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The Leaf Community is a unique blend of inspired qualified personnel where the preservation of the natural environment, renewable energy sources and worldwide R&D meets education, local culture and society. The Leaf Lab is an industrial building of a rectangular shape and a floor area of approximately 6,000 m 2 located in the Leaf Community, one of the very well‐established smart microgrids in Europe. The Leaf Lab is a near‐zero energy building, combining passive systems, energy‐efficient technologies, integrated monitoring and control, as well as renewable energy production. The number of residents in the Leaf House varies, as it accommodates both employees of the Loccioni Group and short‐term visitors of the Leaf Community. The apartments in the Leaf House are equipped with a touch display providing access to an energy management interface for observing indoor conditions and energy‐related data as well as managing the heating, ventilation and air conditioning; lights; and window shutters.

This chapter presents a comprehensive approach for evaluating the performance of one industrial and one residential smart/near‐zero energy building. The research activities performed and presented in this chapter target the estimation and evaluation of the performance gap between the design and operational phase of zero energy buildings. The steps are as follows: selection of the case study buildings; analysis of the buildings and their systems' design and an assessment of power and energy requirements through dynamic thermal simulation models; data collection while the buildings are in operation; and a comparison of the results of the buildings and the extraction of useful remarks and conclusions. The chapter analyzes the Leaf Lab's energy performance and compares modeling results with real‐time data. There is a systematic and continuous approach in establishing near‐zero energy targets, through research and innovation activities.

As we are nowadays experiencing a transition period for the energy demand, there is a clear movement of the European energy market towards a new field of efficiency including reliable and smart systems that will upgrade the improvement of Europe's economic and environmental health. To this end smart systems introduce innovative applications with multiple and interdisciplinary characteristics: safe integration of additional renewables, distribution to the network, efficient delivery systems and monitoring control through demand response in order to achieve zero energy targets. The integration of smart technologies requires a holistic approach that takes into account all aspects of sustainability. The implementation of highly efficient smart buildings is feasible through the integration of smart metering, renewable systems acting as generators/storage and energy management. The holistic system supports and fulfils demand load management and distribution network of future grids.. Moreover, the benefits of effective thermal and electrical storage are underlined as a crucial factor of smart systems and smart buildings. This paper highlights the principles of integrated design procedure and links the process with smart building technologies. Energy efficiency methodologies and innovative techniques applied at building level are presented. To this context current EU policy framework, trends and perspectives concerning integrated design as a supportive tool for zero energy concept are also provided.

Summarization: Traditional electricity grids lack flexibility in power generation and load operation in contrast to smart-micro grids that form semi-autonomous entities with energy management capabilities. Load forecasting is invaluable to smart micro-grids towards assisting the implementation of energy management schedules for cost-efficient and secure operation. In the present paper is examined the 24h forecasting of excess production in an existing micro-grid. Alternative input parameters are considered for achieving an accurate prediction. The prediction can be used for scheduling the charging process of a thermal storage during weekends based on excess power production levels. Παρουσιάστηκε στο: 9th International Conference on Sustainability and Energy in Buildings

Energy efficiency, advanced controls and renewable energy systems for operating industrial, residential and tertiary sector buildings designed to be Near-Zero Energy are investigated to explore the performance gap. The analysis involves a comparison of energy dynamic and quasi-dynamic models with data from smart monitoring systems, indoor and outdoor environment measurements, power consumption and production data. Specific issues and conclusions have been drawn as the basis for addressing the performance gap between energy efficiency prediction in the design phase and measurements’ evaluation in operational phase.

Renewable energy production technologies are indispensable to the development of a clean and energy efficient built environment. Being dependent on climatic parameters, renewable energy production may vary causing a mismatch between energy demand and available production. Therefore, forecasting of renewable energy production allows the design and implementation of management schedules depending on expected production, thus assisting towards a more efficient and secure operation. The application of artificial neural networks (ANN) has been proved effective towards this aim. The present work is investigating the application of ANN for production forecasting of a concentrated solar power (CSP) fresnel system. The fresnel system production is optimum under clear skies and peak direct solar radiation. The system under investigation is connected to a thermal storage and its production is used to cover the heating/cooling loads of a building. The prediction can be used for scheduling the times that the system focalizes and de-focalizes for optimizing energy production and storage. The present work is relevant to researchers, engineers and developers of renewable energy production technologies that work on optimization of energy production and management. This work can be extended to smart grid energy management.