A study on carbon emission calculation in operation stage of residential buildings based on micro electricity usage behavior:Three case studies in China

Along with the improvement of social productivity and living standard,residential buildings generate a growing portion of carbon emissions,especially during the operation stage.However,energy use behaviors are usually ignored in carbon emission calculation.This study focuses on calculating carbon emissions during the operation stage for residential buildings based on the characteristics of energy use behaviors in different regions.Firstly,we investigated energy use behaviors in dwellings across three cities in China:Xi'an,Shanghai and Fuzhou.Then,we established calibrated carbon emission models and optimization models with different green building measures for residential buildings.The results of this research reveal a significant disparity between the energy usage habits of residents in different climate regions.The carbon emissions of residential electricity bills in Xi'an,Shanghai and Fuzhou are 13.6 kgCO_(2)/(m^(2)·a)(excluding central heating),29.3 kgCO_(2)/(m^(2)·a)and 17.2 kgCO_(2)/(m^(2)·a),respectively.Equipment carbon emissions account for 32.2%-64.1% of the total.In comparison to the model based on internal standard setting,the accuracy of the models using actual internal has improved by 25.9%-37.4%.The three-star green building methods have the highest carbon reduction rate among different star buildings,the emission reduction rates are around 30%.This study's findings are useful for carbon emission calculation and green building design of residential buildings in the future.

Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior

A model-based optimal dispatch framework was proposed to optimize operation of residential flexible loads considering their real-life operating characteristics,energy-related occupant behavior,and the benefits of different stakeholders.A pilot test was conducted for a typical household.According to the monitored appliance-level data,operating characteristics of flexible loads were identified and the models of these flexible loads were developed using multiple linear regression and K-means clustering methods.Moreover,a data-mining approach was developed to extract the occupant energy usage behavior of various flexible loads from the monitored data.Occupant behavior of appliance usage,such as daily turn-on times,turn-on moment,duration of each operation,preference of temperature setting,and flexibility window,were determined by the developed data-mining approach.Based on the established flexible load models and the identified occupant energy usage behavior,a many-objective nonlinear optimal dispatch model was developed aiming at minimizing daily electricity costs,occupants’dissatisfaction,CO_(2) emissions,and the average ramping index of household power profiles.The model was solved with the assistance of the NSGA-III and TOPSIS methods.Results indicate that the proposed framework can effectively optimize the operation of household flexible loads.Compared with the benchmark,the daily electricity costs,CO_(2) emissions,and average ramping index of household power profiles of the optimal plan were reduced by 7.3%,6.5%,and 14.4%,respectively,under the TOU tariff,while those were decreased by 9.5%,8.8%,and 23.8%,respectively,under the dynamic price tariff.The outputs of this work can offer guidance for the day-ahead optimal scheduling of household flexible loads in practice.