Data_Sheet_1_Geographically Parameterized Residential Sector Energy and Service Profile.PDF Raluca Suciu Ivan Kantor Hür Bütün François Maréchal 10.3389/fenrg.2019.00069.s001 https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Geographically_Parameterized_Residential_Sector_Energy_and_Service_Profile_PDF/9163577 <p>The large share of energy consumption in the residential sector has necessitated better understanding and evaluation of its energy needs, with the objective of identifying possible pathways for improvement. This work uses heat signature models and climate data to build a parameterized residential sector profile for different climatic zones in Europe. The sector profile is validated using Rotterdam, NL as a case study and the results show variations from the real energy demand profile of less than 10%, primarily caused by cultural and climatic differences between Rotterdam and the rest of Western Europe. The energy and service profile constructed herein is well-suited for exploring the best technologies for supplying residential requirements, drawing from the domain of process integration. This work demonstrates the usefulness of the residential profile by applying process integration techniques within a mixed integer linear programming formulation to evaluate optimal energy conversion technologies for different district energy networks and potential waste heat recovery from industrial plants located in the vicinity of the residential area. The results show that switching to a fully electric energy providing system can lead to operating cost savings of 48% and CO<sub>2</sub> emission savings up to 100%, depending on the mix of electricity generation. The utilization of the sector profile is also exemplified using the city of Rotterdam, where it is shown that industrial waste heat recovery can lead to operating cost and environmental impact savings of 9% and 20%, respectively.</p> 2019-07-30 04:34:15 residential sector profile district energy service CO2 network heat recovery process integration industrial symbiosis optimization