Intelligent Building and Building Energy Efficiency Shengwei Wang - - PowerPoint PPT Presentation

Intelligent Building and Building Energy Efficiency

Shengwei Wang

Chair Professor of Building Services Engineering Building Energy and Automation Research Laboratory, Department of Building Services Engineering The Hong Kong Polytechnic University

beswwang@polyu.edu.hk

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Basic Approaches for Energy Efficient Buildings 建筑节能的基本途径

• Reduced heating/cooling loads /低热负荷/冷负荷

– Building envelope design, passive design, etc. /建筑围护结构设计, 被动式设计,等

• Use of energy efficient and green components and technologies /使用节

能和绿色环保的设备与技术

• Optimization of system and technology integration, operation and control

/优化系统和技术集成, 运行及控制 – Life-cycle commissioning, design optimization, control optimization, etc. / 全生命周期的校验,设计优化,控制优化,等。

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20% to 50% of saving can be achieved according to my local experiences and experiences of others in Europe and North America !

Steps towards Energy Efficient Buildings 建筑能源效益实现步骤

Make designs proper and correct 正确合理的设计 Optimize designs and selections 对设计和系统选用进行优化 Construct/install systems correctly 正确的系统施工/安装 Ensure systems operate as good as intent 确保系统运行效果达到设计期望 Push systems approach the best 使系统趋于最佳

Design Stage 设计阶段 Construction Stage 施工阶段 Operation Stage and T&C Stage 运行与 调试测试阶段

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• Building HVAC System Optimization Tool;

建筑空调系统优化工具

• Package of Online Optimal and Energy Efficient Control and Fault Diagnosis Strategies

实时节能优化控制和故障诊断策略系列

• Building System Online Performance Simulation Test Platform;

建筑系统实时性能模拟实验平台

• BA Control and Diagnosis Strategy Online Test Platform;

BA控制和诊断策略实时测试平台

Technologies and Tools Developed

• Building Level Energy Performance Quick Evaluation and Diagnostic Tool;

建筑整体性能快速评估和诊断工具

• Detailed Evaluation and Diagnostic Tool for A/C and BA systems;

空调和BA系统评估和诊断工具

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Building Energy Diagnosis Tools 建筑能耗诊断工具 Virtual Test Platforms 仿真实验平台 Optimization Tools 优化工具

Examples of Deliverables (A) / 可达目标示例(A)

For Existing Buildings/既有建筑:

• Building Energy Audit, Building System Evaluation and

Diagnosis

建筑能源系统的评估和诊断

• Building Energy Saving Plan and Strategies

建筑节能方案和策略

• HVAC and Control System Commissioning & Upgrading

空调和控制系统的校验及改造

• Upgrading System Operation and Control Strategies

改进系统运行策略与控制策略

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For New Buildings/新建建筑:

• Commissioning and Optimization of System Configuration

and Selection 校验和优化系统设计和设备选型

• Commissioning and Optimization of Monitoring and Control

Instrumentation and Systems 校验和优化监控设备和系统

• Commissioning of HVAC and Control Systems at Construction

and T&C Stages 在施工和调试测试阶段校验空调及其控制系统

• Develop Optimal and Energy Efficient Control Strategies

开发优化及節能控制策略

Examples of Deliverables (B) /可达目标示例(B)

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建筑全生命周期校验及优化 – 环球貿易广场

Building Life-cycle Diagnosis, Commissioning and Optimization

• International Commerce Centre (ICC)

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Our Role les in in IC ICC C Project ject /我们在项目中的角色

• Independent Energy

Consultant (Independent Commissioning Agent)

• 独立的节能顾问(独立的校

验代理)

• To Dev

evel elop the HVAC Energy Optimal Control System

• 开发HVAC节能优化控制

系统

490 m 118 F

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Sum ummary mary of f Ene nergy rgy Be Bene nefi fits/ ts/节能效益总计

• 1,000,000 kWh energy consumption is saved due to the modification
• n the secondary water loops of Zone 3 & 4 / 改进3、4区二次水

环路—节能1百万度

• 2,360,000 kWh , (about 5.1% of annual energy consumption of

chillers and cooling towers) of the cooling system can be saving due to the change from single speed to variable speed using VFD /冷却

塔单速运行改为VFD无级变频—节能2.36百万度

• 607,000 kWh , (about 2.8% of annual energy consumption of

chillers and cooling towers) of the cooling system will be wasted when the lowest frequency is limited at 37 Hz / 若冷却塔最低频

率定为37Hz，将费能约61万度

• 3, 500,000 kWh (about 7%) of the total energy consumption of

HVAC system) can be saved using PolyU control strategies based on the original design/ 在原设计方案的基础上，采用我们的

控制策略节能3.5百万度

Savin ing g by Co Contr trol

• l Opti

timiz izat atio ion – compared with the case when the HVAC system operates correctly as the

• riginal design intent. 3.5

.5M per year

优化控制节能 -与HVAC系统按照原设计正常 运行比，每年节省约3.5

.5百万度 Savi ving g by C y Commi miss ssion ioning ing (Impr provi

• ving

ng th the s sys yste tem m confi figura gurati tion

• n and sele

lection tion) ) – compared with the

• riginal design. About

t 3.5 .5 M pe per ye year

与原设计方案比，校验（改进系统形式及配 置） 年节能为3.5百万度

The annual total energy saving is about 7.0M kWh ! 年节能总计约7百万度

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Con

• ntrib

tributions utions in in sup uppor porting ting ICC bui uildin lding g in in get getting ting HK HK-BE BEAM AM Platinum tinum Cer ertificat tificate e 对ICC ICC铂金认证的贡献

Annual Energy Use Reduction

By 14.6% to get extra 2 credits

年能耗降低14.6% 得2分 Peak Demand Reduction

By 26.9% to get extra 2 credits

峰值负荷减少26.9% 得2分 Optimal Control Strategies

“Innovation” for extra 1 credits

“创新项”—优化控制策略 得1分

Gr Grad ade e 评级 Overall 得分率 Performance 性能评价

Platinum 铂金 75% Excellent 优异 Gold 金 65% Very Good 很好 Silver 银 55% Good 较好 Bronze 铜 40% Above average 中等偏上

The overall assessment grade is based

• n the percentage of applicable credits

(about 145) gained in 5 categories: site aspects ， material aspects ， energy use，water use , and IAQ (vision 4/04 ).

认证等级根据建筑在：场地，材料，能源， 用水以及室内环境质量这五个方面的整体 得分率而定.

Gold Gold 金级（72.7%）

5 credits 分 (3.5%)

Pla Plati tinu num 铂金级（76.2%)

H V A C

A New Hotel Development in Sheung Wan (Holiday Inn Express)

• Independent Energy

Consultant (Independent Commissioning Agent)

独立的节能顾问(独立的校验 顾问)

• To Dev

evel elop the HVAC Energy Optimal Control System

开发HV HVAC节能优化控制系统

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Summary of Energy Benefits/节能效益总计

• Saving

ving contribute tributed d by Po y PolyU yU Commis mmissio sioning ning (Im Improving ving the e sys ystem tem configura figuration ion and selection) lection) and d Contr trol

• l Optimization

imization – compared with the case when the HVAC system operates correctly as the original design intent. About

ut 20% % save ved d annuall ally 设计校验与优化控制节能 -与HV HVAC系统按照原设 计正常运行比，每年节省20% 20%电能。

• Compared with the normal energy use of the hotel group, the
• verall energy consumption is reduced by over 30% and about

50% lower than what required by current standard. －建筑总节能超过30％(与常规酒店平均建筑能耗相比) －建筑总节能超过50％(与当前节能标准要求相比）

Research Areas/研究领域

• Building HVAC&R system dynamic Simulation

建筑空调制冷系统动态模拟

• HVAC&R system Optimal and Energy-Efficient Control

空调制冷系统优化及节能控制

• Building Energy and HVAC&R System Fault Diagnosis

建筑能效及空调制冷系统故障诊断

• IB/BA Integration and Management Technology

IB/BA BA集成及管理技术

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Interaction between Intelligent Buildings and Smart Grid

• Active Participation of Building to Smart Grid Demand

Response

WEATHER DATA INTERNAL GAIN HVAC SYSTEMS OPTIMAL CONTROL STRATEGIES BUILDING LOAD AGGREGATION BUILDINGS LOAD PREDICTION DYNAMIC PRICING OPTIMAL SETTING

Smart Grids

Concept of Interaction between Buildings and Smart Grid

The integration of smart metering system with BAS

Interaction Establishment

Integrated with smart meters and grid information management and control centre, BASs can provide valuable information (e.g. energy demand characteristics) for grid optimization.

Grid Dynamic Pricing Setting Altered Building Power Demand Predictor Building Power Demand Predictor

Building Power Demand Alteration Potential Characterization

Pi: the reference power demand prediction of the ith building Indices Pi Pi′ Pi′: the altered power demand prediction of the ith building

r

Building Optimal Power Demand Control

r: the finalized electricity prices Smart Grid Information Management and Control Center Building Automation System

Intelligent Field Devices

(Offline Process) (Online Process) (Online Process)

r′: the trial electricity prices r′

Other Buildings Other Buildings

∑

Interactive Building Power Demand Management Strategy

Building Power Alteration Estimation Building- Smart Grid Interaction Building Power Demand Management

Energy performance assessment and diagnosis for existing information-poor buildings

既有信息匮乏建筑的能效评估及诊断

Practical constrains in assessing and diagnosing information-poor buildings

• Insufficient availability of energy use data

– Few or no sub-meters are installed

• Poor-quality measurements

– Insufficient sensors are installed – Installed sensors are seldom calibrated/maintained

• Unknown, abnormal operation modes

– Occupants’ behaviors : random set-points – Failures of automatic control: unscheduled operation

Existing methods are not applicable for information-poor buildings

• Calculation-based methods

– Detail simulation tools: requires too many inputs, skillful experts and time-consuming – Simplified methods: insufficient outputs with limited accuracy and applicability

• Measurement-based methods

– Sufficient end-use data from sub-meters – Comprehensive BMS platform – Continuous monitoring of operational performance

Energy balances: useful information is embodied in buildings and systems

Conditioned zone Unconditioned zone

Heat of “other-consumers” is dissipated to unconditioned zone or outside

Electricity

“others”

e.g., lift, exterior lighting

Cooling Supply

Internal heat gain Conductive heat gain through envelope Heat gain due to solar radiation Heat gain due to ventilation

HVAC

e.g., chiller, pump, fan

“internal”

e.g., lighting, plugged load

Others Internal HVAC Building

E E E E   

Demand Supply

CL CL 

Electricity consumption balance Cooling energy balance

Monthly energy performance assessment based on limited information

Inputs Monthly electricity bills Building design data Weather data Short-term field measurements

Electricity balance

Cooling demand Cooling supply

Cooling energy balance

Optimization algorithm Outputs

Energy consumptions

• f individual systems

Building cooling load Energy efficiencies of HVAC system and main components EHVAC

EInternal Energy Performance Calculation

Monthly energy performance indicators can be determined by energy balance principles in cooling season

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Optimization algorithm for energy balancing

kWh E E E

HVAC Others Internal

401 , 620   

    

HVAC Internal Demand

E E CL 843 , 290

SCOP E CL

HVAC Supply

 

Determine EInternal, EOthers and EHVAC to minimize relative cooling balance residual:

) ( 5 .

Supply Demand Supply Demand

CL CL CL CL R

μ

   

Outputs: Building energy performance data

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Internal-consumers HVAC system Others-consumers

Energy consumption of individual systems

Cooling load SCOP α

Jan 3.09E+06 1.44 38.8% Feb 2.76E+06 1.40 40.0% Mar 3.59E+06 1.49 36.8% Apr 4.97E+06 1.92 33.7% May 7.15E+06 2.04 33.5% Jun 7.88E+06 2.09 30.9% Jul 8.73E+06 2.15 31.5% Aug 9.18E+06 2.22 32.0% Sep 8.11E+06 2.14 32.5% Oct 6.21E+06 2.00 35.2% Nov 5.75E+06 1.93 34.8% Dec 3.48E+06 1.57 39.0% Total 7.09E+07 1.87 34.9%

Performance of HVAC system

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Main Interface of the developed software tool

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Inputs of the developed software tool

Envelope Information HVAC Configuration Monthly Energy Bills

Outputs of the developed software tool

HVAC consumption Building cooling load Building cooling break down System COP Balance residuals

Outputs of the developed software tool

Summary of Energy performance data

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