Hermano Bernardo

1. Assessment of the Energy Savings Potential of Daylight Utilization and its Impact on a Building Energy Performance Hermano Bernardo Vienna, 2010

2. Energy efficiency in buildings – some challanges • Reducing energy consumption and greenhouse gases emissions • OptimizingHeating, VentilationandAirConditioning (HVAC) systems • Optimizing artificial lighting systems: • Maximizing the use of natural lighting

3. Energy efficiency in buildings – some challanges • Ensuring good indoor air quality (IAQ): • Adequate ventilation with filtration • Thermal comfort • Evaluating the energy performance: • Determining the energy efficiency classification 3

4. Portuguese energy outlook Buildings: 29% of final energy Buildings: 62% of electricity Source: DGEG, 2006

5. Legal impositions • European Directive 2002/91/EC: Energy Performance of Buildings (EPBD) • SCE - Sistema Nacional de Certificação Energética e da Qualidade do Ar Interior nos Edifícios (Decreto-Lei n.º 78/2006) • RSECE - Regulamento dos Sistemas Energéticos de Climatização em Edifícios (Decreto-Lei n.º 79/2006) • RCCTE - Regulamento das Características de Comportamento Térmico dos Edifícios (Decreto-Lei n.º 80/2006)

6. Portuguese regulations – application • RCCTE • Residential buildings; • Small services buildings without central HVAC systems, or P ≤ 25 kW; • Basis for the simplified methodology – certification of existing buildings. • RSECE • Services buildings: • Large (>1000 m2 or 500 m2); • Small with HVAC (P > 25kW). • Residential buildings with HVAC systems, P > 25kW

7. Case study – Canteen Building Location: Leiria Climate zone: I2V1Norte Main façade: SE Year of construction: 2005

8. Methodology • Development of a computational model of the building • Model calibration • Full energy audit • Detailed characterization of the actual operating conditions of the building • e.g. Occupation, equipments, lighting, temperature regulation... • Simulation of nominal consumptions • Using reference patterns, rather than actual occupation, equipments and lighting profiles and densities • A climate data file is needed • Determination of the Ieenom index and assignment of the Energy Class

9. Calculation of IEE index IEE – Energy efficiency index [kgoe/m2]; FCI – Correction factor for heating; FCV – Correction factor for cooling; Qaq – Energy used for heating [kgoe/year]; Qarr – Energy used for cooling [kgoe/year]; Qout – Energy used for other purposes [kgoe/ano]; Ap – Net floor surface [m2].

10. Building energy simulation • Set of parameters has to be defined: • e.g. working hours diagrams for lighting, occupation, equipments, air changes, ventilation equipments, heating and cooling temperatures • Confort conditions: • Heating season: air temperature of 20ºC • Cooling season : 25ºC with 50% relative humidity • Primary energy conversion factors • 0,290 kgoe/kWh for electricity • 0,086 kgoe/kWh for natural gas

11. Building energy simulation • Simulation tool – DesignBuilder for EnergyPlus: • Climatic data; • Definition of the geometry and thermal zones to be included in simulation; • Building envelope characterization; • Internal loads definition; • Parameters of infiltration and ventilation systems; • Environmental control definitions. 11

12. Building model

13. Model calibration • Comparison between annual energy consumptions

14. Base case simulation

15. Lighting systems optimization simulation

16. Comparison of results – primary energy • Energy for lighting systems reduced in 25% but, due thermal load reduction, energy for heating increased in 4%.

17. Annual energy savings potential • Globally, there is an energy saving potential of 2.085kgoe, which means a total cost reduction of 587€ and a reduction in CO2 emissions of 3.172 kgCO2e.

18. Energy labelling of the case study building • Full simulations were performed under two scenarios: • Reference case; • Maximization the use of natural lighting.

19. Reference case - with nominal profiles IEE = 104,89 kgoe/m2 Class B

20. Maximization of the use of natural lighting IEE = 100,59 kgoe/m2 Class A

21. Conclusions • A lighting control system that maximizes the use of natural lighting leads to a considerable reduction of the IEEnom index and the building becomes a Class A building. • During the winter, artificial lighting systems can be beneficial and should always be taken into account when performing simulations and sizing HVAC systems, as they represent a thermal load which contributes to the building’s heating. • During the summer, lighting systems should also be considered, this time because they represent an extra load that must be removed by the cooling system, in case of its existence. • Computational simulation enables the comparison beforehand, in terms of energy performance and thermal comfort, of different alternatives.