of seven fault auto-correction algorithms implemented in commercial FDD platforms. Implementation includes software changes in the FDD tools and additional controls hardware or software changes in the BAS that were required to enable the execution of different types of auto-correction algorithms in real buildings. The routines successfully and automatically correct faults and improve the operation of large built-up Heating, Ventilation, and Air Conditioning (HVAC) systems, common in most commercial buildings. The auto-correction algorithms are tested across four buildings and three different building automation systems, following a rigorous procedure to make sure they work properly and do not negatively impact the system and building occupants. Technology benefits, market drivers, and scalability changes are drawn from the implementation effort and test results, to drive future research and industry engagement.

Optimization of Multi Agent System for Distribution Control of Distinct Heating System Using Improved Q-Learning Controller

The aim of this research is the use of multi-agent systems for the optimization of a distributed control for district heating systems. A district heating system comprises of production units, a distribution network, and a host of consumer substations. The operations of district heating system usually involves conflicting goals, e.g., to satisfy customers and to minimize production costs. Hence the agent must be capable of optimizing between maximizing supply to substations and minimizing production cost. Current substations employ purely reactive devices, making local decisions without taking into account the global state. Moreover the substations determine the flow in all parts of the district heating system. The optimal operation of the district heating system is therefore limited to providing sufficiently high temperature and pressure to all customers by taking local measurement to achieve this goal without considering other factors such as cost of production and time. The approach studied in this research is to equip substations with software agents to form a multi-agent system using Q-learning. The study also shows that it is possible to control the trade-off between quality of service and degree of surplus production as well as the possibility of extending the system with new consumers without increasing production capacity. In another study, an experiment was conducted in a controlled physical environment, where two agent-based approaches were evaluated and compared to existing technologies. The experiment shows that it is possible to automatically load balance a small district heating network using agent technology.. Finally, a generalized formal characterization of the problem space under investigation is provided, i.e., production and logistics network management, together with a preliminary evaluation of the applicability of the suggested multi-agent system approach for this general problem area.

Flow measurement uncertainty quantification for building central cooling systems with multiple water-cooled chillers using a Bayesian approach

Measurement uncertainty has significant negative impacts on the operation and control of heating, ventilation and air conditioning systems. It is a big challenge and should be solved urgently. Existing studies focus on reducing the impacts of measurement uncertainty by developing uncertainty tolerant methods without quantifying the measurement uncertainties themselves. They therefore fail to fundamentally solve them. This study aims to directly quantify the measurement uncertainties of water flow meters in multiple water-cooled chiller systems using a Bayesian approach. A measurement uncertainty quantification strategy is proposed based on Bayesian inference and energy balance models, and the Markov chain Monto Carlo method is used to achieve the strategy. The site data collected from a chiller system are used to test the strategy. Four simulation tests with different levels of measurement uncertainty are conducted to further test and systematically validate the strategy. Test results show that the measurement uncertainties (both systematic and random uncertainties) of the water flow meters in the chiller systems can be quantified effectively and with acceptable accuracy. The strategy performs very well in quantifying random uncertainties of flow meters, and the relative errors range from 0% to 12.8%. The performance of the strategy in quantifying systematic uncertainties is also satisfactory, and the relative errors range from 0.1% to 36.57%. The proposed strategy is able to quantify measurement uncertainties and can be used to optimize the control of chiller systems and improve the reliability of chiller systems.

A pressure and temperature cycling test stand with hot-gas bypass control for evaluation of adhesive joints in HVAC&R applications

The formation of joints is critical to the long-term reliability with leak-free operation of heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems. Leakages commonly occur due to fatigue failure developing in joined materials from continuous pressure and temperature cycling with mechanical vibration and refrigerant under pressure inside the system. In particular, rapid pressure and temperature changes happen frequently (e.g., multiple times per day) when the system is switched on and off. Therefore, it is important to have an automatically controlled fast-response pressure and temperature cycling (PTC) test stand available to test the performance of refrigeration joints to evaluate new bonding technologies. An innovative PTC test stand with hot-gas bypass control was designed, built, and demonstrated that eliminated the need for an evaporator and ensures rapid transition between different operating conditions. Tests were performed to demonstrate test stand functionality using R410A as the refrigerant to provide pressure and temperature cycles from 600 to 4500 kPa and 5 to 80 °C. A 50-cycle, 5-hour demonstration test was performed with both adhesively bonded and brazed joints following standardized joint testing guidelines. Both joint types survived the test without leaking, suggesting that the adhesive joints have sufficient thermal fatigue resistance along with the conventional brazed joints. Throughout the demonstration, the test stand accurately controlled the setpoint temperatures and pressures while switching the test section between these conditions. The test stand serves as a new approach for pressure and temperature cyclic fatigue testing of joints in HVAC&R systems.