Simplified prediction model for lighting energy consumption in office building scheme design

At the scheme design stage,the potential of daylighting is significant due to the saving for electric lighting use. There are few simple tools for architects to optimize the daylighting design. Therefore,it is useful to develop a design guideline related to the evaluation of lighting energy saving potential and sunlight design strategies. This paper analyzes the impacts of different artificial lighting control methods and design parameters on daylighting. A direct correlation between lighting energy consumption and parameters such as orientations,window to wall ratio (WWR) and perimeter depth is established. A simplified prediction model is proposed to estimate lighting energy consumption with the given perimeter depth,WWR,and window transparency. Validation of the model is carried out compared with detailed lighting simulation software for an office building. After the variation analysis for these parameters,design advises for the daylighting design at scheme design phase are summarized.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.

Research on Calculation Models of Coal Comminution Energy Consumption

在总结粉碎功耗规律的研究进展的基础上，分析了单一粒度颗粒的粉碎功耗预测理论，介绍了粉碎功耗理论的3种著名假说体积假说、面积假说、裂缝假说及其修正计算方法，并进行了比较，描述了粉碎功耗计算的通用关系式和近年来提出的新机制，包括原生裂纹假说、突变理论及分形理论等；进而针对具有复杂粒度分布物料的粉碎功耗，将给料与产品粒度分布作为参数引入能耗计算中，获得了当粒度分布分别满足G．S分布、R—R分布、分形分布时的各种粉碎功耗理论预测结果。此外，还列出了部分常用的粉碎功耗经验关联式并进行了比较。文中提出在研究煤的粉碎功耗规律时，采用Walker计算式结合分形粒度分布或R-R分布的能耗模型及Morrell经验式是可行的。

A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.

Analysis and forecast of residential building energy consumption in Chongqing on carbon emissions

Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.

Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.

Energy consumption dynamic prediction for HVAC systems based on feature clustering deconstruction and model training adaptation

The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings,promoting energy conservation and low-carbon control.This paper focused on the energy consumption of heating,ventilation and air conditioning(HVAC)systems operating under various modes across different seasons.We constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters,along with historical energy consumption data.To enhance the K-means algorithm,we employed statistical feature extraction and dimensional normalization(SFEDN)to facilitate data clustering and deconstruction.This method,combined with the gated recurrent unit(GRU)prediction model employing adaptive training based on the Particle Swarm Optimization algorithm,was evaluated for robustness and stability through k-fold cross-validation.Within the clustering-based modeling framework,optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple models.The dynamic prediction models with SFEDN cluster showed a 11.9%reduction in root mean square error(RMSE)compared to static prediction,achieving a coefficient of determination(R2)of 0.890 and a mean absolute percentage error(MAPE)reduction of 19.9%.When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling,RMSE decreased by 12.6%,R2 increased by 4.0%,and MAPE decreased by 26.3%.The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method,and multi-attribute clustering modeling outperforms single-attribute modeling.

Combined Prediction for Vehicle Speed with Fixed Route

Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

基于EnergyPlus的CBD建筑能耗预测模型研究

建立了基于EnergyPlus的天津市CBD建筑能耗预测模型,对影响该市CBD建筑能耗的设计参数进行了灵敏性分析,选定其中9个关键设计参数,建立了天津市小白楼CBD建筑年总能耗的预测回归模型并进行了验证。研究结果显示,照明功率密度、设备功率密度、窗墙比等参数对CBD建筑总能耗影响较大,天津市CBD能耗预测回归模型R^2为0. 966,估计标准偏差为1. 122 W/m^2;能耗预测值与模拟值的最大偏差分别为-12. 813%和-7. 063%。

基于机器学习的空气源热泵干燥能耗回归预测

为了降低空气源热泵干燥过程能耗,研究了空气源热泵干燥能耗特性,采用多元线性回归模型(multivariate linear regression model, MLRM)和BP神经网络(back propagation neural network, BPNN)模型来预测干燥工艺能耗。在分析干燥能耗影响特征参数的基础上,提出将干燥工艺过程进行切分处理的方法以降低数据获取难度。选取烘房设定温度、烘房设定湿度、烘房初始温度、烘房初始湿度、环境平均温度、环境平均湿度、物料质量和初始含水率8个特征参数作为模型输入,能耗和物料结束含水率作为模型输出。使用MLRM模型、BPNN模型和其他机器学习模型进行能耗预测,MLRM模型对能耗拟合的决定系数为0.739,对物料结束含水率拟合的决定系数为0.931;BPNN模型使用Sigmoid函数作为激活函数时对能耗拟合的决定系数最高,为0.828,使用Identity函数作为激活函数时对物料结束含水率拟合的决定系数最高,为0.942,拟合效果优于其他机器学习模型,能够满足实际生产需求。以复水豌豆为干燥对象设计加载物料65 kg、持续时间4 h的完整变温变湿干燥工艺进行验证试验,结果表明:试验总能耗为15.066 kW·h,MLRM模型和BPNN模型的预测总能耗分别为14.476 kW·h、15.183 kW·h,预测精度分别为96.08%、99.23%;试验结束含水率为8.541%,MLRM模型和BPNN模型的预测结束含水率分别为9.560%、8.889%,预测精度分别为88.07%、95.93%。该研究提出了一种使用MLRM模型和BPNN模型对空气源热泵干燥能耗进行分段精准预测的有效手段,对于优化干燥工艺和降低干燥能耗具有实际意义。

数模联动的多特征工件加工能耗预测方法研究

在实际切削加工过程中材料去除率是不断变化的,现有将其视为恒量的能耗建模方法难以实现能耗准确预测。为了提高切削过程能耗预测精度,提出了一种基于材料去除率的数模联动加工能耗预测方法。首先,基于切削过程刀具与工件的接触关系分析了切入、完全切入和切出阶段材料去除率变化规律,并对相应的加工能耗特性进行了分析;其次,提出了数据驱动的刀具切入,切出阶段加工能耗预测方法,以及模型驱动的完全切入阶段加工能耗预测方法,实现加工过程能耗准确预测;最后,利用实验案例验证了所提模型及方法的有效性,为今后研究能耗预测精度奠定了基础。

基于个人舒适系统杭州住宅冬季热舒适与能耗

为揭示该地区个人舒适系统(PCS)作用下的居民冬季热舒适特征和供暖能耗需求,以杭州市为对象,采用问卷调研和入户现场实测的方式得出冬季住宅开窗、遮阳、使用空调和PCS等典型热环境组合调节模式以及典型供暖模式和居民活动状态组合下的热舒适特征;在此基础上模拟得到冬季室内热舒适和供暖能耗特征。结果表明,冬季“无设备+静坐”、“空调+静坐”、“PCS+静坐”、“无设备+家务劳动”、“空调+家务劳动”、“PCS+家务劳动”等6种工况下的冬季中性温度分别是17.3、18.8、16.4、15.7、15.7、13.9℃,舒适温度区间分别是14.3~20.3℃、17.1~20.5℃、14.4~18.4℃、13.7~17.8℃、13.3~18.1℃、11.0~16.9℃。冬季室内热舒适水平受热环境调节模式影响较大,客厅在室舒适时间占比在43.74%~80.21%之间,卧室在室舒适时间占比均为70%以上。使用空调与PCS供暖时,典型建筑在冬初冬末的供暖能耗强度是1.28 kWh·m^(-2),在严冬的供暖能耗强度是13.06 kWh·m^(-2)。

基于全影响因素的轧钢加热炉板坯单耗预测

板坯实际生产过程中单耗计算受原料和燃料条件、操作工艺、钢种等因素影响,且各因素与板坯单耗之间的映射关系较为复杂。文章采用BP神经网络建立板坯单耗预测模型,以板坯加热炉实际生产数据为研究对象,加热过程中涉及的全部影响因素共17项作为输入变量,建立板坯单耗计算预测模型。结合试错法确定合理的BP神经网络结构为:输入层节点数为17,隐藏层节点数为10,输出层节点数为1。预测结果显示单耗预测值与实际值趋势一致,预测均方根误差仅为0.181 GJ/t,模型整体精度可达92.06%。

数控铣削能耗预测及切削参数多目标优化研究

为了研究数控铣床节能优化问题,首先以316L不锈钢为加工对象,设计了数控铣削实验方案,并进行了实验数据分析;然后以实验数据为样本,运用BP神经网络建立了数控机床能耗预测模型,并利用蜣螂优化算法(DBO)对BP神经网络结构进行优化,建立基于DBO-BP神经网络的数控机床能耗预测模型。通过对比优化前后两模型,选择具有更高的预测精度和稳定性的DBO-BP神经网络模型与以加工成本为目标而建立的铣削参数多目标优化模型,并运用NSGA-Ⅱ对模型求解,得到最优解集,最后运用熵权TOPSIS法对最优解集进行决策,得到最优解。通过对比优化前后比能耗和加工成本,优化后的切削参数使比能耗和加工成本分别下降了33.84%和5%。研究结果表明,优化后的切削参数更加节能和节约加工成本。

DFA-ODENets:面向周期多阶段复杂系统的预测仿真框架

部分复杂系统受内外部因素影响在运行时会呈现出周期性的阶段变化,且在不同阶段具有完全不同的动态特性.因此在使用数据驱动方法解决此类系统的预测和仿真问题时,使用单一结构模型难以准确地学习系统在不同阶段的动态特性.本研究提出了基于确定性有限状态机-常微分方程网络的预测仿真框架(DFA-ODENets),以建模周期多阶段系统.该模型由多个ODENet组成,每个ODENet能够从不规则采样的序列数据中学习系统在各个阶段内的动态特性.同时模型集成了基于确定性有限状态自动机思想的阶段转换预测器以实现模型预测时在不同阶段之间自动转换.最后,将DFA-ODENet框架应用于某计算中心制冷系统的预测仿真场景中.模型能够在给定系统运行过程中的服务器负载和环境温度下模拟系统运行过程,并对系统的制冷功率、进气口温度等主要输出变量进行预测.其中,对于制冷系统能耗预测的平均相对误差在5%以内.同时,利用制冷系统仿真模型优化了系统停止制冷时的温度设定值,通过仿真实验表明该优化最高可以节省18%的制冷能耗.

基于混合模型的脱硫废水旁路蒸发系统能耗特性

脱硫废水旁路蒸发系统抽取部分空预器入口热烟气会使锅炉效率降低、煤耗增加。为了实现对抽取热烟气造成能耗增加的精准预测,提出了一种机理模型与人工神经网络有机结合的混合模型预测方法。采集广东某660MW电厂的运行数据作为样本,以空气预热器进口的风温、空气的流量、抽取烟气量、抽取烟气温度、锅炉负荷以及给煤量6个参数作为输入,建立了用于预测经过空气预热器空气换热量的BP神经网络模型,对不同隐含层结构进行模拟计算,分析比较确定了网络的最优结构是6-9-1,最终模型的决定系数R^(2)是0.99478,预测模型的相对误差在1%附近波动,整体预测效果较好。在此基础上结合机理模型,针对机组负荷波动及抽取烟气量波动的典型工况进行了能耗预测,获得了旁路蒸发系统能耗的定性规律。

基于卷积神经网络的医院建筑公共照明能耗预测研究

为提升医院建筑公共照明能耗预测准确性,研究一种基于卷积神经网络的医院建筑公共照明能耗预测方法。对历史公共照明能耗数据实施缺失数据填补、孤立值检测与处理,以此作为输出变量;通过计算灰色关联度选取医院建筑公共照明能耗影响因子,作为输入变量。基于卷积神经网络构建预测模型,以输入、输出变量为基础,完成模型训练,完成实际能耗的预测。结果表明:所研究预测方法的拟合优度值达到极大值,最高可达到0.92,说明该方法的预测结果更为准确,与真实情况更为贴近,具有一定应用价值。

基于SARIMA-LSTM组合模型的油气集输系统能耗预测

油气集输是油田开发生产过程的重要阶段,准确预测油气集输系统能耗能够为生产调度和能源管控提供支持。为提高油气集输系统能耗预测的准确性,针对其线性和非线性特征,综合考虑数理统计和机器学习预测方法的优缺点,提出一种基于季节性差分自回归积分滑动平均(SARIMA)和长短期记忆(LSTM)神经网络的组合预测模型。根据S油田M环状掺水油气集输系统6年的运行数据,设计组合模型的网络结构,训练组合模型的网络参数。研究结果表明:与传统的SARIMA模型和LSTM神经网络相比,组合模型对三个能耗指标的预测准确性显著提高,可为企业调整生产运行方案和优化能源管控方案提供指导和数据支持。