Multi-energy complementary based near zero energy building collaborative control system and method

Abstract

This invention belongs to the field of building energy management technology. It discloses a collaborative control system and method for near-zero energy buildings based on multi-energy complementarity, comprising: acquiring a multi-energy complementary operation task set for the target near-zero energy building; querying the operation planning text of each building energy subsystem and extracting subsystem attributes and a subsystem operation planning list; mapping to a collaborative control period to obtain collaborative mapping results; generating an energy supply reference information set based on the collaborative mapping results and subsystem attributes; extracting operation status information; constructing constraints based on the operation status information and the energy supply reference information set, determining optimization objectives, and optimizing the energy collaborative control strategy; extracting the planned energy supply path for each multi-energy complementary subsystem in the energy collaborative control strategy, and executing building energy collaboration during the collaborative control period; significantly reducing the building's operating energy consumption.

Description

Technical Field

[0001] This invention relates to the field of building energy management technology, and more specifically, to a near-zero energy building collaborative control system and method based on multi-energy complementarity. Background Technology

[0002] With the escalating global energy crisis, energy conservation and emission reduction in the building sector have become a key path to achieving sustainable development. The proportion of building energy consumption in total social energy consumption continues to rise, and the industry consensus is to promote the evolution of buildings from high energy consumption to ultra-low energy consumption, near-zero energy consumption, and even zero energy consumption. As an important stage in this evolution process, near-zero energy buildings achieve a significant reduction in building operating energy consumption while ensuring indoor environmental comfort through the integrated application of key technologies such as high-performance building envelope, efficient fresh air heat recovery system, and renewable energy utilization.

[0003] However, existing multi-energy complementary near-zero energy buildings have significant technical deficiencies in system coordination and control, a problem that is particularly prominent under dynamic load changes. Specifically, near-zero energy buildings integrate multiple interconnected subsystems, including high-performance building envelopes, thermal bridge-free designs, excellent airtightness, fresh air heat recovery, ground source heat pumps, air source heat pumps, and energy storage systems. These subsystems exhibit complex spatiotemporal coupling characteristics and energy transfer correlations during operation. When buildings face dynamic load disturbances such as diurnal temperature fluctuations, changes in occupant activity, and seasonal climate transitions, the lack of effective coordinated response mechanisms between different energy subsystems leads to frequent occurrences of inaccurate system switching timing judgments and mismatches between energy supply and demand. For example, during transitions... Under seasonal conditions, when building heat load fluctuates, if the switching control between ground source heat pump systems and air source heat pump systems relies solely on a single temperature threshold, frequent system start-ups and shutdowns or continuous operation of energy forms with lower energy efficiency often occur. Simultaneously, if the charging and discharging sequence of the energy storage system is not optimized in conjunction with factors such as the operating status of the heat pump system, the efficiency of fresh air heat recovery, and the thermal inertia of the building envelope, it will lead to a decrease in renewable energy utilization and an increase in auxiliary energy consumption. Furthermore, in near-zero energy buildings with extremely high airtightness, the operation of the fresh air system directly affects the indoor thermal and humidity environment and heating and cooling loads. If the fresh air heat recovery process lacks dynamic coordination with the active energy supply system, an energy supply gap will occur during high-load periods, while energy waste will result during low-load periods.

[0004] The current mainstream approach to solving such problems is to adopt a hierarchical control strategy or rule-based logic control, which achieves system management by pre-setting operating modes and switching conditions under different working conditions. However, this static rule-based control method is difficult to adapt to the dynamic nonlinear characteristics caused by the coupling of multiple systems in near-zero energy buildings. Its control parameters rely on a lot of on-site debugging and experience accumulation, and the applicability of the preset rules decreases rapidly when the building usage mode changes or the climate conditions deviate from the design conditions. More importantly, existing control methods mostly focus on the local optimization of a single subsystem, lacking a holistic understanding of the inherent correlation between the thermal performance, airtightness, heat recovery efficiency, and multi-energy supply capacity of the building envelope. This makes it impossible to achieve optimal energy efficiency and dynamic balance of load response at the system level, ultimately resulting in the failure to fully release the energy-saving potential of near-zero energy buildings in actual operation. There is a significant deviation between energy consumption performance and design targets, which limits the in-depth application and promotion of multi-energy complementary technology in near-zero energy buildings.

[0005] In view of this, the present invention proposes a near-zero energy building collaborative control system and method based on multi-energy complementarity to solve the above problems. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a near-zero energy building collaborative control method based on multi-energy complementarity, comprising:

[0007] Step S1: Obtain the multi-energy complementary operation task set of the target near-zero energy building; wherein, the multi-energy complementary operation task set includes coordinated control periods and energy supply identifiers;

[0008] Step S2: Based on the energy supply identifier, query the operation plan text of each multi-energy complementary subsystem, and extract the subsystem attributes and subsystem operation plan list;

[0009] Step S3: Map the energy supply plan tasks in the subsystem operation planning list to the collaborative control period to obtain the collaborative mapping result. Based on the collaborative mapping result and the thermal inertia information and heat recovery efficiency information in the subsystem attributes, generate an energy supply reference information set.

[0010] Step S4: Access the building energy supply resources and extract the operating status information of the multi-energy complementary subsystems in the building energy supply resources during different load cycles during the coordinated control period;

[0011] Step S5: Based on the operating status information and the energy supply reference information set, considering the thermal inertia information of each multi-energy complementary subsystem, the constraints of building energy supply resource construction, and the optimization objective of determining the building energy response intensity, optimize and solve the energy collaborative control strategy.

[0012] Step S6: Extract the planned energy supply path of each multi-energy complementary subsystem in the energy collaborative control strategy, and perform building energy coordination during the collaborative control period.

[0013] A near-zero energy building collaborative control system based on multi-energy complementarity includes:

[0014] Data acquisition module: acquires the multi-energy complementary operation task set of the target near-zero energy building; wherein, the multi-energy complementary operation task set includes coordinated control periods and energy supply identifiers;

[0015] Data extraction module: Based on the energy supply identifier, query the operation plan text of each multi-energy complementary subsystem, and extract the subsystem attributes and the subsystem operation plan list;

[0016] Reference generation module: Maps the energy supply plan tasks in the subsystem operation plan list to the collaborative control period, obtains the collaborative mapping result, and generates an energy supply reference information set based on the collaborative mapping result and the thermal inertia information and heat recovery efficiency information in the subsystem attributes.

[0017] Access module: Accesses building energy supply resources and extracts the operating status information of multi-energy complementary subsystems in the building energy supply resources during the coordinated control period under different load cycles;

[0018] Strategy Solving Module: Based on the operating status information and energy supply reference information set, considering the thermal inertia information of each multi-energy complementary subsystem, the constraints of building energy supply resource construction, and the optimization objective determined by the building energy response intensity, the module optimizes and solves the energy collaborative control strategy.

[0019] The coordination module extracts the planned energy supply path of each multi-energy complementary subsystem in the energy coordination control strategy and executes building energy coordination during the coordination control period.

[0020] The technical effects and advantages of this invention based on a multi-energy complementary near-zero energy building collaborative control method are as follows:

[0021] This invention captures the spatiotemporal coupling relationship between subsystems by identifying energy supply, constructs a multi-energy complementary energy chain, and transforms it into an operational task to form the basis of a dynamic task set. By extracting information summaries and keywords through natural language processing tools, dependency parsing and semantic correlation analysis generate attribute information and operational planning information, accurately characterizing the intrinsic relationship between the thermal performance, airtightness level and heat recovery efficiency of the building envelope, avoiding the dependence of static rules on experience, and achieving a fine analysis of the coupling characteristics of multiple systems. By mapping energy supply plans to coordinated control periods, the output and demand intensity of load cycles are converted. Through a spatiotemporal adjustment process that integrates the quantified values ​​of heat transfer delay and heat loss suppression layer by layer, an energy supply reference information set is generated. This reflects the dynamic coordination between the thermal inertia of the building envelope and the heat recovery of fresh air, initially achieving spatiotemporal matching between supply and demand, and reducing the risk of frequent start-stop switching of heat pump systems during transitional seasons. After accessing building energy supply resources, standby periods and maximum supply intensity are extracted. Through the boundary adjustment process of overlapping and matching standby periods and load cycles, operational status information is generated. This adjustment prioritizes the integration of disturbance boundaries caused by diurnal temperature differences and changes in human activity, ensuring that the supply status of ground source heat pumps, air source heat pumps, and energy storage systems is highly consistent with the maximum supply intensity of building load types, reducing energy supply gaps or low-load waste during seasonal climate transitions. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the near-zero energy building collaborative control method based on multi-energy complementarity of the present invention;

[0023] Figure 2 This is a schematic diagram of the near-zero energy building collaborative control system based on multi-energy complementarity of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1

[0026] Please see Figure 1 As shown, this embodiment is based on a multi-energy complementary near-zero energy building collaborative control method, including:

[0027] Step S1: Obtain the multi-energy complementary operation task set of the target near-zero energy building; wherein, the multi-energy complementary operation task set includes coordinated control periods and energy supply identifiers;

[0028] Step S2: Based on the energy supply identifier, query the operation plan text of each multi-energy complementary subsystem, and extract the subsystem attributes and subsystem operation plan list;

[0029] Step S3: Map the energy supply plan tasks in the subsystem operation planning list to the collaborative control period to obtain the collaborative mapping result. Based on the collaborative mapping result and the thermal inertia information and heat recovery efficiency information in the subsystem attributes, generate an energy supply reference information set.

[0030] Step S4: Access the building energy supply resources and extract the operating status information of the multi-energy complementary subsystems in the building energy supply resources during different load cycles during the coordinated control period;

[0031] Step S5: Based on the operating status information and the energy supply reference information set, considering the thermal inertia information of each multi-energy complementary subsystem, the constraints of building energy supply resource construction, and the optimization objective of determining the building energy response intensity, optimize and solve the energy collaborative control strategy.

[0032] Step S6: Extract the planned energy supply path of each multi-energy complementary subsystem in the energy collaborative control strategy, and perform building energy coordination during the collaborative control period.

[0033] In a preferred embodiment, the step of obtaining the multi-energy complementary operation task set of the target near-zero energy building specifically includes:

[0034] The goal is to obtain the multi-energy complementary energy chain of the target near-zero energy building, in which each subsystem, acting as an energy supply node, participates. Each multi-energy complementary subsystem in the multi-energy complementary energy chain is integrated within the target near-zero energy building. The multi-energy complementary subsystem includes energy supply units of different energy forms, such as ground source heat pump subsystems, air source heat pump subsystems, energy storage subsystems, fresh air heat recovery subsystems, and photovoltaic power generation systems. Each multi-energy complementary subsystem participates in the entire building's energy supply process as an energy supply node.

[0035] Each extracted multi-energy complementary energy chain is converted into a corresponding multi-energy complementary subsystem operation task. The energy supply identifier of each multi-energy complementary subsystem operation task is combined with the preset collaborative control period to construct a multi-energy complementary operation task set for the target near-zero energy building.

[0036] In this embodiment, by acquiring each multi-energy complementary energy chain within a near-zero energy building, and based on the energy supply identifier and collaborative control period of each multi-energy complementary energy chain, a multi-energy complementary operation task is constructed for each multi-energy complementary energy chain, thereby generating a multi-energy complementary operation task set for the target near-zero energy building. The collaborative control period is a pre-defined time range for building energy collaborative control, such as a 24-hour operation cycle, a weekly operation cycle, or a monthly operation cycle.

[0037] In a preferred embodiment, the steps of querying the operation plan text of each multi-energy complementary subsystem based on the energy supply identifier, and extracting the subsystem attributes and subsystem operation plan list, specifically include:

[0038] Based on the energy supply identifier, the operation planning text of each multi-energy complementary subsystem is queried in the building energy management system; wherein, the operation planning text includes several information paragraphs describing different planning information categories in the multi-energy complementary operation process; the operation planning text is text information stored in the building energy management system regarding the operation plan, performance parameters, system configuration, energy supply planning, etc. of each multi-energy complementary subsystem.

[0039] A natural language processing (NLP) tool trained with domain knowledge is used to extract information summaries from each information segment. Based on the keywords recorded in the information summaries, a list of subsystem attributes and subsystem operation plans for the multi-energy complementary subsystem is generated. The NLP tool trained with domain knowledge employs a pre-trained language model based on the Transformer architecture as its foundation. This model is first pre-trained on a large-scale general corpus to learn general language representations, and then continuously pre-trained and fine-tuned on domain knowledge corpora such as professional literature, technical specifications, and operation reports in the building energy field. This enables the model to accurately understand the professional terminology, technical concepts, and logical relationships in the building energy field. Specifically, the construction process of this natural language processing tool includes: first, collecting a domain knowledge corpus including near-zero energy building design standards, multi-energy complementary system technical manuals, heat pump system operation specifications, and fresh air heat recovery technical documents, and cleaning, segmenting, and labeling the corpus; then, using masked language modeling tasks and next sentence prediction tasks to perform domain-adaptive training on the pre-trained model, enabling the model to learn the semantic features of the building energy domain; finally, for the information summary extraction task, constructing a supervised dataset containing information paragraph-summary pairs, and using a sequence-to-sequence generation framework to fine-tune the model, enabling it to automatically extract information summaries containing core information such as key technical parameters, operation plans, and system configurations.

[0040] Furthermore, based on the keywords recorded in the information digest, the steps for analyzing and generating the subsystem attributes and subsystem operation plan list of the multi-energy complementary subsystem specifically include:

[0041] Natural language processing tools are used to perform word segmentation, dependency parsing, and keyword association extraction on each information summary to obtain several keywords recorded in the information summary. Among them, the dependency parsing is used to identify the grammatical dependencies between words in the sentence, thereby understanding the hierarchical relationships between concepts such as thermal inertia information, heat recovery efficiency information, and energy supply nodes; the keyword association extraction process analyzes the dependency roles and semantic functions of words in the sentence to extract key information related to subsystem attributes and operation planning.

[0042] Based on the dependency relationships of each keyword and its semantic relevance to each preset keyword in the preset keyword set, attribute information and operation planning information for each multi-energy complementary subsystem are analyzed and generated. The attribute information includes thermal inertia information, heat recovery efficiency information, and identifiers of upstream and downstream energy supply nodes. The operation planning information includes the energy output period of each multi-energy complementary subsystem and the energy output intensity and load demand intensity of each energy output period. The thermal inertia information includes quantified values ​​of heat transfer delay for the building envelope and quantified values ​​of heat loss suppression for building airtightness. The quantified value of heat transfer delay characterizes the heat transfer time delay characteristics of the building envelope under external thermal disturbances. The heat loss suppression quantification value characterizes the building's airtightness's ability to suppress heat loss; the heat recovery efficiency information characterizes the efficiency parameters of the fresh air heat recovery subsystem in recovering exhaust heat under different operating conditions; the upstream and downstream energy supply node identifiers are used to identify the upstream and downstream energy supply nodes of the current subsystem in the multi-energy complementary energy chain. For example, the upstream node of the energy storage subsystem may be a photovoltaic power generation system, and the downstream node may be a ground source heat pump subsystem; the semantic relevance is determined by calculating the cosine similarity of the word vectors of the keywords with the words in the preset keyword set. The preset keyword set is divided into a first preset keyword set associated with attribute information and a second preset keyword set associated with operation planning information.

[0043] Based on the attribute information, construct the subsystem attributes of the multi-energy complementary subsystem; based on the operation planning information, construct the subsystem operation planning list of the multi-energy complementary subsystem.

[0044] In this embodiment, by extracting the energy supply identifier from the operation tasks of each multi-energy complementary subsystem, querying the operation plan text in the building energy management system, extracting information summaries using a natural language processing tool trained with domain knowledge, and processing the information summaries using techniques such as word segmentation and dependency parsing, and then analyzing and generating several attribute information and several operation plan information for each multi-energy complementary subsystem based on the dependency relationship of each keyword in the information summary and the semantic relevance of each preset keyword in the first preset keyword set associated with attribute information and the second preset keyword set associated with operation plan information, several attribute information and several operation plan information for each multi-energy complementary subsystem are generated, and the subsystem attributes and subsystem operation plan list for each multi-energy complementary subsystem are determined accordingly.

[0045] In a preferred embodiment, the step of mapping the energy supply plan tasks in the subsystem operation planning list to the collaborative control period to obtain the collaborative mapping result, and generating an energy supply reference information set based on the collaborative mapping result and the thermal inertia information and heat recovery efficiency information in the subsystem attributes, specifically includes:

[0046] The energy supply plan tasks for different time periods in the subsystem operation planning list are converted into energy output intensity and load demand intensity for each load cycle, and the energy output intensity and load demand intensity for each load cycle are mapped to the collaborative control period to obtain the energy output intensity and load demand intensity for different load cycles in the collaborative control period; wherein, the load cycle is the time cycle of building energy demand changes, for example, a day can be divided into multiple load cycles according to the building usage pattern, including the nighttime low load cycle, the morning load rise cycle, the daytime high load cycle, the evening load fall cycle, etc.

[0047] Based on the energy output intensity and load demand intensity of each multi-energy complementary subsystem under different load cycles, and the thermal inertia and heat recovery efficiency information in the subsystem attributes of the multi-energy complementary subsystem, a spatiotemporal adjustment process of merging the quantified values ​​of heat transfer delay and heat loss suppression is used to generate an energy supply reference information set for each multi-energy complementary subsystem during the coordinated control period. Specifically, the spatiotemporal adjustment process of merging the quantified values ​​of heat transfer delay and heat loss suppression involves: first, calculating the time delay of external thermal disturbances reaching the interior based on the quantified value of heat transfer delay of the building envelope, thereby determining the time shift impact of the building envelope on the load demand intensity; then, calculating the degree of suppression of building heat loss under different load cycles based on the quantified value of heat loss suppression of building airtightness, thereby correcting the amplitude of the load demand intensity; finally, combining the heat recovery efficiency information of the fresh air heat recovery subsystem, calculating the compensating effect of the heat recovery process on the energy output intensity. By merging the above thermal performance parameters layer by layer to adjust the time and intensity of the energy supply plan, an energy supply reference information set that comprehensively considers building thermal inertia, airtightness, and heat recovery efficiency is generated.

[0048] In this embodiment, after obtaining the subsystem attributes and subsystem operation plan list of each multi-energy complementary subsystem, the energy output intensity and load demand intensity of different load cycles in the coordinated control period are obtained by mapping the subsystem operation plan list to the coordinated control period. Then, the mapping results are spatiotemporally adjusted and fused with the thermal inertia information and heat recovery efficiency information in the subsystem attributes of the multi-energy complementary subsystem to determine the energy supply reference information set of each multi-energy complementary subsystem in the coordinated control period, providing data support for the subsequent solution of the energy coordinated control strategy.

[0049] In a preferred embodiment, the step of accessing building energy supply resources and extracting the operating status information of multi-energy complementary subsystems within the building energy supply resources during different load cycles during the coordinated control period specifically includes:

[0050] Access building energy supply resources and extract the standby time periods and maximum supply intensity for different building load types from the multi-energy complementary subsystems within these resources. The building energy supply resources include various energy supply equipment and systems configured in near-zero energy buildings, including ground source heat pump units, air source heat pump units, energy storage devices, and fresh air heat recovery devices. The standby time periods are the time periods during which each multi-energy complementary subsystem is in a dispatchable state. The building load types include different types of energy demands such as heating load, cooling load, ventilation load, and domestic hot water load.

[0051] Based on the standby time period of each multi-energy complementary subsystem and the coordinated control time period, the operational status information of different load cycles within the coordinated control time period in the building energy supply resources is extracted through the boundary adjustment process of overlapping and matching standby time periods and load cycles. Specifically, the boundary adjustment process of overlapping and matching standby time periods and load cycles involves: performing time interval intersection calculations on the standby time period and coordinated control time period of each multi-energy complementary subsystem to identify the available standby time period within the coordinated control time period; then aligning and matching the time boundaries of the available standby time period and the load cycle; when there is partial overlap between the standby time period and the load cycle, the standby time period is segmented or merged according to the boundary of the load cycle, so that the operational status information can be organized according to the time granularity of the load cycle; finally, operational status information divided according to the load cycle is generated, with each load cycle corresponding to the dispatchable multi-energy complementary subsystems and their supply capacity within that time period.

[0052] The operational status information includes the supply status of each multi-energy complementary subsystem and the maximum supply intensity for different building load types. The supply status indicates whether the multi-energy complementary subsystem is in a supply-available state, a standby state, or a maintenance state during the load cycle.

[0053] In this embodiment, by accessing the building energy supply resources provided by each multi-energy complementary subsystem, the standby time periods of several multi-energy complementary subsystems and the maximum supply intensity for different building load types are extracted. Then, by matching the overlap between the standby time period and the coordinated control time period and adjusting the load cycle boundary, the operating status information containing the supply status and maximum supply intensity in different load cycles within the coordinated control time period is extracted.

[0054] In a preferred embodiment, based on operational status information and an energy supply reference information set, considering the thermal inertia information of each multi-energy complementary subsystem, the constraints of building energy supply resource construction, and the optimization objective determined by the building energy response intensity, the steps for optimizing the solution of the energy collaborative control strategy are specifically included:

[0055] Based on the supply status of the multi-energy complementary subsystem, the single supply intensity for different building load types, and the attribute information and operation planning information of each multi-energy complementary subsystem;

[0056] Each load cycle of each multi-energy complementary subsystem during the coordinated control period is allocated to the corresponding supply task period from the corresponding energy supply node to the next energy supply node, or the subsystem scheduling period between two adjacent supply task periods. The supply task period is the time during which the multi-energy complementary subsystem performs energy transfer from the current energy supply node to the next energy supply node, such as the time during which a ground source heat pump subsystem supplies heat to the terminal heating system. The subsystem scheduling period is the time during which system switching, mode conversion, or standby adjustment occurs between two adjacent supply task periods, such as the transition period from ground source heat pump energy supply mode to air source heat pump energy supply mode.

[0057] Based on the first constraint condition constructed from the quantified value of heat transfer delay and the quantified value of heat loss suppression, the second constraint condition constructed from the supply path of building energy supply resources, the third constraint condition constructed from the supply intensity of building energy supply resources, and the optimization objective constructed from the response delay of building energy supply resources, the energy coordination control strategy is optimized and solved using an optimization algorithm.

[0058] Furthermore, based on the first constraint constructed from the quantified values ​​of heat transfer delay and heat loss suppression, the second constraint constructed from the supply path of building energy supply resources, the third constraint constructed from the supply intensity of building energy supply resources, and the optimization objective constructed from the response delay of building energy supply resources, an optimization algorithm is used to optimize and solve the steps of the energy coordinated control strategy, specifically including:

[0059] The first constraint condition is that the energy storage intensity of each multi-energy complementary subsystem in each load cycle, determined by the sum of the energy output intensity of each multi-energy complementary subsystem in each load cycle and the single supply intensity of the building energy supply resources allocated to each supply task period, is less than the heat recovery efficiency information of the multi-energy complementary subsystem. Furthermore, the first constraint condition is that the load storage intensity of each multi-energy complementary subsystem in each load cycle, determined by the sum of the load demand intensity of each multi-energy complementary subsystem in each load cycle and the single supply intensity of the building energy supply resources allocated to the upstream energy supply node of the multi-energy complementary subsystem in each supply task period, is less than the thermal inertia information of the multi-energy complementary subsystem. This first constraint condition ensures that during energy output, the energy storage intensity of each multi-energy complementary subsystem does not exceed the upper limit of its heat recovery capacity, and simultaneously, when receiving energy from the upstream energy supply node, its load storage intensity does not exceed the thermal inertia bearing capacity determined by the heat transfer delay characteristics and airtight heat loss suppression characteristics of the building envelope, thereby avoiding problems such as energy storage overflow or insufficient load response capacity during energy supply.

[0060] The second constraint is defined as follows: the heat transfer path between the location information of the energy supply node where each building's energy supply resource is located at the end of the previous supply task period and the location information of the energy supply node where it is located at the beginning of the next supply task period is less than the product of the duration of the subsystem scheduling period between the two supply task periods and the standard thermal response speed of the multi-energy complementary subsystem; and the heat transfer path between the location information of the energy supply node of each building's energy supply resource in each supply task period and the location information of the next energy supply node is less than the product of the duration of that supply task period and the standard thermal response speed of the multi-energy complementary subsystem. The heat transfer path in the second constraint represents the energy transfer from one energy supply node in the multi-energy complementary system. The path length to another energy supply node can be quantified by the system pipeline length, the flow distance of the heat transfer medium, or the equivalent thermal resistance distance. The standard thermal response speed characterizes the heat transfer rate of the multi-energy complementary subsystem under standard operating conditions. This parameter is determined by a combination of factors such as the system's heat capacity, heat transfer coefficient, and fluid velocity. The second constraint ensures that when the building's energy supply resources are switched between adjacent supply task periods, the heat transfer path can be completed within the subsystem scheduling period at the standard thermal response speed. At the same time, it ensures that the heat transfer path from the energy supply node to the next energy supply node can also be completed within a single supply task period, thereby avoiding system switching delays or supply response lags.

[0061] The third constraint condition is that the single supply intensity of several building energy supply resources allocated in each supply task period is less than the maximum supply intensity of each building energy supply resource for the corresponding building load type. The third constraint condition ensures that the actual supply intensity of the scheduled building energy supply resources in each supply task period does not exceed its designed maximum supply capacity, thereby avoiding system failure or efficiency reduction caused by equipment overload operation.

[0062] The optimization objective is to minimize the sum of the supply paths during each supply task period and the sum of the scheduling paths during each subsystem scheduling period of the building's energy supply resources within the coordinated control period. This optimization objective aims to minimize the total length of the energy transfer paths of the multi-energy complementary system throughout the entire coordinated control period. This optimization objective is equivalent to minimizing the system's heat transfer loss and response delay, thereby improving the overall energy efficiency and load response speed of the multi-energy complementary system. By optimizing the allocation scheme of energy supply resources in different supply task periods and subsystem scheduling periods, the system's coordinated control with the shortest heat transfer path is achieved.

[0063] An optimization algorithm is employed to optimize the energy coordinated control strategy that allocates each multi-energy complementary subsystem to the corresponding supply task period from the corresponding energy supply node to the next energy supply node or the subsystem scheduling period between two adjacent supply task periods during each load cycle in the coordinated control period. The optimization algorithm uses heuristic optimization algorithms such as mixed-integer linear programming or genetic algorithms. This algorithm takes the scheduling decision of each multi-energy complementary subsystem in each load cycle as the decision variable, the first constraint, the second constraint, and the third constraint as the constraint set, and the minimum sum of the supply path and the scheduling path as the objective function. Through an iterative optimization process, the energy coordinated control strategy that satisfies all constraints and has the optimal objective function is found.

[0064] In this embodiment, by acquiring building energy supply resources, considering the first constraint condition constructed based on the quantified values ​​of heat transfer delay and heat loss suppression, the second constraint condition constructed based on the supply path of building energy supply resources, the third constraint condition constructed based on the supply intensity of building energy supply resources, and the optimization objective constructed based on the response delay of building energy supply resources, an optimization algorithm is used to optimize and solve the energy collaborative control strategy. This achieves the allocation of energy resources and the execution of building energy collaborative actions for each energy supply node under the operation task of each multi-energy complementary subsystem during the collaborative control period. Considering the influence of energy transfer between each energy supply node in the multi-energy complementary system and the influence of factors such as the heat transfer delay characteristics of the building envelope, the airtightness heat loss suppression characteristics, heat recovery efficiency, and energy supply capacity of each multi-energy complementary subsystem, by having all energy supply nodes in the entire multi-energy complementary energy chain share all energy supply resources and operating status information, the collaborative response capability of the multi-energy complementary system and the rationality and intelligence of the allocation of energy supply tasks for near-zero energy buildings are improved. At the same time, the energy supply resource utilization rate and energy response efficiency are improved, the energy-saving potential of near-zero energy buildings is fully released, and the building's operating energy consumption is reduced.

[0065] In a preferred embodiment, the planned energy supply path of each multi-energy complementary subsystem in the energy collaborative control strategy is extracted, and the building energy collaborative action steps during the collaborative control period are executed, specifically including:

[0066] The load cycle allocated from the corresponding energy supply node to the next energy supply node and the location information of the two energy supply nodes are extracted during each supply task period of each multi-energy complementary subsystem in the energy coordinated control strategy. A path smoothing adjustment process is then used to generate the planned energy supply path for each multi-energy complementary subsystem. This path smoothing adjustment process is used to perform continuous smoothing processing on the discretized supply path obtained by the optimization algorithm. Specifically, it includes: firstly, extracting the positional jumps of energy supply nodes between adjacent supply task periods to identify path abrupt changes that may lead to frequent system switching; then, using cubic spline interpolation or Bézier curve fitting methods to smooth the path abrupt changes, generating a continuous and stable energy supply path curve under the premise of satisfying the second constraint condition; finally, by setting a path smoothness threshold and a maximum curvature constraint, ensuring that the smoothed planned energy supply path can both avoid frequent system starts and stops and guarantee the timeliness of load response.

[0067] Based on the heat transfer path determined by the load cycle and location information of the energy supply node for each supply task period recorded in the planned energy supply path, supply guidance information for each multi-energy complementary subsystem during the coordinated control period is generated. The supply guidance information includes the start and stop times, supply intensity setpoints, supply mode switching instructions, and coordination sequence with other subsystems for each multi-energy complementary subsystem during each supply task period. This supply guidance information provides execution instructions for the underlying controller of the multi-energy complementary subsystem.

[0068] The supply guidance information is sent to each multi-energy complementary subsystem, and the consistency of the supply path execution is verified through real-time feedback loops, enabling coordinated building energy actions during the coordinated control period. Specifically, the real-time feedback loop verification process involves: during the execution of the supply guidance information by the multi-energy complementary subsystems, real-time collection of feedback information such as the actual supply intensity, operating status, and energy supply node location of each subsystem; comparison of the feedback information with the expected values ​​in the planned energy supply path to calculate the execution deviation; when the execution deviation exceeds a preset threshold, a local path adjustment mechanism is triggered, fine-tuning the supply intensity and timing of the current and subsequent supply task periods without altering the overall energy coordinated control strategy; through this real-time feedback loop process, the consistency of the planned energy supply path execution is ensured, improving the robustness and adaptability of the multi-energy complementary system in actual operation.

[0069] In this embodiment, after obtaining the final energy collaborative control strategy, the load cycle and energy supply node location of each multi-energy complementary subsystem corresponding to each supply task period are extracted from the energy collaborative control strategy. A planned energy supply path is generated through a path smoothing adjustment process. Then, supply guidance information is generated based on the planned energy supply path, and the consistency of execution is ensured through a real-time feedback loop verification mechanism. In this way, the energy resource allocation and building energy collaborative action execution for each energy supply node under the operation task of each multi-energy complementary subsystem during the collaborative control period are realized.

[0070] Through the above technical solution, this embodiment can effectively solve the problems of inaccurate system switching timing judgment and mismatch between energy supply and demand in the collaborative control of multi-energy complementary systems in existing near-zero energy buildings. It fully considers the inherent correlation between the thermal performance, airtightness, heat recovery efficiency, and multi-energy supply capacity of the building envelope, and achieves optimal energy efficiency and dynamic balance of load response at the system level. This allows the energy-saving potential of near-zero energy buildings to be fully released in actual operation, and the actual energy consumption performance of the building is highly consistent with the design target, thus promoting the in-depth application and promotion of multi-energy complementary technology in near-zero energy buildings.

[0071] Example 2

[0072] Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A near-zero energy building collaborative control system based on multi-energy complementarity is provided, including:

[0073] Data acquisition module: acquires the multi-energy complementary operation task set of the target near-zero energy building; wherein, the multi-energy complementary operation task set includes coordinated control periods and energy supply identifiers;

[0074] Data extraction module: Based on the energy supply identifier, query the operation plan text of each multi-energy complementary subsystem, and extract the subsystem attributes and the subsystem operation plan list;

[0075] Reference generation module: Maps the energy supply plan tasks in the subsystem operation plan list to the collaborative control period, obtains the collaborative mapping result, and generates an energy supply reference information set based on the collaborative mapping result and the thermal inertia information and heat recovery efficiency information in the subsystem attributes.

[0076] Access module: Accesses building energy supply resources and extracts the operating status information of multi-energy complementary subsystems in the building energy supply resources during the coordinated control period under different load cycles;

[0077] Strategy Solving Module: Based on the operating status information and energy supply reference information set, considering the thermal inertia information of each multi-energy complementary subsystem, the constraints of building energy supply resource construction, and the optimization objective determined by the building energy response intensity, the module optimizes and solves the energy collaborative control strategy.

[0078] The coordination module extracts the planned energy supply path of each multi-energy complementary subsystem in the energy coordination control strategy and executes building energy coordination during the coordination control period.

[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for collaborative control of near zero energy buildings based on multi-energy complementarity, characterized in that, include: Step S1: Obtain the multi-energy complementary operation task set of the target near-zero energy building; wherein, the multi-energy complementary operation task set includes coordinated control periods and energy supply identifiers; Step S2: Based on the energy supply identifier, query the operation plan text of each multi-energy complementary subsystem, and extract the subsystem attributes and subsystem operation plan list; Step S3: Map the energy supply plan tasks in the subsystem operation planning list to the collaborative control period to obtain the collaborative mapping result. Based on the collaborative mapping result and the thermal inertia information and heat recovery efficiency information in the subsystem attributes, generate an energy supply reference information set. Step S4: Access the building energy supply resources and extract the operating status information of the multi-energy complementary subsystems in the building energy supply resources during different load cycles during the coordinated control period; Step S5: Based on the operating status information and energy supply reference information set, considering the thermal inertia information of each multi-energy complementary subsystem, the constraints of building energy supply resource construction, and the optimization objective of determining the building energy response intensity, optimize and solve the energy collaborative control strategy. Step S6: Extract the planned energy supply path of each multi-energy complementary subsystem in the energy collaborative control strategy, and perform building energy collaboration during the collaborative control period.

2. The multi-energy-complementary based near-zero energy building collaborative control method according to claim 1, wherein, Step S1 includes: Obtain the multi-energy complementary energy chain of the target near-zero energy building in which each subsystem serving as an energy supply node participates; wherein, each multi-energy complementary subsystem in the multi-energy complementary energy chain is integrated within the target near-zero energy building; Each extracted multi-energy complementary energy chain is converted into a corresponding multi-energy complementary subsystem operation task. Based on the energy supply identifier of each multi-energy complementary subsystem operation task and the preset collaborative control period, a multi-energy complementary operation task set for the target near-zero energy building is constructed.

3. The multi-energy-complementary based near zero-energy building cooperative control method according to claim 1, wherein, Step S2 includes: Based on the energy supply identifier, query the operation plan text of each multi-energy complementary subsystem operation task in the building energy management system; wherein, the operation plan text includes several information paragraphs describing different planning information categories during the operation of multi-energy complementary systems; Using a natural language processing tool trained with domain knowledge, information summaries are extracted from each information segment. Based on the keywords recorded in the information summaries, subsystem attributes and subsystem operation plans of the multi-energy complementary subsystem are analyzed and generated.

4. The multi-energy-complementary based near zero-energy building cooperative control method according to claim 1, wherein, Based on the keywords recorded in the information digest, analyze and generate a list of subsystem attributes and subsystem operation plans for the multi-energy complementary subsystem, including: Natural language processing tools are used to perform word segmentation, dependency parsing, and keyword association extraction on each information summary to obtain several keywords recorded in the information summary; Based on the dependency relationship of each keyword and the semantic correlation with each preset keyword in the preset keyword set, the attribute information and operation planning information of each multi-energy complementary subsystem are analyzed and generated; wherein, the attribute information includes thermal inertia information, heat recovery efficiency information, and identifiers of upstream and downstream energy supply nodes, and the operation planning information includes the energy output period of each multi-energy complementary subsystem and the energy output intensity and load demand intensity of each energy output period. Based on the attribute information, construct the subsystem attributes of the multi-energy complementary subsystem; based on the operation planning information, construct the subsystem operation planning list of the multi-energy complementary subsystem.

5. The near-zero energy building collaborative control method based on multi-energy complementarity according to claim 1, characterized in that, Step S3 includes: The energy supply plan tasks for different time periods in the subsystem operation plan list are converted into energy output intensity and load demand intensity for each load cycle, and the energy output intensity and load demand intensity for each load cycle are mapped to the collaborative control period to obtain the energy output intensity and load demand intensity for different load cycles in the collaborative control period. Based on the energy output intensity and load demand intensity of each multi-energy complementary subsystem under different load cycles, as well as the thermal inertia information and heat recovery efficiency information in the subsystem attributes of the multi-energy complementary subsystem, a spatiotemporal adjustment process of merging the quantized values ​​of heat transfer delay and heat loss suppression is used to generate an energy supply reference information set for each multi-energy complementary subsystem during the coordinated control period. The thermal inertia information includes the quantized values ​​of heat transfer delay for the building envelope and the quantized values ​​of heat loss suppression for the building's airtightness.

6. The multi-energy-complementary based near zero-energy building cooperative control method according to claim 4, wherein, Step S4 includes: Access building energy supply resources and extract the standby time period of the multi-energy complementary subsystem and the maximum supply intensity for different building load types from the building energy supply resources; Based on the standby time period and the coordinated control time period of each multi-energy complementary subsystem, the operating status information of different load cycles in the coordinated control time period of the building energy supply resources is extracted by the boundary adjustment process of overlapping and matching standby time period and load cycle. The operational status information includes the supply status of each multi-energy complementary subsystem and the maximum supply intensity for different building load types.

7. The multi-energy-complementary based near zero-energy building cooperative control method according to claim 1, wherein, Step S5 includes: Based on the supply status of the multi-energy complementary subsystem, the single supply intensity for different building load types, and the attribute information and operation planning information of each multi-energy complementary subsystem, each load cycle of each multi-energy complementary subsystem in the coordinated control period is allocated to the corresponding supply task period from the corresponding energy supply node to the next energy supply node or the subsystem scheduling period between two adjacent supply task periods. Based on the first constraint constructed from the quantified values ​​of heat transfer delay and heat loss suppression, the second constraint constructed from the supply path of building energy supply resources, the third constraint constructed from the supply intensity of building energy supply resources, and the optimization objective constructed from the response delay of building energy supply resources, the energy coordination control strategy is optimized and solved using an optimization algorithm. 8.The multi-energy complementary based near zero-energy building collaborative control method according to claim 7, wherein, Based on the first constraint condition constructed from the quantified values ​​of heat transfer delay and heat loss suppression, the second constraint condition constructed from the supply path of building energy supply resources, the third constraint condition constructed from the supply intensity of building energy supply resources, and the optimization objective constructed from the response delay of building energy supply resources, an optimization algorithm is used to optimize and solve the energy coordination control strategy, including: The first constraint condition is that the energy storage intensity of each multi-energy complementary subsystem in each load cycle, determined by the sum of the energy output intensity of each multi-energy complementary subsystem in each load cycle and the single supply intensity of the building energy supply resources allocated to each supply task period, is less than the heat recovery efficiency information of the multi-energy complementary subsystem; and the first constraint condition is that the load storage intensity of each multi-energy complementary subsystem in each load cycle, determined by the sum of the load demand intensity of each multi-energy complementary subsystem in each load cycle and the single supply intensity of the building energy supply resources allocated to the previous energy supply node of the multi-energy complementary subsystem in each supply task period, is less than the thermal inertia information of the multi-energy complementary subsystem. The second constraint is that the heat transfer path between the location information of the energy supply node where each building energy supply resource is located at the end of the previous supply task period and the location information of the energy supply node where it is located at the beginning of the next supply task period is less than the product of the duration of the subsystem scheduling period between the two supply task periods and the standard thermal response speed of the multi-energy complementary subsystem; and the heat transfer path between the location information of the energy supply node of each building energy supply resource in each supply task period and the location information of the next energy supply node is less than the product of the duration of the supply task period and the standard thermal response speed of the multi-energy complementary subsystem. The third constraint is that the single supply intensity of several building energy supply resources allocated to each supply task period is less than the maximum supply intensity of each building energy supply resource for the corresponding building load type; the optimization objective is to minimize the sum of the supply paths of building energy supply resources in each supply task period and the sum of the scheduling paths of each subsystem scheduling period within the coordinated control period. An optimization algorithm is used to optimize the energy coordination control strategy that allocates each load cycle of each multi-energy complementary subsystem to the corresponding supply task period from the corresponding energy supply node to the next energy supply node or the subsystem scheduling period between two adjacent supply task periods during the coordinated control period.

9. The near-zero energy building collaborative control method based on multi-energy complementarity according to claim 1, characterized in that, Step S6 includes: Extract the load cycle allocated from the corresponding energy supply node to the next energy supply node and the location information of the two energy supply nodes during the period when each multi-energy complementary subsystem performs each supply task in the energy coordinated control strategy, and generate the planned energy supply path for each multi-energy complementary subsystem through the path smoothing adjustment process. Based on the heat transfer path determined by the load cycle and location information of the energy supply node for each supply task period recorded in the planned energy supply path, supply guidance information for each multi-energy complementary subsystem during the coordinated control period is generated. The supply guidance information is sent to each multi-energy complementary subsystem, and the consistency of the supply path is verified through real-time feedback loops to execute building energy coordination actions during the coordinated control period.

10. A multi-energy complementary near-zero energy building collaborative control system for implementing the multi-energy complementary near-zero energy building collaborative control method according to any one of claims 1 to 9, characterized in that, include: Data acquisition module: acquires the multi-energy complementary operation task set of the target near-zero energy building; wherein, the multi-energy complementary operation task set includes coordinated control periods and energy supply identifiers; Data extraction module: Based on the energy supply identifier, query the operation plan text of each multi-energy complementary subsystem, and extract the subsystem attributes and the subsystem operation plan list; Reference generation module: Maps the energy supply plan tasks in the subsystem operation plan list to the collaborative control period, obtains the collaborative mapping result, and generates an energy supply reference information set based on the collaborative mapping result and the thermal inertia information and heat recovery efficiency information in the subsystem attributes. Access module: Accesses building energy supply resources and extracts the operating status information of multi-energy complementary subsystems in the building energy supply resources during the coordinated control period under different load cycles; Strategy Solving Module: Based on the operating status information and energy supply reference information set, considering the thermal inertia information of each multi-energy complementary subsystem, the constraints of building energy supply resource construction, and the optimization objective determined by the building energy response intensity, the module optimizes and solves the energy collaborative control strategy. The coordination module extracts the planned energy supply path of each multi-energy complementary subsystem in the energy coordination control strategy and executes building energy coordination during the coordination control period.

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