Mining a coal seam below a heating goaf with a force auxiliary ventilation system at Longhua underground coal mine, China

Extraction of a coal seam which lies not far below a heating goafcan be a major safety challenge. A force auxiliary ventilation system was adopted as a control method in successful extraction and recovery of the panel 30110 of the #3-1 coal seam, which is about 30-40 rn below the heating goaf of the #2-2 seam at Longhua underground coal mine, Shanxi Province, China. Booster fans and ventilation control devices such as doors and regulators were used in the system. The results show that, provided that a force auxiliary ventilation system is properly designed to achieve a pressure balance between a panel and its overlying goat＇, the system can be used to extract a coal seam overlain by a heating goal. This paper describes the design, installation and performance of the ventilation system during the extraction and recovery phases of the oanel 30110.

Practice and analysis of recycling non-drinking water from air-condition and reverse-osmosis system into rainwater collection system

This paper is based on the rainwater collection project in the retrofit of the Dongyi teaching block in Zhejiang University Xixi Campus.The analysis incorporates the local meteorological data, recycling water utilization, and precipitation adjustment.The rainwater collection system in this program also adds the condensation water from the heating, ventilation and air conditioning （ HVAC） system and the concentration from the reverse-osmosis system used for watering greens and supplying waterscapes.By calculating, the quantity of the HVAC condensation water in summer is 3.48 m3/d, and the quantity of the reverse-osmosis concentrated water is 198 to 396 L/d.This method solves the water shortage caused by high evaporation in summer and low precipitation in winter.Supported by empirical monitoring data, the proposed method significantly increases the economic efficiency of the system during the summer period.

Country-level meteorological parameters for building energy efficiency in China

Accurate basic data are necessary to support performance-based design for achieving carbon peak and carbon neutral targets in the building sector.Meteorological parameters are the prerequisites of building thermal engineering design,heating ventilation and air conditioning design,and energy consumption simulations.Focusing on the key issues such as low spatial coverage and the lack of daily or higher time resolution data,daily and hourly models of the surface meteorological data and solar radiation were established and evaluated.Surface meteorological data and solar radiation data were generated for 1019 cities and towns in China from 1988 to 2017.The data were carefully compared,and the accuracy was proved to be high.All the meteorological parameters can be assessed in the building sector via a sharing platform.Then,country-level meteorological parameters were developed for energy-efficient building assessment in China,based on actual meteorological data in the present study.This set of meteorological parameters may facilitate engineering applications as well as allowing the updating and expansion of relevant building energy efficiency standards.The study was supported by the National Science and Technology Major Project of China during the 13th Five-Year Plan Period,named Fundamental parameters on building energy efficiency in China,comprising of 15 top-ranking universities and institutions in China.

Energy Conservation Assessment in a Telecommunication Building in an Arid Environment by On-Site Measurements

The paper presents the results of an on-site measurement carried out in the premises of a telecommunication building to explore avenues to reduce energy consumption in the building. The results of the study indicate that the building＇s fixed electrical load for powering landlines, mobile lines, and internet services is about 46% of the total building load. The chillers split A/C units and the air handling unit account for almost 47% of the power. Lights and other small equipment consume 7% of the total energy supplied to the building. Temperature measurements showed that the air conditioning system in the building was not functioning efficiently resulting in the generation of hot spots in several areas. The findings suggest that a thorough evaluation of the HVAC system in the building must be carried out to ensure an efficient operation of the existing cooling system to overcome uneven temperature buildup in different zones of the building.

Selection of Heat Recovery Ventilators in Different Climate Zones of China

Considering four different climate zones in China, an investigation on the choice of heat recovery ventilator for the buildings with little moisture emissions is carried out. The annual composition of energy consumption of air intake for per unitary air ventilation flow rate is evaluated by employing the testing data of climatic parameters in eight selected cities. The analysis shows that the total heat recovery is suitable in a controlled ventilation system with air humidity controlled during heating period of all the climates. For the building without air humidity controlled in winter, the sensible heat recovery ventilators can be used in severe cold and cold regions, and total heat recovery systems are more suitable for energy saving in hot summer and cold winter and hot summer and warm winter regions.

Case Analysis of a Pump-Driven Heat Pipe Heat Recovery Ventilator in an Existing Experiment Building

The building energy consumption is an important part among the total society energy consumption,in which the energy consumption for air conditioning occupies almost 70%.The energy consumption of the air conditioning system for fresh air handling can be saved effectively when the exhaust air energy could be recovered to preheat or precool the fresh air.Considering the install locations requirements on field,the pump-driven heat pipes(PHP)were developed as heat recovery ventilators(HRVs)and used in an existing experiment building in Beijing Urban.The thermal performance of the PHP HRVs was tested in real operation time periods under winter running mode.Both the power and heat consumption of the modular air handling units with and without HRVs were monitored and obtained,as well as the hourly power and heat consumption.The energy savings of HRVs were analyzed.The results indicate that the PHP HRVs can work steadily and meet the energy recovery need well.The temperature effectiveness of the HRVs can be kept from 60%to 70%.The test total energy saving rate was 24.48%,and the average hourly heat consumption reduced by 28.54%.The daily energy consumption can be saved by 118 kWh,and the energy savings can reach to 9440 kWh for a whole winter.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Efficacy of coupling heat recovery ventilation and fan coil systems in improving the indoor air quality and thermal comfort condition

Mechanical Ventilation with Heat Recovery(MVHR)systems are gaining increasing interest in buildings with low energy demand,for improvement of the Indoor Air Quality(IAQ)and reduction of the ventilation energy loss.In retrofitted buildings,MVHRs are often integrated with an additional air heater to cover space heating demand.Hence,evaluation of the interactions between MVHR and heat emitter,and their effects on indoor airflow characteristics is of significant importance.The present study aims to investigate effects of a combined MVHR-fan-coil system in heating mode on IAQ and thermal comfort parameters inside a retrofitted room,by means of a computational fluid dynamic(CFD)code.The proposed CFD model is validated by comparing the numerical results with experimental data.The results yielded by numerical simulations allow evaluating the indoor environmental quality characteristics as well as addressing the MVHR and fan coil interactions.The results indicate that the airflow discharged from the fan coil could have a significant impact on the age of the air;while it provides a desirable thermal comfort condition within the room,it may hinder to some extent delivery of the fresh air to the occupied zone due to creation of counterflow fields.Furthermore,it is shown that although increasing the fan speed(ON mode)would slightly enhance the air change efficiency,the OFF mode yields not only a better distribution of the fresh air but also a higher ventilation efficiency than when fan coil operates.

Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system

Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closely with real physical systems.Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances.To address this challenge,this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems.This method utilizes clustering algorithms to remove redundant data for achieving data compression.Moreover,a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models.Its basic idea is that once a component model is calibrated by heuristic methods,its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components.By doing so,the calibration process can be guided,so that fewer iterations would be used.The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building.Results show that the proposed clustering compression-based method can reduce computation loads by 97%,compared to the conventional calibration method.And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6%of the time costs.

Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time.Today,most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure occurs.This is mainly because the building owners do not previously have good tools to detect and diagnose these faults,determine their impact,and act on findings.Commercially available fault detection and diagnostics(FDD)tools have been developed to address this issue and have the potential to reduce equipment downtime,energy costs,maintenance costs,and improve occupant comfort and system reliability.However,many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the results.In this paper,supervised and semi-supervised machine learning(ML)approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive maintenance.The study data was collected from one packaged rooftop unit(RTU)HVAC system running under normal operating conditions at an industrial facility in Connecticut.This paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning,achieving accuracies as high as 95.7%using few-shot learning.

Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems

For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.

Simulating dispatchable grid services provided by flexible building loads:State of the art and needed building energy modeling improvements

End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability,reduce costs,and support increased hosting of distributed renewable generation.This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling.We consider services delivered to(1)the high-voitage bulk power system through the coordinated action of many,distributed building loads working together,and(2)targeted support provided to the operation of low-voltage electric distribution grids.Although an exhaustive exploration is not possible,we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling(BEM)tools,suggesting pathways for future research and development.

Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review

The built environment sector is responsible for almost one-third of the world’s final energy consumption. Hence, seeking plausible solutions to minimise building energy demands and mitigate adverse environmental impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods for the built environment over the past decade, with special reference to holistic approaches. Different AI-based techniques employed to resolve interconnected problems related to heating, ventilation and air conditioning (HVAC) systems and enhance building performances were reviewed, including energy forecasting and management, indoor air quality and occupancy comfort/satisfaction prediction, occupancy detection and recognition, and fault detection and diagnosis. The present study explored existing AI-based techniques focusing on the framework, methodology, and performance. The literature highlighted that selecting the most suitable machine learning and deep learning model for solving a problem could be challenging. The recent explosive growth experienced by the research area has led to hundreds of machine learning algorithms being applied to building performance-related studies. The literature showed that existing research studies considered a wide range of scope/scales (from an HVAC component to urban areas) and time scales (minute to year). This makes it difficult to find an optimal algorithm for a specific task or case. The studies also employed a wide range of evaluation metrics, adding to the challenge. Further developments and more specific guidelines are required for the built environment field to encourage best practices in evaluating and selecting models. The literature also showed that while machine and deep learning had been successfully applied in building energy efficiency research, most of the studies are still at the experimental or testing stage, and there are limited studies which implemented machine and deep learning strategies in actual buildings and conducted the post-occupancy evaluation.