Community Multi-node Building Energy Consumption Characteristics And Community Energy Load Forecasting Method

In the context of new urbanization construction and"carbon peak and carbon neutral"strategic goals,low carbon community energy planning for district building clusters has become a necessary part of urban construction.The Low Carbon Community Energy Plan requires the improvement of energy system efficiency from an energy demand-side perspective.It promotes the integration and optimization of renewable energy generation and supply.Accelerating the integration of renewable energy technologies in buildings to promote the green and low-carbon development of buildings can facilitate the implementation of China’s completion of specific work to reduce carbon emissions and help the building energy-saving sector achieve the"double carbon"strategic goal as soon as possible.Accurate forecasting of community energy loads is the basis for rational planning of community renewable energy integration.It is also the prerequisite for matching energy supply with demand and demand-side energy management at the community level.In current district energy planning methodologies,hourly load data is often difficult to obtain at the community level.Therefore,static indicators are often used to estimate or extrapolate based on monthly statistics,making it challenging to capture the dynamic and complex relationships between community buildings and the community environment.Existing community load forecasting methods often simplify community occupant behavior by using deterministic schedules as input conditions without considering the multi-node occupant characteristics in the community context.In summary,current community load forecasting methods have difficulty representing complex energy use scenarios caused by different building types and complex occupant behavior,making it difficult to provide highly accurate load forecasting data at the community energy planning stage.This paper focuses on the community energy planning stage,solving the problem of low accuracy of community load forecasting results due to incomplete primary data and unclear coupling relationships between buildings.The system complexity of community occupants’energy use behavior is clarified by combining the complex adaptive systems theory and the"bottom-up"community load forecasting method.The"bottom-up"community cooling and heating load forecasting method is proposed based on community occupants’agent modeling.The main focus of this paper is divided into the following four points.(1)In order to maximize the ability of typical buildings to represent the various building sub-groups within the community,this paper adopts the"real building physical model"method.It constructs a typical building physical model database covering building functions and basic building information by extracting basic building outlines and POI data of 215 communities in typical cities corresponding to each climate zone in China.(2)In order to obtain the occupancy and energy use schedules of multi-node buildings at the community scale,this paper characterizes community buildings’occupant transfer and energy use behavior from the perspective of’different individuals.’It applies the agent-based modeling method to stochastically represent the complex energy use scenarios in actual community buildings.Compared with the measured data,the R~2=0.8651 and RMSE=0.1826 of the community occupant agent model indicate that the model can reflect the actual building occupancy pattern.(3)This paper constructs a workflow for generating standardized load profiles for community buildings in the community planning stage and proposes the"bottom-up"community cooling and heating load prediction method based on the modeling of community occupant agents.The method proposed in this paper can improve the load factor index by 46.1%compared to the method of load indicators,effectively reducing the initial investment in system design if used for system capacity design,which can effectively reduce the initial investment in system design if this method is used for system capacity design.(4)In this study,three types of typical communities with different functional building ratios are investigated to clarify how functional building ratios affect the energy consumption characteristics of multi-node buildings in the community.This paper finds the features of the effect of different types of buildings on the community’s overall load,and captures the characteristics of load synergy dissipation in different typical community scenarios.The results show that the proposed"bottom-up"community cooling and heating load forecasting method based on community occupant agent modeling can help accurately represent the occupancy pattern of actual building occupants in the community.It can reflect the cooperative load consumption relationship caused by the different energy consumption characteristics of building occupants in each node of the community.This method is suitable for the community energy planning stage where basic information is lacking.The reasonable prediction of community building loads in this stage will help promote the organic integration of renewable energy technologies.