3051 results about "Building energy" patented technology

The invention provides an intelligent building energy conservation and emission reduction digital twin management system and method, and relates to the field of building automation and energy conservation control. The intelligent building energy conservation and emission reduction digital twinborn management system comprises a digital twinborn model construction module, an energy consumption prediction and regulation module, a multi-device cooperative control module, a real-time feedback and self-learning module and an edge-cloud cooperative architecture. By integrating a building information model, real-time sensor data, meteorological data and the like, a three-dimensional visual dynamic twinborn model is constructed, historical data playback and thermodynamic diagram analysis are supported, omnibearing virtual mapping of a physical building is realized, a physical system and virtual model data are synchronized in real time through an Internet of Things sensor network, and the real-time real-time dynamic twinborn model is established. The consistency of a virtual model and an actual system is ensured, the precision and reliability of the model are remarkably improved, artificial illumination is dynamically compensated according to natural illumination, lamps are automatically turned off in an unmanned area through infrared induction, and equipment-level energy saving is achieved in combination with air conditioning system optimization.

The invention discloses a building energy consumption dynamic optimization method and system based on BIM and reinforcement learning, and belongs to the technical field of building energy management and intelligent control, and the method comprises the steps: building a BIM containing building component physical attribute parameters, and generating a building digital twinborn body with dynamic thermal attribute evolution; extracting the spatial topological relation and the physical property parameters of the components, and constructing a multi-dimensional state space of a preset reinforcement learning model; embedding physical constraint conditions, and training the reinforcement learning model to generate a multi-objective optimization strategy of the energy equipment; and analyzing the multi-objective optimization strategy into an equipment control instruction set, and feeding back the equipment control instruction set to the building digital twin for real-time physical attribute simulation. According to the method, the physical accuracy of the BIM and the self-adaptive decision-making ability of reinforcement learning are combined, adversarial training under physical constraints is introduced, an energy consumption optimization strategy which conforms to actual operation limitation and dynamically adapts to environmental changes can be generated, and the energy utilization efficiency and the system response speed are remarkably improved.

The invention discloses a BIM (Building Information Modeling)-based building energy consumption prediction and dynamic energy-saving optimization method, which comprises the following steps of: extracting key characteristic parameters of geometry, materials and construction of a building from a BIM model, and obtaining multi-dimensional data of building types, structural characteristics, material attributes and environmental factors; cleaning the acquired multi-dimensional data, removing repeated, wrong or abnormal data points, and performing standardization processing through a minimum-maximum scaling method or performing normalization processing through a z-score standardization method; the adjusted AI model is used for building energy consumption prediction, cost models of material, labor force, maintenance and energy cost factors are combined, and a dynamic energy-saving strategy is generated through a genetic algorithm; the generated dynamic energy-saving strategy is converted into a specific system adjusting instruction to be applied to an actual building, and energy-saving control is achieved by adjusting the air volume of a building ventilation system, the brightness of an illumination system and the temperature set value of a heating ventilation air-conditioning system.

The invention relates to the technical field of intelligent building energy-saving control, and discloses a green building equipment intelligent energy-saving control method and system based on artificial intelligence, and the method comprises the steps: collecting building environment data, and carrying out the preprocessing; and simulating the energy consumption of the building equipment by using the digital twin model, and training the baseline neural network model to obtain an initial control strategy. And adopting a genetic algorithm to optimize hyper-parameters of the baseline model, and generating a plurality of candidate energy-saving control strategies. And inputting the optimized strategy into the deep reinforcement learning model, carrying out strategy iteration, calculating the energy-saving efficiency and comfort score after each iteration, screening the optimal strategy through a reward mechanism, and improving the accuracy of energy consumption prediction of the building equipment. The hyper-parameter of the neural network is optimized by using the genetic algorithm, and the intelligent degree of the energy-saving control strategy is improved. On the basis of deep reinforcement learning, the intelligent agent can adaptively adjust an energy-saving strategy under different environmental conditions, and the user comfort is guaranteed while the optimal building energy consumption is ensured.

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

The invention discloses a BIM (Building Information Modeling)-based building energy efficiency optimization system and method, particularly relates to the field of building energy efficiency management, and is used for solving the problems of dynamic optimization and collaborative maintenance of building equipment aging and energy efficiency reduction. Initial attenuation data, historical operation data and environmental influence parameters of the equipment are embedded into the data, and dynamic adjustment and real-time tracking of the aging state of the equipment are realized in combination with a sensor network and edge calculation. The maintenance period of the equipment is predicted based on a random forest regression algorithm, a collaborative maintenance plan is generated in combination with physical position information in the BIM, and load optimization and time sequence coordination during multi-equipment maintenance are ensured. By calculating the dynamic adaptability nonlinear response coefficient and the performance regeneration residual nonlinear deviation coefficient, the load adjustment opportunity is accurately judged, the load distribution between the equipment is optimized, the overall energy efficiency of the building is effectively improved, and the full-life-cycle management and energy efficiency optimization of the building equipment are realized.

The invention discloses a building energy-saving control method and system based on energy consumption parameters, and relates to the field of building energy saving.The building energy-saving control method based on the energy consumption parameters comprises the following steps that S1, parameters are obtained, and a building energy consumption database is constructed; s2, extracting a historical total energy consumption parameter and a historical block energy consumption parameter of building energy consumption; s3, constructing an energy consumption prediction model, and performing training optimization on the energy consumption prediction model; s4, predicting a future total energy consumption trend and a future block energy consumption trend; s5, presetting an energy consumption management and control database and an energy consumption matching rule, and matching an energy consumption management and control strategy in the energy consumption management and control database; and S6, implementing the energy consumption management and control strategy, and updating the building energy consumption database. According to the method, the historical energy consumption data of the building is utilized to accurately predict the future energy consumption trend, the total energy consumption is predicted in advance and refined to the energy consumption of each specific area, and data support is provided for accurate control.

The invention relates to the technical field of building energy saving, in particular to a building energy-saving management system and method based on the Internet of Things. The method comprises the following steps: acquiring a target architectural design drawing; building structure feature point recognition is carried out on the target building design drawing, three-dimensional space point cloud modeling is carried out, and a building three-dimensional structure model is constructed; building environment illumination parameters and personnel flow monitoring parameters are collected based on multiple sensor nodes; environment light intensity space distribution analysis is carried out on the building environment illumination parameters, then dynamic light intensity distribution rendering is carried out, and an environment light intensity distribution rendering model is constructed; and according to the building three-dimensional structure model, carrying out regional use scene analysis on the environment light intensity distribution rendering model, and carrying out optimal time sequence illumination demand fitting so as to construct an optimal illumination demand time sequence curve of each region. The energy consumption utilization rate of the building is increased, and accurate equipment regulation and control are achieved.

The invention discloses an intelligent linkage control management system based on air conditioner application, and relates to the technical field of intelligent control and energy management, and the system comprises a load prediction module which obtains an air conditioner load demand prediction value through an intelligent load prediction model based on preprocessed environment energy consumption data; the optimization scheduling module is used for optimizing the current air conditioner operation mode through the building energy management system based on the air conditioner load demand prediction value, and obtaining an optimized air conditioner operation scheduling instruction; the intelligent adjusting module is used for intelligently adjusting the operation parameters of the air conditioner system based on the optimized air conditioner operation scheduling instruction, adaptively adjusting the energy efficiency of the air conditioner and obtaining operation feedback data; and the fault management module is used for identifying potential fault risks of the air conditioner and formulating fault risk prevention measures through a time sequence analysis method based on the operation feedback data. According to the invention, through innovation of an intelligent load prediction and fault prevention mechanism, the energy utilization efficiency and the system stability are effectively improved.

The invention relates to an intelligent building energy-saving control method and system self-adaptive to environment changes, and belongs to the technical field of intelligent buildings. The energy-saving control method comprises the following steps: acquiring data of sensors distributed inside and outside a building and external weather forecast data; performing space-time alignment processing on the sensor data, generating a time-synchronized multi-dimensional environment data stream, performing anomaly detection processing, and outputting a pre-processed structured environment data set; performing environmental parameter prediction according to the structured environmental data set and the external weather forecast data to obtain an environmental parameter prediction result in a future preset time period, and constructing a multi-objective optimization function; acquiring a real-time power grid carbon emission coefficient and equipment operation state parameters, and generating a dynamic constraint condition set; and solving the multi-objective optimization function based on a hierarchical optimization algorithm, taking the dynamic constraint condition set as an input parameter, and outputting an equipment control instruction and an optimization control decision of an execution priority. The energy consumption can be reduced while the comfort degree is ensured.

The invention discloses an intelligent building energy-saving management method and system based on a digital twinning technology, and relates to the field of digital twinning technologies, and the method comprises the steps: building a three-dimensional building model based on a building information model and three-dimensional laser scanning point cloud data, and generating a twinning database through multi-source equipment data fusion; performing energy consumption simulation modeling according to the lamp dimming parameters, the air conditioner performance curve and the elevator operation log in the twin database to generate an energy consumption reference report; acquiring personnel density and environment parameters, and generating a building environment state matrix mapped with the twin database after space-time calibration; and establishing a hierarchical threshold value control strategy library based on the building energy consumption reference report and the building environment state matrix, and generating an optimization control strategy set through the equipment linkage relation matrix. According to the method, by embedding a Pareto optimal solution screening mechanism and equipment mutual exclusion rule verification, the daily average energy consumption is reduced on the premise that the comfort reaching rate of the optimal control parameter packet is ensured.

The invention provides a building energy-saving control system based on centralized control of the Internet of Things, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting personnel activity data, environment parameter data and equipment state data in a building in real time; the data preprocessing module is used for carrying out cleaning, denoising and time sequence alignment processing; the area use mode recognition module is used for analyzing the use conditions of equipment in different areas in the building according to a multi-layer classification rule; the energy-saving control strategy generation module is used for analyzing equipment in the building and generating a region-level energy-saving control instruction set; the execution control module is used for controlling an air conditioning system, a lighting system and an elevator system in the building in real time; the feedback regulation module is used for monitoring real-time feedback data of the equipment operation parameter set and feeding back to the energy-saving control strategy generation module in combination with environmental parameter data change; according to the invention, autonomy and accuracy of building energy-saving control are improved.

The invention discloses an intelligent building energy-saving optimization platform and method based on carbon footprint tracking, and relates to the technical field of building energy saving and carbon emission management. The method is used for solving the problems of extensive carbon emission evaluation, rigid quota distribution and insufficient energy-carbon collaboration. A three-dimensional carbon density map is constructed by collecting people flow, equipment energy consumption and environment data in real time, and carbon emission hotspots are dynamically identified. And analyzing the association between the power grid and the renewable energy source through a carbon flow tracking model, and correcting a weight output contribution matrix. The characteristics of equipment energy efficiency, building material hidden carbon emission and the like are fused to construct a carbon emission gene entropy, a quota migration strategy is generated in combination with a game algorithm, and oriented transfer from high carbon to low carbon buildings is promoted. A double-ring collaborative framework is constructed, an inner ring chaos search optimization device starts and stops to suppress carbon density fluctuation, an outer ring carbon price mapping adjusts energy storage scheduling, accurate carbon emission tracing, quota dynamic allocation and energy-carbon deep collaboration are achieved, building low-carbon transformation is supported, and the building cluster carbon emission reduction efficiency is improved.

The invention discloses an efficient cross-season energy storage energy pile, and relates to the technical field of new energy and energy conservation. The problems that in an existing energy storage system, the thermal load prediction error is large, underground thermal diffusion attenuation is caused, the heat exchange capacity is lowered, and the geological adaptability is insufficient are solved. According to the scheme, an LA mixed time sequence prediction model is adopted to optimize load prediction, a distributed optical fiber temperature measurement array and a finite element inversion algorithm are combined to accurately reconstruct a stratum temperature field, and the geological type is identified based on a support vector machine classifier to realize self-adaptive regulation and control of heat exchange parameters; meanwhile, the heat pump power, the circulating pump frequency and the heat charging and discharging rate of the phase change material are optimized through a depth deterministic strategy gradient algorithm and a gradient heat release strategy; the heat exchange efficiency, the long-term stability and the complex environment adaptability of the cross-season energy storage system are remarkably improved, and the energy-saving effect of a building energy supply system is improved.

The invention relates to the technical field of digital twinning modeling, in particular to a digital twinning-based building energy equipment energy consumption digital construction method and system, and the method comprises the following steps: obtaining the operation state data of building energy equipment according to a digital twinning model, calculating the energy consumption increment in unit time and the energy consumption change rate of a time window, and calculating the energy consumption change rate of the building energy equipment; and obtaining equipment energy consumption abrupt change time sequence data. According to the method, the energy consumption increment and the change rate in the time window are calculated, the energy consumption abrupt change time sequence data are captured, the abrupt change point of the high-load equipment is accurately identified, the energy consumption trend parameters are adjusted based on the accumulated energy consumption change rate, the regional energy consumption gradient change rate is analyzed, and the contribution of equipment increase and decrease and mode adjustment to the energy consumption is calculated. The weight adjustment of the equipment in the overall building energy consumption is optimized, so that the influence of local load on the overall energy consumption is more accurate, the error value and the growth rate are calculated, the energy consumption calculation mode is adjusted, and the stability of error control is improved.

The invention relates to the technical field of energy management, in particular to a building energy-saving analysis system based on big data. According to the method, through time sequence segmentation of the real-time energy consumption data and the historical load records and in combination with Z-score standardization processing, accurate description of the dynamic fluctuation range of the building energy consumption data is achieved, and interference of seasonal changes and equipment operation fluctuation on an analysis result is effectively avoided. According to the outlier detection algorithm constructed based on multi-dimensional features, the fine granularity level of anomaly recognition is improved through composite analysis of curve slope and power fluctuation. A dynamic threshold self-adaptive mechanism is combined with an operation mode and an equipment state, so that the sensitivity and response adaptation capability of energy consumption data monitoring are enhanced. A multi-dimensional parameter fusion method constructed by an association index and a credibility factor is adopted, and the credibility modeling and risk grade division of the energy consumption data are effectively realized by quantifying the association among the fluctuation amplitude, the duration and the data deviation degree.

The invention relates to the technical field of building energy consumption management, in particular to a building energy consumption management system and method for analyzing the load of a power grid, and the system comprises an acquisition processing unit, a load analysis unit and an energy management unit. A load prediction module of the load analysis unit predicts a load by using a long short-term memory network and a graph convolutional neural network, a decomposition traceability module decomposes a traceability load by using an attention mechanism and a hidden Markov model, and an energy management unit regulates and controls adjustable equipment in peak and valley periods according to load prediction. According to the method and the system, the abnormal energy consumption equipment is pre-warned according to the load decomposition and traceability result, multiple technical means are integrated, the traceability load can be accurately predicted and decomposed, the energy management is optimized, the complex building environment is adapted, and the problems that the equipment relationship is difficult to grasp, the load change reason is difficult to track and the energy consumption is difficult to accurately manage in a traditional mode are effectively solved.

The invention relates to the field of distributed energy, in particular to a distributed energy intelligent allocation method based on Internet of Things big data in a smart city, which comprises three steps of data acquisition, processing and output. Firstly, photovoltaic, energy storage and charging pile real-time data are obtained through a multi-protocol analysis engine, and meteorological cloud charts, traffic thermodynamic diagrams and building energy consumption historical data are combined. And secondly, constructing a dynamic weight model by utilizing a space-time joint attention mechanism, generating a global wind and light output prediction network through federated learning, constructing an elastic scheduling strategy library by adopting a multi-target dynamic game algorithm, optimizing a scheduling model on a digital twin platform, and finally generating a real-time scheduling scheme through a two-stage increment optimization architecture. The method effectively improves the efficiency, economical efficiency, reliability and environmental protection property of an energy system, provides a powerful tool for urban managers, guarantees stable, economical and clean energy supply for residents, and promotes sustainable development of smart cities.

The invention discloses light blue toughened glass, which comprises a toughened glass substrate and a composite metal film layer laminated on the substrate, and is toughened coated glass with three metal silver film layers. After a common glass substrate is plated with a composite metal film layer, toughening treatment is carried out, and after toughening treatment, the metal film layers reflect, interfere and refract light, so that the glass is light blue in the sun, is low-radiation coated toughened glass, and has good decoration and energy-saving effects; the visible light transmittance is high, the outdoor visible light reflectivity is low, the solar energy transmittance is low, and the solar energy reflectivity is high; the prepared hollow glass has better energy-saving effects of light control, heat insulation, heat preservation and the like, so that a building is closer to the nature, and the hollow glass is used for near-zero-energy-consumption buildings, so that the building meets the requirements of the national standard GB55015-2021 General Standard for Building Energy conservation and Renewable Energy Utilization.

The invention discloses low-radiation coated tempered glass, which comprises a tempered glass substrate and a composite metal film layer laminated on the substrate, and is tempered coated glass with three metal silver film layers. After a common glass substrate is plated with a composite metal film layer, toughening treatment is carried out, and after toughening treatment, the metal film layers reflect, interfere and refract light, so that the glass is light green (French green) in the sun, and the decoration and energy-saving effects are good; the visible light transmittance is high, the outdoor visible light reflectivity is low, the solar energy transmittance is low, and the solar energy reflectivity is high; the prepared hollow glass has better energy-saving effects of light control, heat insulation, heat preservation and the like, so that a building is closer to the nature, and the hollow glass is used for near-zero-energy-consumption buildings, so that the building meets the requirements of the national standard GB55015-2021 General Standard for Building Energy conservation and Renewable Energy Utilization.

The invention discloses coated toughened glass, which comprises a toughened glass substrate and a composite metal film layer laminated on the substrate, and is coated toughened glass with three metal silver film layers. According to the invention, after a common glass substrate is plated with the composite metal film layers, toughening treatment is carried out, and after the toughening treatment, the metal film layers reflect, interfere and refract light, so that the glass is light gray in the sun, and the decoration and energy-saving effects are good; the visible light transmittance is high, the outdoor visible light reflectivity is low, the solar energy transmittance is low, and the solar energy reflectivity is high; the prepared hollow glass has better energy-saving effects of light control, heat insulation, heat preservation and the like, so that a building is closer to the nature, and the hollow glass is used for near-zero-energy-consumption buildings, so that the building meets the requirements of the national standard GB55015-2021 General Standard for Building Energy conservation and Renewable Energy Utilization.

The invention relates to the technical field of energy consumption simulation, in particular to a simulation method and system based on BIM building energy consumption, and the method comprises the following steps: importing structural data in a BIM building model, recording the actual sizes and material characteristic information of building walls, windows and floors, and generating a building structure parameter table through parameter extraction and data integration. According to the method, the BIM technology and the energy consumption simulation process are utilized, the accuracy of building energy consumption prediction and the practicability of operation are remarkably improved, the building structure parameter table is constructed, the thermal bridge effect is deeply analyzed, so that thermal performance data are more accurate, dynamic adjustment of actual climate change parameters is provided in thermal load simulation, the simulation adaptability is improved, and the simulation efficiency is improved. According to the method, the consistency of an energy consumption prediction result and an actual climate condition is ensured, the simulation precision and practicability are ensured through continuous matching and iterative optimization of actual data and a simulation result, powerful data support is provided for energy efficiency management and optimization, an energy-saving target is helped to be achieved, and the environmental performance of a building is optimized.

The invention discloses a preparation method of low-radiation coated toughened glass, which comprises the following steps: sintering a target material, carrying out moisture removal and degassing pretreatment on glass, carrying out coating treatment and toughening treatment, and laminating a composite metal film layer on a toughened glass substrate to prepare the toughened coated glass with three metal silver film layers. According to the method disclosed by the invention, after a common glass substrate is plated with the composite metal film layers, toughening treatment is carried out, and after the toughening treatment, all the metal film layers reflect, interfere and refract light, so that the glass disclosed by the invention is light green (French green) in the sun, and the decoration and energy-saving effects are good; the visible light transmittance is high, the outdoor visible light reflectivity is low, the solar energy transmittance is low, and the solar energy reflectivity is high; the prepared hollow glass has better energy-saving effects such as light control, heat insulation and heat preservation, enables buildings to be closer to the nature, is used for near-zero energy consumption buildings, and enables the buildings to meet the requirements of the national standard General Standard for Building Energy conservation and Renewable Energy Utilization.

The invention discloses a near-zero energy consumption building energy supply method and system based on multi-objective optimization, and relates to the technical field of building energy efficiency and renewable energy application, and the method comprises the steps: collecting energy consumption data in a building, and constructing a multi-objective evaluation system according to the energy consumption data; performing weight optimization on the multi-target evaluation system to obtain an optimal weight coefficient; combining the optimal weight coefficient with a solar photovoltaic system and a ground source heat pump system, and establishing a renewable energy supply strategy model; and outputting an energy supply scheduling instruction of the building in each time period based on the renewable energy energy supply strategy model, and adjusting operation parameters of a solar photovoltaic system and a ground source heat pump system. According to the invention, by adjusting the operation mode of the equipment in real time, the balance between the energy efficiency and the comfort level is ensured, the common problem of energy efficiency waste or insufficient comfort level in the traditional method is avoided, and by means of intelligent scheduling and optimization, the energy consumption of the building can be effectively reduced, and the sustainability of energy utilization of the building is improved.

The invention discloses a preparation method of light gray tempered low-emissivity coated glass. The preparation method comprises the following steps: sintering a target material, carrying out moisture removal and degassing pretreatment on glass, carrying out coating treatment and tempering treatment, and laminating a composite metal film layer on a tempered glass substrate to prepare the tempered coated glass with three metal silver film layers. According to the method, after the common glass substrate is plated with the composite metal film layers, toughening treatment is carried out, and after toughening treatment, the metal film layers reflect, interfere and refract light, so that the glass is light gray in the sun, and the decoration and energy-saving effects are good; the visible light transmittance is high, the outdoor visible light reflectivity is low, the solar energy transmittance is low, and the solar energy reflectivity is high; the prepared hollow glass has better energy-saving effects such as light control, heat insulation and heat preservation, enables buildings to be closer to the nature, is used for near-zero energy consumption buildings, and enables the buildings to meet the requirements of the national standard General Standard for Building Energy conservation and Renewable Energy Utilization.

The invention relates to the technical field of intelligent control, and particularly discloses a linkage control method for energy-saving ventilation equipment, which comprises the following steps of: acquiring indoor and outdoor temperature, indoor carbon dioxide concentration and personnel occupancy state data in real time; according to the method, deep collaborative analysis is carried out on the indoor personnel occupancy state and the deviation between the carbon dioxide concentration and a target set value, so that joint characterization of the current indoor fresh air volume regulation and control requirement is achieved, control parameters of a fresh air volume PI D controller are dynamically optimized accordingly, and a basic fresh air volume regulation instruction is calculated. On the basis, thermal disturbance introduced by fresh air is further quantified based on the outdoor and indoor temperature difference, and the thermal disturbance serves as a feedforward signal to be compensated to an air conditioner load adjusting loop, so that dynamic adjustment of the air conditioner load is achieved. In this way, cooperative control over ventilation and air conditioning systems can be achieved, the fresh air-air conditioning equipment energy consumption coupling conflict in traditional control is restrained, and therefore the building energy consumption is reduced while the indoor environment quality is guaranteed.

The invention provides an intelligent zero-carbon-emission park building energy-saving control method and system and a medium, which are used for optimal control of window light transmittance, illumination intensity and air conditioner operation power of a building. According to the system, future environment parameters (such as temperature and illumination intensity) are predicted through an LSTM model, and current environment data and historical control signals are combined and input into an MLP model to generate a real-time control strategy, so that the running states of windows, illumination and air conditioners are dynamically adjusted. The system can significantly improve the energy efficiency of a building, reduce carbon emission and optimize the indoor environment comfort, and has adaptive learning and automatic fault detection functions. The system is suitable for the fields of green buildings, intelligent buildings and the like, intelligent and automatic environment control can be achieved, and energy conservation, emission reduction and sustainable development of the buildings are promoted.

The invention relates to the technical field of intelligent building energy management, and particularly discloses an air conditioner load cluster optimization regulation and control method and system based on building thermal inertia modeling, and the method comprises the steps: collecting building structure parameters, building material thermophysical parameters, indoor and outdoor temperature and humidity historical data, air conditioner operation data and real-time electricity price data of a building; building a building thermal inertia model based on the collected data; inputting the collected air conditioner operation data, indoor and outdoor temperature and humidity historical data into a building thermal inertia model, and predicting air conditioner loads under different working conditions; constructing an optimized objective function, and solving the optimized objective function by adopting a genetic algorithm to obtain an optimal air conditioner load regulation and control strategy; and according to the optimal air conditioner load regulation and control strategy, the air conditioner load cluster is regulated and controlled in real time. The energy utilization efficiency can be effectively improved, the operation cost is reduced, and intelligent and refined regulation and control of the air conditioner load cluster are achieved.

According to the intelligent energy-saving analysis method based on the building information model (BIM), building thermal performance parameters and an intelligent simulation algorithm are integrated, building heat conduction, a heat bridge effect and solar heat gain are accurately simulated, and a scientific basis is provided for design. Meanwhile, an energy-saving optimization strategy is automatically generated, such as wall thickness adjustment and window design, so that energy consumption is reduced. Besides, a multidisciplinary cooperative working platform is established, effective implementation of energy-saving measures is ensured, and energy use and maintenance tracking of the whole life cycle of the building are realized and energy-saving performance is continuously optimized in combination with an energy management system. The innovation of the method lies in the automatic and intelligent analysis process, and a comprehensive solution is provided for the field of building energy conservation.

The invention provides a network following type and network constructing type energy storage combined planning method and device, and belongs to the field of power system planning. The method comprises the following steps: in a to-be-planned grid following type energy storage and grid construction type energy storage power system, carrying out joint modeling on a grid following type converter, a grid construction type converter and a fed alternating current power grid, so as to obtain a closed loop transfer function of the power system; based on the closed-loop transfer function, constructing a small-interference stability security domain oriented to energy storage joint planning; and converting the small-interference stability security domain into a small-interference stability security constraint, and further establishing a multi-type energy storage joint planning model to obtain a joint planning scheme of following network type energy storage and constructing network type energy storage. According to the method, the influence of the operation condition of the converter on small interference stability can be considered, power system optimization targets such as new energy consumption and the like are considered, and combined optimization configuration of network following type and network construction type energy storage is realized.