these four technically and economically optimised designs a final optimised design was selected. Within the parametric analysis some of the parameters were fixed and some variable. The fixed parameters enabled a valid comparative baseline to be developed so the results were not skewed by i.e. changes to desired internal temperature. The fixed parameters were for space heating demands using an 18°C set point, hot water demand based on 5 person occupancy and 50L of hot water/ person/ day, and predicted appliance electrical consumption including lighting energy use. Other parametric factors were variable and based on the characteristics of specific elements involved with optimising the energy building’s performance including: wall and window U-value, space heating primary energy source, ventilation rate energy loss with heat recovery, heat recovery efficiency, heating and hot water system efficiency, passive solar gains and internal gains, energy generation by different renewable energy systems, renewable energy system sizing, implementation costs, build costs and running costs. Once a range of technically and economically viable systems were created the equation based modelling could be validated using a dynamic modelling tool for data for accuracy. Dynamic modelling is considered the best way to capture the complex interactions that contribute to thermal performance, demand and their uncertainties. This was achieved using TRNSYS. TRNSYS is a transient systems simulation program developed by the University of Wisconsin and has been available for over 30 years (University of Wisconsin, 2015). It is widely used for energy in building simulation. This enabled different usage and occupancy patterns to be modelled as well as more dynamic passive and solar gain modelling including solar gains, radiative heat, internal radiative gains and wall gains). TRNSYS models thermal behaviour of a building divided into different zones and models thermal demands hourly for each thermal zone (University of Wisconsin, 2015). Thus whilst TRNSYS models are less flexible to rapidly change and observe technical and economic optimisation they offer more detailed and accurate outputs. As such the final optimised design was modelled in TRNSYS to increase certainty in the thermal loads. The design parameters and building attributes of the optimised design were given to a Masters student to input. The
these four technically and economically optimised designs a final optimised design was selected. Within the parametric analysis some of the parameters were fixed and some variable. The fixed parameters enabled a valid comparative baseline to be developed so the results were not skewed by i.e. changes to desired internal temperature. The fixed parameters were for space heating demands using an 18°C set point, hot water demand based on 5 person occupancy and 50L of hot water/ person/ day, and predicted appliance electrical consumption including lighting energy use. Other parametric factors were variable and based on the characteristics of specific elements involved with optimising the building’s energy performance including: wall and window U-value, space heating primary energy source, ventilation rate energy loss with heat recovery, heat recovery efficiency, heating and hot water system efficiency, passive solar gains and internal gains, energy generation by different renewable energy systems, renewable energy system sizing, implementation costs, build costs and running costs. Once a range of technically and economically viable systems were created the equation based modelling could be validated using a dynamic modelling tool for data for accuracy. Dynamic modelling is considered the best way to capture the complex interactions that contribute to thermal performance, demand and their uncertainties. This was achieved using TRNSYS. TRNSYS is a transient systems simulation program developed by the University of Wisconsin and has been available for over 30 years (University of Wisconsin, 2015). It is widely used for energy in building simulation. This enabled different usage and occupancy patterns to be modelled as well as more dynamic passive and solar gain modelling including solar gains, radiative heat, internal radiative gains and wall gains). TRNSYS models thermal behaviour of a building divided into different zones and models thermal demands hourly for each thermal zone (University of Wisconsin, 2015). Thus whilst TRNSYS models are less flexible to rapidly change and observe technical and economic optimisation they offer more detailed and accurate outputs. As such the final optimised design was modelled in TRNSYS to increase certainty in the thermal loads. The design parameters and building attributes of the optimised design were given to a Masters student to input. The
6105467_Final_Thesis_2016_10_01_For_Upload_7.docx