usage and occupancy. The impact of different occupancy patterns on the energy demand, the illuminance of the building, as well as the internal levels of temperature, relative humidity and CO2, are examined.
In Chapter 5, the energy consumption and indoor environmental quality of one of the three buildings of the Design and Environment School (SDE3) of the National University of Singapore is evaluated and cross-correlated based on a different perspective. Prediction algorithms based on artificial neural network models are tested.
In Chapter 6, objective and subjective evaluation of thermal comfort is analyzed in the context of a unique smart zero-energy industrial facility in Italy.
In Chapter 7, the user engagement of residents in a smart zero-energy building in the same area of Italy is investigated in order to provide the framework for analyzing individual preferences, identifying consumption patterns and assessing the utilisability of information provided to users as well as how effective this is in supporting behavioral change.
Chapter 8 deals with the integration of energy storage in smart communities and smart grids. The various energy storage technologies are presented including electrical, mechanical, chemical and thermal. Energy storage and optimization of its utilization in smart grids integrating renewable energy technologies is explored through state-of-the-art case studies.
Finally, the conclusion outlines the main and overall conclusions and recommendations stemming from the findings of the presented research.
Acknowledgments
The editors express their deepest appreciation and gratitude to all partners, personnel, and researchers for their unique contributions, time and efforts which altogether resulted in making this publication happen. We are also very thankful to the European Commission and the EU taxpayer for devoting the necessary financial resources for the implementation of the Smart GEMS project.
Nikos KAMPELIS
November 2021
List of Acronyms
| AMI | Advanced Metering Infrastructure |
| ANN | Artificial Neural Network |
| AP | Accredited Professional |
| ATES | Aquifer Thermal Energy Storage |
| BAS | Building Automation System |
| BCVTB | Building Controls Virtual Test Bed |
| BEMS | Building Energy Management System |
| BIM | Building Information Modeling |
| biPV | building-integrated PhotoVoltaics |
| BMS | Building Management System |
| CAES | Compressed Air Energy Storage |
| CDD | Cooling Degree Days |
| CHP | Cogeneration of Heat and Power |
| COP | Coefficient Of Performance |
| CPC | Compound Parabolic Collector |
| CSP | Curtailment Service Provider |
| Cv | Coefficient of variance |
| DA | Day-Ahead |
| DC | Direct Current |
| DER | Distributed Energy Resources |
| DG | Diesel Generator |
| DHW | Domestic Hot Water |
| DNI | Direct Normal Irradiance |
| DR | Demand Response |
| DSG | Direct Steam Generation |
| DSM | Demand Side Management |
| EED | Energy Efficiency Directive |
| EER | Energy Efficiency Ratio |
| EES | Electrical Energy Storage |
| EMS | Energy Management System |
| EPBD | Energy Performance Buildings Directive |
| ES | Energy Signature |
| ESEER | European Seasonal Energy Efficiency Ratio |
| ETL | Extract, Transform, Load |
| EV | Electric Vehicles |
| FC | Fuel Cell |
| FCU | Fan Coil Units |
| FMU | Functional Mock-up Units |
| G2V | Grid-to-Vehicle |
| GA | Genetic Algorithm |
| GSHP | Ground Source Heat Pumps |
| HDD | Heating Degree Days |
| HESS | Hybrid Energy Storage Systems |
| HMI | Human Machine Interface |
| HPS | Hydro-Pumped Systems |
| HRU | Heat Recovery Units |
| HTF | Heat Transfer Fluid |
| HVAC | Heating, Ventilation, Air Conditioning |