Оглавление
Группа авторов. Smart Zero-energy Buildings and Communities for Smart Grids
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Smart Zero-energy Buildings and Communities for Smart Grids
Preface
Why this book?
Who is this book for?
Structure
Acknowledgments
List of Acronyms
1. The Role of Smart Grids in the Building Sector
1.1. Smart and zero-energy buildings
1.1.1. Smart metering
1.1.2. Demand response (DR)
1.1.3. Distributed systems
1.2. Smart and zero-energy communities
1.3. Conclusion and future prospects
2. Integrated Design (ID) Towards Smart Zero-energy Buildings and Smart Grids
2.1. Introduction
2.2. Methodology
2.3. Integrated design in smart and zero-energy buildings
2.4. ID process principles and guidelines
2.4.1. Benefits
2.4.2. Barriers
2.5. Scope of services
2.6. Remuneration models
Level 1 – Basic remuneration
Level 2 – Extra remuneration for extra tasks
Level 3 – Performance-related remuneration
2.7. Application of evaluation tools
1) Dynamic thermal simulation
2) Calculation of benchmarks
3) Lifecycle cost assessment
2.8. Sustainability certification
2.9. Consultancy and quality assurance
2.10. Measurement of design quality criteria
2.11. Defining a client’s objectives
2.11.1. Capital cost reduction
2.11.2. Delivery risk reduction
2.12. Defining the tenant’s objectives
2.12.1. Operational cost reduction
2.12.2. Building unsuitability risk reduction
2.13. Best practice sites. 2.13.1. Alexandros N. Tombazis and Associates Architects S.A. office building1
2.13.2. APIVITA Commercial and Industrial S.A
2.13.3. Stavros Niarchos Foundation Cultural Center
2.13.4. Karelas Office Park
3. Data Analysis and Energy Modeling in Smart and Zero-energy Buildings and Communities. 3.1. Energy signature for the NTL of Cyprus Institute1
3.2. Athalassa Campus and the NTL building
3.2.1. Methodology
3.2.2. Description of the Novel Technology case study
3.2.3. Data exploration
3.2.4. Correlation matrix
3.2.5. Regression model
3.2.5.1. Explained variance score
3.2.5.2. Mean absolute percentage error
3.2.5.3. Mean absolute error
3.2.5.4. Mean squared error and root mean squared error
3.2.5.5. Median absolute error
3.2.5.6. R2 score function
3.2.5.7. Linear regression model
3.2.5.8. Multiple regression model
3.2.5.9. Two variables linear regression
3.2.5.10. Three variables linear regression
3.2.5.11. Ridge regression
3.2.5.12. Single variable linear regression
3.2.5.13. Two variables linear regression
3.2.5.14. Three variables linear regression
3.3. Linear Fresnel solar collector at the NTL building, Cyprus Institute2
3.3.1. Development of the NTL model
3.3.2. Energy performance analysis in the NTL
3.3.3. Discussion
3.4. Conclusion
4. On the Comparison of Occupancy in Relation to Energy Consumption and Indoor Environmental Quality: A Case Study. 4.1. Introduction
4.2. Methodology
4.3. Description of the case building
4.4. Description of the experimental procedure
4.5. Results. 4.5.1. Investigation of energy consumption and indoor air quality
4.5.2. Days of special interest – high occupancy
4.5.3. Days of special interest – increased energy consumption
4.6. Discussion and concluding remarks
5. Indoor Environmental Quality and Energy Consumption Assessment and ANN Predictions for an Integrated Internet-based Energy Management System Towards a Zero-energy Building. 5.1. Introduction
5.2. Description of the SDE buildings. 5.2.1. General information
5.2.2. Monitoring activities for SDE 3
5.3. The power loads and hourly energy consumption
5.4. Indoor environmental quality
5.4.1. Thermal comfort assessment – time series analysis
5.4.2. Indoor air quality
5.4.3. The indoor illuminance levels
5.5. Cross correlation
5.6. Prediction using artificial neural networks (ANN)
5.6.1. Prediction of outdoor temperature
5.6.2. Prediction of relative humidity
5.6.3. Prediction of power loads
5.7. Specifications for an integrated internet-based energy management system towards a zero-energy building. The scope of the integrated internet-based energy management for SDE
5.7.1. The phases of the internet-based energy management system for SDE
5.7.2. Integration of software and prediction algorithms
5.8. Conclusion
6. Objective and Subjective Evaluation of Thermal Comfort in the Loccioni Leaf Lab, Italy. 6.1. Introduction
6.2. Background information
6.3. Methodology
6.3.1. Subjective measurements
6.3.2. Objective measurements
6.3.3. Combined analysis of objective and subjective measurements
6.3.4. User preferences and satisfaction with internal conditions
6.4. Collection of building background data
6.5. Collection of monitored data
6.6. Right-Now questionnaire survey
6.7. Results
Respondent characteristics
6.7.1. Analysis of MyLeaf measurements
6.7.1.1. Continuous measurements
6.7.1.2. Matched measurements
6.7.2. Analysis of Comfort Meter measurements
6.7.2.1. Continuous measurements
6.7.2.2. Matched measurements
6.7.3. Analysis of Right-Now survey responses
6.7.3.1. Thermal sensation
6.7.3.2. Acceptability of current thermal conditions
6.7.3.3. Preferred thermal sensation
6.7.4. Respondent characteristics and thermal comfort
6.7.4.1. Gender
6.7.4.2. Time sitting at desk before taking the survey
6.7.4.3. Adjacency to external window
6.7.5. Combined analysis of objective and subjective measurements
6.7.5.1. Determination of patterns in thermal sensation and average temperature for day of the week and period of day
6.7.6. Correlation analysis for MyLeaf and Right-Now survey measurements
6.7.7. Correlation analysis for objective and subjective measurements (Research for Innovation office space)
6.7.8. Comparison between objective and subjective thermal sensation measurements
6.7.9. Determination of acceptable and unacceptable conditions
6.8. Conclusion
7. Smart Meters and User Engagement in the Leaf House. 7.1. Introduction
7.2. Methodology
7.3. Analysis of user engagement. 7.3.1. Development of the questionnaire
7.3.1.1. Set of questions
7.3.1.2. Independent variables
7.3.2. Leaf House case study
Building envelope and systems
Energy data
Energy generation and storage data
7.3.2.1. Leaf House tenants’ focus group
7.4. Results
7.4.1. Demographics, socioeconomics
7.4.2. Physiological, social and behavioral aspects
7.4.3. Information level
7.4.4. Health and comfort
7.4.5. Living situation
7.5. Conclusion
8. Integration of Energy Storage in Smart Communities and Smart Grids
8.1. Energy storage systems in smart grids. 8.1.1. Electrical and electrochemical energy storage in smart grids
8.1.2. Mechanical energy storage in smart grids
8.1.3. Thermal energy storage in smart grids
8.2. Energy storage and smart grids: case studies. 8.2.1. Case study 1: the Leaf Community smart grid energy storage system
8.2.1.1. Leaf Community web-based monitoring and control platform
8.2.1.2. Local control
8.2.1.3. Central control
8.2.1.4. Data analysis
8.2.1.5. The TES of the Leaf Community
8.2.1.6. Electrical energy storage in Leaf Community
8.2.1.7. Performance analysis of energy storage for the Leaf Community microgrid. 8.2.1.7.1. Performance analysis of the TES
8.2.1.7.2. Performance analysis of the BES
8.2.2. Case study 2: energy storage of CSP and integration with smart grids. 8.2.2.1. Technical description of a polygenerative solar plant
8.2.2.2. The polygenerative solar plant in a university campus
8.2.2.3. Short-term thermal energy storage – the buffer tank
8.2.2.4. The molten salts thermocline storage
8.2.2.5. The absorption chiller
8.2.2.6. The ORC system
8.2.2.7. Performance analysis of the energy storage of the CSP. 8.2.2.7.1. Quasi dynamic state calculation of LFR energy production
8.2.2.7.2. Monitoring of the solar field
8.3. Conclusion and future prospects
Conclusion and Recommendations
References
List of Authors
Index. A, B
C, D
E
F, G
H
I, K
L
M, N, O
P
R, S
V, W, Z
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