Weitere Kapitel dieses Buchs durch Wischen aufrufen
On the fast actual demographic trend and increasing comfort level, consumers are becoming more and more demanding in the areas of heating, cooling, ventilation, air conditioning, and lighting. Reducing energy consumption is necessary in all key sectors, such as buildings and construction, cities, and urban areas. Recent studies showed that using Information and Communication Technologies (ICT) will have a significant impact on improving energy efficiency and occupant comfort in complex real buildings. The main aim is to develop energy efficient control approaches and solutions to improve energy efficiency and occupant comfort by using innovative ICT techniques. These solutions could integrate techniques from different domains mainly intelligent control approaches using context-awareness and predictive analytics with a strong focus on occupant expectation, profile, and behavior. In this chapter, we put more emphasis on the influence of occupants’ activities, complex building’s systems on energy saving by reviewing existing approaches and tools for energy efficiency in complex real buildings.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Adeli, H., & Jiang, X. (2006). Dynamic fuzzy wavelet neural network model for structural system identification. Journal of Structural Engineering, 102, 102–111. CrossRef
Atthajariyakul, S. (2004). Real-time determination of optimal indoor-air condition for thermal comfort, air quality and efficient energy usage. Energy and Buildings, 36, 720–733. CrossRef
Azar, E., & Menassa, C. C. (2011). Agent-based modeling of occupants and their impact on energy use in commercial buildings. Journal of Computing in Civil Engineering, 26(4), 506–518. CrossRef
BACnet. (2003). A data communication protocol for building automation and control networks. Retrieved from www.bacnet.org/.
Barabasi, A.-L. (2013). Perspectives on a hyperconnected world-insights from the science of complexity. Technical report, World Economic Forum’s Global Agenda Council on Complex Systems.
Berg-Munch, B., Clausen, G., & Fanger, R. O. (1986). Ventilation requirements for the control of body odor in spaces occupied by woman. Environment International, 12, 195–199. CrossRef
Coetzee, L., & Eksteen, J. (2011) The internet of things-promise for the future? An introduction. In IST-Africa Conference Proceedings, IEEE (pp. 1–9).
De Florio, V., Bakhouya, M., Coronato, A., & Di Marzo, G. (2013). Models and concepts for socio-technical complex systems: Towards Fractal Social Organizations. Systems Research and Behavioral Science, 30(6), 750–772. CrossRef
Doukas, H., Patlitzianas, K. D., Iatropoulos, K., & Psarras, J. (2007). Intelligent building energy management system using rule sets. Building and Environment, 42(10), 3562–3569. CrossRef
Dounis, A. L., & Caraiscos, C. (2009). Advanced control systems engineering for energy and comfort management in a building environment—A review. Renewable and Sustainable Energy Reviews, 13(6), 1246–1261. CrossRef
El Mankibi, M. (2009). Indoor air quality control in case of scheduled or intermittent occupancy based building: Development of a scale model. Building and Environment, 44, 1356–1361. CrossRef
EnRiMa. (2010) FP7 project supported by the European Commission FP7-2010-NMP-ENV-ENERGY-ICT-EeB, ICT for energy efficiency. Project Number: 260041.
European Commission. Information Society (2010). Retrieved from http://ec.europa.eu/information_society/activities/sustainable_growth/buildings/index_en.htm.
Fabi, V., Andersen, R. V., Corgnati, S., & Olesen, B. W. (2012). Occupants’ window opening behaviour: A literature review of factors influencing occupant behaviour and models. Building and Environment, 58, 188–198. CrossRef
Faruque Ali, S., & Ramaswamy, A. (2009). Optimal fuzzy logic control for MDOF structural systems using evolutionary algorithms. Engineering Applications of Artificial Intelligence, 22, 407–419. CrossRef
Federal R&D Agenda for Net Zero Energy. (2008). High-performance green buildings report. Retrieved from https://www.whitehouse.gov/files/documents/ostp/NSTC%20Reports/Federal%20RD%20Agenda%20for%20Net%20Zero%20Energy%20High%20Performance %20Green%20Buildings%20Oct2008.pdf.
Hardin, G. (1968). The tragedy of the commons. Science, 162(3859), 1243–1248. CrossRef
Hoes, P., Hensen, J. L. M., Loomans, M. G. L. C., de Vries, B., & Bourgeois, D. (2009). User behavior in whole building simulation. Energy and Buildings, 41, 295–302. CrossRef
Inoue, T. (1998). The development of an optimal control system for window shading devices based on investigations in office buildings. ASHRAE Transactions, 104, 1034–1049.
ISO 7730:2005. (2005). Ergonomics of thermal environment—Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal discomfort criteria. Geneve: International Standardization Organization.
Jakubiec, J. A., & Reinhart, C. F. (2012). The ‘adaptive zone’—A concept for assessing discomfort glare throughout daylit spaces. Lighting Research and Technology, 44(2), 149–170. CrossRef
Kim, J. T., & Kim, G. (2010). Overview and new developments in optical daylighting systems for building a healthy indoor environment. Building and Environment, 45, 256–269. CrossRef
KNX Association. (2003). Retrieved from http://www.knx.org/.
Lachhab, F., Bakhouya, M., Ouladsine, R., & Essaaidi, M. (2015). A state-feedback approach for controlling ventilation systems in energy efficient buildings. In Proceeding of IRSEC 2015, Marrakech, Morocco. doi:10.1109/IRSEC.2015.7454986.
Lachhab, F., Bakhouya, M., Ouladsine, R., & Essaaidi, M. (2016). Towards a context-aware platform for complex and stream event processing. In Proceeding of HPCS 2016, Innsbruck, Austria. doi:10.1109/IRSEC.2015.7454986.
Lachhab, F., Bakhouya, M., Ouladsine, R., & Essaaidi, M. (2016). Performance evaluation of CEP engines for stream data processing. In The Cloudtech 2016, Marrakech, Morocco.
Lute, P. J. (2000). Predictive control of indoor temperatures in office buildings energy consumption and comfort. In Clima.
Mohsenian-Rad, A.-H., & Leon-Garcia, A. (2010). Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 1(2), 120–133. CrossRef
Mozumdar, R. (2009). A hierarchical wireless network architecture for building automation and control systems. In ICNS: The Seventh International Conference on Networking and Services.
Nesler, C. (1986). Adaptive control of thermal processes in buildings. IEEE Control Systems Magazine, 6(4), 9–13. CrossRef
Nguyen, T. A., & Aiello, M. (2013). Energy intelligent buildings based on user activity: A survey. Energy and Buildings, 56, 244–257. CrossRef
Oldewurtel, F. (2012). Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45, 15–27. CrossRef
Oldewurtel, F., Sturzenegger, D., & Morari, M. (2013). Importance of occupancy information for building climate control. Applied Energy, 101, 521–532. CrossRef
PEBBLE. (2007). FP7 project supported by the European Commission FP7-ICT-2007-9.6.3, ICT for energy efficiency. Project Number: 248537.
Reinhart, C. F. (2004). Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy, 77, 15–28. CrossRef
Reinhart, C. F., & Wienold, J. (2011). The daylighting dashboard—A simulation-based design analysis for daylight spaces. Building and Environment, 46(2), 386–396. CrossRef
Sadineni, S. B., Madala, S., & Boehm, R. F. (2011). Passive building energy savings: A review of building envelope components. Renewable and Sustainable Energy Reviews, 15, 3617–3631. CrossRef
Salat, S. (2009). Energy loads, CO 2 emissions and building stocks: Morphologies, typologies, energy systems and behaviour. Building Research and Information, 37(5–6), 598–609. CrossRef
Seppänen, O. A., Fisk, W. J., & Mendell, M. J. (1999). Association of ventilation rates and CO 2 concentrations with health and other responses in commercial and institutional buildings. Indoor Air, 9(4), 226–252. CrossRef
The European Commission. (2009). ICT for a low carbon economy. Smart buildings. The European Commission report. Retrieved from http://ec.europa.eu/information_society/activities/sustainable_growth/buildings.
Tuhus-Dubrow, D., & Krarti, M. (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 45(7), 1574–1581. CrossRef
Ürge-Vorsatz, D., Danny, H., Mirasgedis, S., & Levine, M. D. (2007). Mitigating CO 2 emissions from energy use in the world’s buildings. Building Research and Information, 35(4), 379–398. CrossRef
Yang, B. (2001). Neural networks for multi-objective adaptive structural control. Journal of Structural Engineering, 1272, 203–210.
Yao, R. (2009). A theoretical adaptive model of thermal comfort—Adaptive Predicted Mean Vote (aPMV) running. Building and Environment, 44, 2089–2096. CrossRef
Ye, J., Hassan, T. M., Carter, C. D., & Zarli, A. (2008) ICT for energy efficiency: The case for smart buildings. Final report.
Yeh, L.-W., Lu, C.-Y., Kou, C.-W., Tseng, Y.-C., & Yi, C.-W. (2010) Autonomous light control by wireless sensor and actuator networks. IEEE Sensors Journal, 10(6), 101029–101041.
Zhao, P. (2013). An energy management system for building structures using a multi-agent decision-making control methodology. IEEE Transactions on Industry Applications, 49(1), 1−8.
- Energy-Efficient Buildings as Complex Socio-technical Systems: Approaches and Challenges
in-adhesives, MKVS, Hellmich GmbH/© Hellmich GmbH, Zühlke/© Zühlke, Neuer Inhalt/© momius | stock.adobe.com