A STUDY ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR PREDICTING THE HEATING AND COOLING LOADS OF BUILDINGS

The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.

Direction Dependence of Savings on Cooling and Heating Loads by Energy Efficient Windows

The energy saving performance of energy efficient windows has strong dependence on window direction. Transmitted insolation level definitely affected the cooling and heating load. Simple simulation on the decrement of cooling load and the increment of heating load of a shading window compared with those of a transparent window show the prospect of energy saving effect clearly.From southeastward to southwestward, shading window even enlarges total heating and cooling loads when the thermal transmission is the same. However, if the shading coefficient of window is switched between summer and winter, total cooling and heating load can be reduced. This result clarifies the importance of ＂smart window＂.

Integrated Behavior of Carbon and Copper Alloy Heat Sink Under Different Heat Loads and Cooling Conditions

An actively water-cooled limiter has been designed for the long pulse operation of an HT-7 device, by adopting an integrated structure-doped graphite and a copper alloy heat sink with a super carbon sheet serving as a compliant layer between them. The behaviors of the integrated structure were evaluated in an electron beam facility under different heat loads and cooling conditions. The surface temperature and bulk temperature distribution were carefully measured by optical pyrometers and thermocouples under a steady state heat flux of 1 to 5 MW/m^2 and a water flow rate of 3 m^3/h, 4.5 m^3/h and 6 m^3/h, respectively. It was found that the surface temperature increased rapidly with the heat flux rising, but decreased only slightly with the water flow rate rising. The surface temperature reached approximately 1200℃ at 5 MW/m^2 of heat flux and 6 m^3/h of water flow. The primary experimental results indicate that the integrated design meets the requirements for the heat expelling capacity of the HT-7 device. A set of numerical simulations was also completed, whose outcome was in good accord with the experimental results.

Correlation Study of Operational Data and System Performance of District Cooling System with Ice Storage

The district cooling system (DCS) with ice storage can reduce the peak electricity demand of the business district buildings it serves, improve system efficiency, and lower operational costs. This study utilizes a monitoring and control platform for DCS with ice storage to analyze historical parameter values related to system operation and executed operations. We assess the distribution of cooling loads among various devices within the DCS, identify operational characteristics of the system through correlation analysis and principal component analysis (PCA), and subsequently determine key parameters affecting changes in cooling loads. Accurate forecasting of cooling loads is crucial for determining optimal control strategies. The research process can be summarized briefly as follows: data preprocessing, parameter analysis, parameter selection, and validation of load forecasting performance. The study reveals that while individual devices in the system perform well, there is considerable room for improving overall system efficiency. Six principal components have been identified as input parameters for the cold load forecasting model, with each of these components having eigenvalues greater than 1 and contributing to an accumulated variance of 87.26%, and during the dimensionality reduction process, we obtained a confidence ellipse with a 95% confidence interval. Regarding cooling load forecasting, the Relative Absolute Error (RAE) value of the light gradient boosting machine (lightGBM) algorithm is 3.62%, Relative Root Mean Square Error (RRMSE) is 42.75%, and R-squared value (R2) is 92.96%, indicating superior forecasting performance compared to other commonly used cooling load forecasting algorithms. This research provides valuable insights and auxiliary guidance for data analysis and optimizing operations in practical engineering applications. .

Empirical investigation of the cooling performance of a new designed Trombe wall in combination with solar chimney and water spraying system

This paper presents an experimental study of a new designed Trombe wall in combination with solar chimney and water spraying system in a test room under Yazd(Iran) desert climate.The Trombe wall area is 50% of that of the southern wall of the building that occupies less space and reduces the implementation costs. The new design of the channel has caused the absorber to receive the solar radiation from three directions. Based on the results, the optimum mass flow rate and the nozzle diameter of the water spraying system has been obtained 10 l/h and 30 μm, respectively. The results indicate that the water spraying system decreases indoor temperature and increases indoor relative humidity by about 8 ℃ and 17%, respectively. The most effect of outdoor relative humidity variation is on indoor relative humidity, rather than indoor temperature. When outdoor temperature increases, both indoor relative humidity and the difference between indoor and outdoor relative humidity decreases. The results also showed that theTrombe wall; Solar chimney; Water spraying system(2) Prediction of energy performance of residential buildings:A genetic programming approach, P67-74, by Mauro Castelli,Leonardo Trujillo, Leonardo Vanneschi, Ale觢 Popovic Abstract: Energy consumption has long been emphasized as an important policy issue in today's economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country's energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs,which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques.

Analysis and Economic Evaluation of Hourly Operation Strategy Based on MSW Classification and LNG Multi-Generation System

In this study,a model of combined cooling,heating and power system with municipal solid waste(MSW)and liquefied natural gas(LNG)as energy sources was proposed and developed based on the energy demand of a large community,andMSW was classified and utilized.The systemoperated by determining power by heating load,and measures were taken to reduce operating costs by purchasing and selling LNG,natural gas(NG),cooling,heating,and power.Based on this system model,three operation strategies were proposed based on whether MSW was classified and the length of kitchen waste fermentation time,and each strategy was simulated hourly throughout the year.The results showed that the strategy of MSW classified and centralized fermentation of kitchen waste in summer(i.e.,strategy 3)required the least total amount of LNG for the whole year,which was 47701.77 t.In terms of total annual cost expenditure,strategy 3 had the best overall economy,with the lowest total annual expenditure of 2.7730×108 RMB at LNG and NG unit prices of 4 and 4.2 RMB/kg,respectively.The lower heating value of biogas produced by fermentation of kitchen waste from MSW being classified was higher than that of MSW before being classified,so the average annual thermal economy of the operating strategy of MSW being classified was better than that of MSW not being classified.Among the strategies in which MSW was classified and utilized,strategy 3 could better meet the load demand of users in the corresponding season,and thus this strategy had better thermal economy than the strategy of year-round fermentation of kitchen waste(i.e.,strategy 2).The hourly analysis data showed that the net electrical efficiency of the system varies in the same trend as the cooling,heating and power loads in all seasons,while the relationship between the energy utilization efficiency and load varied from season to season.This study can provide guidance for the practical application of MSW being classified in the system.

Comprehensive power-supply planning for active distribution system considering cooling,heating and power load balance

An active distribution system power-supply planning model considering cooling,heating and power load balance is proposed in this paper.A regional energy service company is assumed to be in charge of the investment and operation for the system in the model.The expansion of substations,building up distributed combined cooling,heating and power(CCHP),gas heating boiler(GHB)and air conditioner(AC)are included as investment planning options.In terms of operation,the load scenarios are divided into heating,cooling and transition periods.Also,the extreme load scene is included to assure the power supply reliability of the system.Numerical results demonstrate the effectiveness of the proposed model and illustrate the economic benefits of applying distributed CCHP in regional power supply on investment and operation.

A Deep Neural Network Coordination Model for Electric Heating and Cooling Loads Based on IoT Data

As the ubiquitous electric power internet of things(UEPIoT)evolves and IoT data increases,traditional scheduling modes for load dispatch centers have yielded a variety of chal-lenges such as calculation of real-time optimization,extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling loads.In this paper,a deep neural network coor-dination model for electric heating and cooling loads based on the situation awareness(SA)of thermostatically controlled loads(TCLs)is proposed.First,a sliding window is used to adaptively preprocess the IoT node data with uncertainty.According to personal thermal comfort(PTC)and peak shaving contribution(PSC),a dynamic model for loads is proposed;meanwhile,personalized behavior and consumer psychology are integrated into a flexible regulation model of TCLs.Then,a deep Q-network(DQN)-based approach,using the thermal comfort and electricity cost as the comprehensive reward function,is proposed to solve the sequential decision problem.Finally,the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads,by using UEPIoT intelligent dispatching system data.The case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.

Comparison of space cooling/heating load under non-uniform indoor environment with convective heat gain/loss from envelope

The indoor parameters are generally non-uniform distributed.Consequently,it is important to study the space cooling/heating load oriented to local requirements.Though the influence of indoor set point,heat sources,and ambient temperature of convective thermal boundary on cooling/heating load has been investigated in the uniform environment in previous research,the influence of these factors,particularly the convective heat gain/loss through a building envelope,on cooling/heating load of non-uniform environment has not yet been investigated.Therefore,based on the explicit expression of indoor temperature under the convective boundary condition,the expression of space cooling/heating load with convective heat transfer from the building envelope is derived and compared through case studies.The results can be summarized as follows.(1)The convective heat transferred through the building envelope is significantly related to the airflow patterns:the heating load in the case with ceiling supply air,where the supply air has a smaller contribution to the local zone,is 24%higher than that in the case with bottom supply air.(2)The degree of influence from each thermal boundary to the local zone of space cooling cases is close to that of a uniform environment,while the influence of each factor,particularly that of supply air,is non-uniformly distributed in space heating.(3)It is possible to enhance the influence of supply air and heat source with a reasonable airflow pattern to reduce the space heating load.In general,the findings of this study can be used to guide the energy savings of rooms with non-uniform environments for space cooling/heating.

Regression tree ensemble learning-based prediction of the heating and cooling loads of residential buildings

Building energy consumption is heavily dependent on its heating load(HL)and cooling load(CL).Therefore,an efficient building demand forecast is critical for ensuring energy savings and improving the operating efficacy of the heating,ventilation,and air conditioning(HVAC)system.Modern and specialized energy-efficient building modeling technologies may offer a fair estimate of the influence of different construction methods.However,deploying these tools could be time-consuming and complex for the user.Thus,in this article,an ensemble model based on decision trees and the least square-boosting(LS-boosting)algorithm known as the regression tree ensemble(RTE)is proposed for the accurate prediction of HL and CL.The hyper parameters of the RTE are optimized by shuffled frog leaping optimization(SFLA),which leads to SRTE.Stepwise regression(STR)and Gaussian process regression(GPR)based on different kernel functions are also designed for comparison purposes.Results demonstrate that the value of root mean squared error is reduced by 37%–68%and 30%–41%for HL and CL of residential buildings,respectively,by the proposed SRTE in comparison to other models.Furthermore,the findings from the real dataset support the proposed model’s effectiveness in predicting HVAC energy usage.It can be concluded that the proposed SRTE is more effective and accurate than other methods for predicting the energy consumption of HVAC systems.

预加载RC梁在升降温全过程中承载性能的试验研究

为了研究钢筋混凝土梁在服役状态下遭受火灾后承载性能的变化,对7根钢筋混凝土梁进行了高温和加载试验,测试了在250℃、450℃和650℃加热过程和降温过程中梁的温度、极限弯矩和极限挠度等参数;分析了温度及冷却方式对预加载梁承载性能的影响。结果表明:在预加极限弯矩的39%荷载状态下,随着温度的升高,自然冷却梁的极限弯矩逐渐降低为未受高温时梁的96.45%.93.41%和91.38%。喷水冷却梁的极限弯矩逐渐降低为未受高温时梁的96.45%、95.69%和94.68%。达到极限荷载时,自然冷却和喷水冷却梁的跨中挠度均随着所受温度的升高而逐渐增大,受250℃、450℃和650℃后自然冷却梁的挠度分别为10.03mm、16.11mm和22.32mm;而喷水冷却梁的挠度分别为15.89mm、16.63mm和17.43mm。预加载梁所经受的温度越高,则其承载性能降低的越多。

火灾后钢筋混凝土板力学性能评估方法

钢筋混凝土楼板是火灾中最容易破坏的结构构件,需要对其火灾后的承载能力进行评估并加固。考虑升温阶段、降温阶段及火灾后阶段材料本构关系的不同,考虑高温下混凝土保护层的爆裂对温度场及力学性能的影响,提出了火灾后钢筋混凝土楼板力学性能分析的有限元计算模型。同时,针对典型的钢筋混凝土板加固方法,在前述有限元模型的基础上建立了火灾后加固的钢筋混凝土板有限元计算模型,利用该模型对典型的火灾后加固钢筋混凝土板的承载能力进行了计算分析。模型中采用单元生死技术实现了混凝土高温下的爆裂及火灾后加固的数值模拟,模型可用于火灾后及加固后钢筋混凝土板力学性能的评估。分析表明,经历火灾后,钢筋混凝土板的承载能力降低幅度较大,板存在明显残余挠度。加固后,钢筋混凝土板的承载能力得到较大程度的恢复。

基于热负荷均匀分配原则的直接空冷系统轴流风机转速灵活调节策略

在直接空冷电站中,轴流风机功耗占厂用电比例较高。为获得更为高效的风机运行调整策略,以某典型2´600 MW直接空冷机组为例,基于热负荷均匀分配原则,采用数值模拟方法,研究轴流风机转速灵活调节策略。获得不同环境温度和风速条件下,各轴流风机转速分布规律以及通过空冷凝汽器单元的冷却空气质量流量分布规律。最终,计算得到不同环境气象条件下不同轴流风机转速调节策略风机总功耗变化规律。结果表明,基于热负荷均匀分配原则的轴流风机转速灵活调整策略明显优于轴流风机转速整体调节策略,结果可为直接空冷机组优化运行和深层次节能提供参考。

基于多变量相空间重构和径向基函数神经网络的综合能源系统电冷热超短期负荷预测

为解决能源危机问题,提高能源利用率,综合能源系统(integrated energy system,IES)成为发展创新型能源系统的重要方向。准确的多元负荷预测对IES的经济调度和优化运行有着重要的影响,而借助混沌理论能够进一步挖掘IES多元负荷潜在的耦合特性。提出了一种基于多变量相空间重构(multivariate phase space reconstruction,MPSR)和径向基函数神经网络(radial basis function neural network,RBFNN)相结合的IES超短期电冷热负荷预测模型。首先,分析了IES中能源子系统之间的耦合关系,运用Pearson相关性分析定量描述多元负荷和气象特征的相关性。然后,采用C-C法对时间序列进行MPSR以进一步挖掘电冷热负荷和气象特征在时间上的耦合特性。最后,利用RBFNN模型对电冷热负荷间耦合关系进行学习并预测。实验结果表明,所提方法有效挖掘并学习电冷热负荷在时间上的耦合特性,且在不同样本容量下具有良好且稳定的预测效果。

竖向和水平组合荷载下能量桩单桩变形特性

能量桩作为一种新型能源地下结构,在承受上部建筑荷载同时,能够获取浅层地热能。目前针对水平荷载与竖向荷载共同作用下的能量桩热力学特性的研究较少,而水平荷载与竖向荷载共同作用下,桩身温度变化会引起桩体弯矩、水平和竖向位移等发生变化。基于模型试验,对桩体施加10次冷热循环,开展了竖向和水平组合荷载下能量桩的变形特性研究。结果表明,组合荷载下冷热循环会进一步增大桩身弯矩,且对桩体中部的影响更大,最大桩身弯矩增幅达到了117%;冷热循环会产生桩顶累积位移,试验桩竖向位移增加了0.201 mm,温度作用引起的水平位移增加值达到了1.46%D(D为桩体直径);同时,冷热循环会导致桩向桩前倾斜,10次冷热循环后转角达到1.88×10^(-3)rad,且会随着循环次数增加有缓慢增加趋势;加热时桩前土压力减小,而制冷时使桩前土压力增大。

操作制度对高炉热负荷分布影响研究

鞍钢股份有限公司炼铁总厂利用覆盖炉体的高精度水温电偶,实现了对5#高炉各部位冷却壁热负荷的长期跟踪研究。通过研究装料制度和送风制度对高炉热负荷分布的影响,明确了边缘焦炭负荷、矿石平台宽度、鼓风动能、风口回旋区长度和炉腹煤气量对热负荷分布的具体作用效果,为高炉热负荷分布的合理性研究和调控手段研究提供了依据。

复杂工况下岩石真三轴试验机隔热材料热—力学演化特性

高温真三轴试验机研制过程中,高温环境、循环加卸载和循环热冲击等复杂加载条件对试验系统隔热性能提出了严苛要求。为了科学合理地选择复杂条件下高温真三轴试验机的隔热材料,制备了3种高温工程隔热材料,分别是高聚热能酰胺脂隔热材料(GX)、玻璃纤维与耐高温树脂复合隔热材料(BX)和高温阻隔复合云母材料(YM),并开展考虑实际仪器设备工况下的循环热冲击、高温环境和循环加卸载等室内试验。结果表明:在高温环境影响下3种隔热材料的导热系数均随温度的升高呈现先升高后降低的趋势,弹性模量出现了一定幅度的下降;随着循环加热冷却次数的增加,YM材料的导热系数数值平稳下降,BX和GX材料的导热系数呈现先升高后降低的变化趋势,弹性模量变化规律与高温环境影响下相似;隔热材料的导热系数均随着循环加卸载次数的增加呈现先升高后降低的变化趋势,其中初次加卸载对材料隔热性能和物理性能的影响最大;综合热—力学性能评判,YM材料性能最稳定,为岩石真三轴试验机隔热材料的最优选。

采煤工作面空冷器位置及风流参数优化研究

针对济宁二号煤矿10303采煤工作面高温热害问题,采用数值分析方法,建立了工作面物理模型,分析了风筒长度、风筒的出风口距离及温度等多因素对降温系统的影响,确定了不同工况(生产和停产)的温度控制参数;通过自动启闭控温设施控制空冷器出风温度,实现了不同作业下采煤工作面所需的制冷量,解决了工作面高温热害问题。结果表明:100 m风筒长度、50m风筒出风距离的降温效果最好;当工作面正常开采作业时,出风温度设置应小于16.5℃,工作面的平均温度将降低3.7℃;当停采作业时,出风温度设置应小于20.5℃,此时工作面的平均温度将降低3.5℃;2种工况下的参数均能达到采煤工作面的温度控制目标。

基于定热负荷的自然通风湿式冷却塔防冻特性三维数值研究

针对湿式冷却塔(湿冷塔)冬季运行易出现填料底层、进风口上缘等位置易挂冰的实际问题,建立了基于定热负荷的自然通风湿冷塔三维数值模型,探究了湿冷塔在严寒天气不外加防冻装置运行时的防冻特性规律,分析了填料底层水温、通风量等关键参数的变化特征及影响因素。研究结果表明:环境温度越低,机组负荷对填料底层平均水温、最低水温的影响越大;填料底层水温偏差变化的主要因素是机组负荷、环境风速、配水方式,其中配水方式的影响幅度较大,机组负荷次之,环境风速影响较小;通风量与机组负荷呈正相关关系,与环境温度呈负相关关系;环境温度相同时,外圈配水下的通风量小于全塔配水;迎风侧内部较低处及背风侧外部的水温最低,冻结风险最大,湿冷塔冬季运行时,应着重在迎风侧和背风侧进行防冻装置布置。

超深井矿山井下降温技术研究

针对某超深井矿山井下热害问题,利用Ventsim^(TM) DESIGN 5.4三维通风软件对矿山井下通风系统进行通风和热模拟计算,并通过热负荷计算对比需风量焓差,分析得到:矿山一期工程通过加大风量可以降低井下工作面气象环境至国家相关安全规程允许范围内(湿球温度不高于27℃)。二期工程通过加大风量不能解决井下高温问题,通过热模拟计算和分析后,根据矿山现状,在3个位置设置制冷站并进行核算,得到了每个制冷站所需负荷,最终对各个制冷机组进行了选型。