119 results about "Energy load" patented technology

A multi-energy load forecasting method, a multi-energy load forecasting system, an electronic device, a program, and a storage medium are provided that realize accurate long-term forecasting of multi-energy loads in a target integrated energy system under conditions where no historical load data is available. [Solution] A multi-energy load forecasting method for an integrated energy system without historical data involves obtaining the meteorological characteristics of a target complex and the cooling, heating, electricity, and gas historical data of a source domain group complex, preprocessing the obtained data, performing cross-correlation and generalization ability analysis of the complex on the preprocessed cooling, heating, electricity, and gas historical data of the source domain group complex, determining appropriate source domain data, constructing a multi-energy load forecasting model, training the model based on the source domain data according to the Metas training policy, obtaining a trained forecasting model, and inputting the preprocessed meteorological characteristics of the target complex into the forecasting model to obtain a forecast result.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention relates to a multi-energy-load scene generation method and device based on a diffusion model. Multi-energy load data and condition information are input into a time-space and cross-dimension decomposition reconstruction network of a classifier-free condition diffusion architecture; projecting the multi-energy load data into time sequence embedding and cross-dimension feature embedding; coding the condition signal and the diffusion step number together to obtain a condition embedding vector; the reconstruction network comprises a time channel and a feature channel, a global context relationship is captured by an encoder, and condition information is fused based on a condition embedding vector through a decoder; wherein an STAR module is arranged behind the decoder of the feature channel; and integrating the output of the time channel and the output of the feature channel, and then carrying out layer-by-layer denoising to generate a multi-energy-load scene. Compared with the prior art, the method has the advantages that autocorrelation, cross correlation and dislocation correlation among cold, hot and electric loads of the building group and renewable energy output are considered, and high-fidelity scene generation under condition control is realized.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention provides a comprehensive energy system multi-element load prediction method based on double-layer decomposition and reconstruction and TECNFormer, and the method comprises the steps: obtaining standardized input data, meteorological factors and time characteristics, which are obtained through the preprocessing of a historical multi-energy load sequence of a comprehensive energy system, wherein the historical multi-energy load sequence comprises an electric load sequence, a cold load sequence and a heat load sequence; performing double-layer modal decomposition on the standardized input data, and classifying the standardized input data into different types of dynamic components; respectively inputting the dynamic components into a time sequence enhanced convolution module, and extracting multi-scale local features by combining multiple heterogeneous differential convolution operators with causal convolution; taking a TECNFormer composed of a time sequence enhanced convolution module and an improved long sequence prediction network as a unified shared feature extraction layer, combining the improved long sequence prediction network with a bidirectional long-short-term memory network and a sparse attention mechanism to capture long-range dependence and local details, and obtaining joint modeling features; on the basis of a hard shared network architecture, a multi-task branch is arranged at an output end, and electric, cold and heat load prediction results are synchronously output.

The invention provides an energy distribution optimization method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring energy purchase cost data and energy supply attribute data of an energy supplier in a target scheduling period and energy demand attribute data and energy constraint attribute data associated with each energy load area; the energy purchase cost data, the energy supply attribute data, the energy demand attribute data and the energy constraint attribute data are processed based on an energy distribution prediction model, energy distribution information is obtained, and the energy distribution prediction model comprises a first-stage distribution sub-model constructed based on a multi-objective optimization function and a constraint condition set; and determining a target energy distribution scheme corresponding to the energy load area according to the energy distribution information. Cooperative optimization of key targets of cost, reliability and timeliness is realized in a target scheduling period, and the overall robustness and feasibility of an energy distribution scheme in a complex and uncertain environment are remarkably improved.

The invention discloses a comprehensive energy load short-term prediction method, system and device and a medium, and the method comprises the steps: screening meteorological factors which affect a load through employing a Pearson correlation coefficient, and determining a meteorological sensitive factor; calculating the similarity between the historical dates and the to-be-predicted dates through a grey correlation analysis algorithm, selecting a plurality of historical dates with the highest similarity, and constructing a similar day training set; carrying out adaptive decomposition on the load sequence by adopting an improved variational mode decomposition algorithm to obtain an intrinsic mode function component set; optimizing core parameters of the support vector machine model through an improved sparrow search algorithm, and predicting each sub-component by using the optimized model to obtain a predicted value of each sub-component; and adding and merging the sub-component prediction values of the same load type according to a time sequence to obtain a comprehensive energy load short-term prediction value. The method effectively improves the precision, stability and adaptability of short-term load prediction of the integrated energy system.

Disclosed is a method for constructing an optimal scheduling model of an integrated energy system considering virtual thermal storage, which relates to the technical field of electric power systems. The method includes: establishing an operation model of an apparatus, a demand response model of an energy load, a charging model of an electric vehicle, and an energy flow model of a heat supply network of the virtual thermal storage in the integrated energy system; processing, according to a second-order cone relaxation method, a direct current power flow model of a power distribution network, and constructing the optimal scheduling model of the integrated energy system by taking minimization of total cost of an energy operator as an objective, thereby coordinately and optimally scheduling a plurality of energy sources.

A carbon emission dynamic prediction method and system based on multi-energy load data, the method comprising: collecting and preprocessing the historical data of carbon emission, energy consumption and multi-energy load consumption of the park, calculating the joint correlation coefficient and screening the energy type; calculating the initial carbon emission-energy conversion matrix and energy-multi-energy load conversion matrix; constructing a carbon emission coupling objective function, solving and updating the corresponding conversion matrix by alternately fixing the carbon emission-energy and energy-multi-energy load conversion matrix; establishing a carbon emission coupling model, calculating the prediction carbon emission error, constructing a prediction error correction objective function, and correcting the conversion matrix; predicting the multi-energy load consumption in the M+1 month and correcting the error, combining the corrected conversion matrix in S4, and predicting the carbon emission in the M+1 month. The present application can dynamically adjust the conversion relationship between carbon emission and multi-energy load, significantly improve the accuracy of carbon emission prediction and the response ability to actual working condition changes.

The invention discloses a multi-energy collaborative predictive control method and system based on digital twinning, and relates to the technical field of industrial control. The method comprises the following steps: inputting each energy historical load of a plurality of historical periods and meteorological prediction data of a next period into a trained LSTM prediction model to carry out multi-energy load prediction, and outputting to obtain each energy load prediction value of the next period; constructing an optimization model, and solving the optimization model to obtain a preliminary scheduling plan; performing simulation deduction on the control instruction in the preliminary scheduling plan to obtain a pre-estimated system state in the next period; if the simulation deduction result is deviated from expectation, correcting the preliminary scheduling plan based on the state of a next-period pre-estimated system to obtain a final control instruction set; the problem of endogenous feedback circulation caused by the fact that prediction depends on a control execution result in existing multi-energy control is solved, sawtooth-shaped deviation between a predicted value and an actual value is avoided, and safe and efficient operation of a multi-energy system is achieved.

A system and method for seasonal energy consumption determination using verified energy loads with the aid of a digital computer are provided. A digital computer is operated, including: obtaining energy loads for a building measured over a seasonal time period; obtaining outdoor temperatures for the building as measured over the seasonal time period; verifying stability of the energy loads, including: evaluating the energy loads over time; and identifying at least one of one or more discontinuities and one or more irregularities in the energy loads based on the evaluation. Operating the computer further includes: determining a baseload energy consumption using at least some of those of the energy loads; calculating seasonal fuel consumption rates and balance point temperatures; and disaggregating seasonal fuel consumption based on the baseload energy consumption, seasonal fuel consumption rates, balance point temperatures, and at least some of the outdoor temperatures into component loads of consumption.

The invention discloses a load translation control method for a hybrid diesel locomotive considering a diesel engine and a gas engine. The method comprises the following steps: in a ground bench test, respectively simulating typical operation conditions of the diesel / gas engine, and determining respective safe power fluctuation intervals; in the running process of the hybrid diesel locomotive, the output power of the diesel / gas engine is dynamically corrected based on the power fluctuation threshold value of the running data; and scene-based multi-energy load translation control is carried out, the scene comprises the dynamic operation characteristics of acceleration and deceleration of the train, and the engine power of the hybrid diesel locomotive is adjusted according to the working mode of the power battery and the power fluctuation difference value, so that the load translation of the hybrid diesel locomotive is met based on the engine power. According to the method, multi-energy cooperative control considering load translation, scientific and controllable threshold value and working condition adaptation is achieved, the comprehensive traction force of the hybrid diesel locomotive is improved, and energy conservation and emission reduction are achieved.

The invention relates to the technical field of energy load monitoring, in particular to an energy consumption decomposition method based on Bayesian optimization and a hidden Markov model, and the method comprises the following steps: S1, carrying out the data loading of electric equipment and the construction of a feature library, carrying out the steady-state voltage and current signal collection of a plurality of pieces of electric equipment, recording the electrical parameters of the electric equipment in different operation states, calculating statistical characteristics of active power and reactive power for each electric device and the state of the electric device, describing power characteristics of each electric device in different states through the statistical characteristics, and estimating a state transition probability matrix of the electric device according to an operation sequence of the electric device; the state transition probability matrix describes a rule of transition of the electric equipment from one operation state to another state, and provides a basis for subsequent time sequence modeling. According to the method, discriminant learning and time sequence modeling are combined, high precision and interpretability are achieved, and the method is suitable for energy consumption identification and energy efficiency optimization in the building and industrial fields.

In a method for transferring a storage medium of power generation energy loaded by a power generation float to a collection base, a power generation float is connected to both sides of a deck part, a power generation float is moved to a collection base, a deck part straddles a long bank projecting on a water surface along a water channel through which a hull can pass along a quay wall of a collection base, a power generation float is anchored in a state where one of the hulls enters a water channel, a hull on at least one side of the deck part is separated from the deck part, and a storage medium capable of storing energy is connected on a side where the hull of the deck part is separated, and the float leaves with the hulls connected to both sides of the deck part.

The invention relates to the technical field of virtual power plant operation and optimization control, in particular to a virtual power plant-oriented multi-data-source fusion multi-energy load prediction method, which comprises the following steps of: constructing aggregated multi-source heterogeneous data of a virtual power plant; the data comprises historical multi-energy load data, calendar and market information and external environment auxiliary data in the jurisdiction of a virtual power plant, preprocessing the multi-source heterogeneous data, performing feature screening on the time sequence feature matrix based on correlation analysis, constructing a model input set, constructing a load prediction model, and predicting the load of the power plant. The model comprises an input layer, a plurality of stacked space-time fusion layers and an output layer which are connected in sequence; the space-time fusion layer comprises a multi-scale time convolution module and a graph convolution module which are parallel, and is used for model training and parameter optimization, load prediction and result output; and combined prediction of the electric load, the thermal load and the cold load is realized under a unified multi-scale space-time diagram neural network framework.

The invention discloses a micro-grid energy management system and method based on artificial intelligence, and belongs to the technical field of micro-grid energy management, and the system comprises a scene feature collection and quantification module which is used for obtaining a scene feature set of a micro-grid and carrying out the quantification processing; the algorithm self-adaptive matching module is used for automatically screening an adaptive artificial intelligence optimization algorithm based on the quantized scene feature set; the scheduling strategy generation module is used for generating an energy scheduling strategy through an adaptive artificial intelligence optimization algorithm on the basis of prediction data output by the photovoltaic power and energy load prediction analysis model in combination with real-time operation parameters of the microgrid; the zero-carbon data statistics and early warning module is used for calculating carbon emission reduction, renewable energy utilization rate and carbon emission intensity index in real time; and the full life cycle adaptation module is used for dynamically upgrading the scene feature set and the feature algorithm mapping rule base. According to the invention, the accuracy and robustness of the scheduling strategy can be improved.

The application discloses a kind of comprehensive flexible resource reliability optimization methods considering dynamic multi-energy demand response.Comprehensive flexible resource is divided into four kinds of multi-state comprehensive flexible resource, construct the multi-state model of comprehensive flexible resource considering time sequence characteristics, obtain the multi-energy load demand of each comprehensive flexible resource;Respectively, the multi-state model of comprehensive flexible resource reduction, transfer, alternative response considering time sequence characteristics is established, obtain the multi-energy load demand of comprehensive flexible resource after reduction, transfer, alternative response end in each state;Fusion three kinds of multi-state model, establish the multi-state model of total demand response, obtain the amount of demand side operation reserve provided in multiple states, and then calculate the reliability of the amount of demand side operation reserve provided by comprehensive flexible resource.The application adopts multi-state model to more accurately characterize the dynamic characteristics of comprehensive flexible resource, and accurately obtains the operation risk parameters of the amount of demand side operation reserve provided by comprehensive flexible resource.

The invention belongs to the technical field of power dispatching, and discloses a power grid economic dispatching strategy considering waste heat utilization of a high-energy-load enterprise, and the method comprises the following steps: a high-energy-load waste heat utilization method and modeling step, and a power grid economic dispatching model step considering waste heat utilization of the high-energy-load enterprise. Wherein the step of the power grid economic dispatching model considering waste heat utilization of the high-energy-load enterprise comprises a step of modeling a production mathematical model on the side of the high-energy-load enterprise and a step of modeling a day-ahead dispatching mathematical model on the side of the power grid. The waste heat utilization system has the following main beneficial technical effects that extremely high economic benefits and environmental benefits are brought by waste heat utilization; a robust day-ahead scheduling scheme is formulated through a power grid side, so that the system can still operate stably and safely under the conditions of new energy output and load power fluctuation; and in the carbon emission constraint of the high-energy-load enterprise, economic benefits and environmental benefits are pursued, the economical efficiency and the low-carbon property of the system are considered, and the decision conservative degree of a traditional optimization method is reduced.

This invention relates to the field of energy forecasting technology, and in particular to a method and system for short-term energy load forecasting based on an improved Transformer using SE-Block. The method includes reversible normalization preprocessing of acquired multivariate load sequence data; feature extraction and fusion of the preprocessed data using improved cross-scale interactive patching, including multi-scale feature extraction, cross-scale interactive alignment, residual correction, and dynamic fusion; and feature filtering of the fused features based on a channel attention mechanism, including feature response based on improved SE-Block and nonlinear interaction of context vectors. This invention addresses the non-stationarity of actual load caused by meteorological conditions and user behavior. By automatically eliminating noise interference among multiple variables, the model accurately depicts the fluctuation details of the load curve, demonstrating its robustness in multivariate load forecasting for integrated energy systems.

The application discloses a virtual power plant operation data intelligent optimization method and system based on the Internet of Things, relates to the technical field of Internet of Things energy monitoring, and acquires energy transmission data sets and energy load data sets after preprocessing and maps to each corresponding node of the virtual power plant through real-time collection of energy operation data of each distributed node layer by arranging power sensors on the distributed node layer of the virtual power plant; energy flow state evaluation is carried out through the construction of an energy flow level deviation index Neq; when there is an anomaly in the energy flow level, a spatial distance matrix Dl(x, i) of the virtual power plant topology is extracted and a comprehensive operation coordination index Eop is constructed to evaluate the residual error distribution state. When the energy flow of the virtual power plant cannot converge naturally, the energy flow Φadj is calculated and distributed to dynamically correct the energy flow of each node, and the energy storage equipment is controlled to switch between the charging and discharging modes. The method realizes dynamic coordination and abnormal self-recovery control of the virtual power plant operation data.

An electric distribution architecture is described herein. In some embodiments, the electric distribution architecture for an aircraft includes at least one electrical source, a plurality of loads including at least one high energy load and at least one low energy load, and a power distribution system. The power distribution system includes a plurality of contactors configured to selectively distribute electrical power from each of the at least one electrical source to each of the plurality of loads. In some approaches, at least one first fast acting high energy protection device is electrically coupled in series with each at least one electrical source, and at least one second fast acting high energy protection device is electrically coupled in series with each of the at least one high energy loads. In some approaches, the power distribution system is devoid of any high energy protection devices.

The application discloses a regional building carbon emission optimization system and method, aiming at solving the problem of multiple optimization targets of regional building balanced carbon emission, economic cost and user comfort. The system includes a perception layer, a transmission layer, a platform layer and an application layer. The perception layer collects multi-source data such as building energy consumption, environmental state and regional energy. The energy load prediction module of the platform layer predicts future load based on historical data. The multi-objective collaborative optimization module constructs a target function with the minimum regional total carbon emission, the lowest total energy cost and the highest average user comfort as the target. The non-dominated sorting genetic algorithm is used to solve the optimal regional energy scheduling strategy. Finally, the strategy is executed through the regional energy hub module. Through data-driven collaborative scheduling, the application scientifically balances the contradiction between emission reduction, economy and comfort, and significantly improves the comprehensive energy efficiency and low-carbon operation level of the regional building group.

The invention provides a wind turbine heat pump unit control method, after a wind turbine is started, the working condition of a unit is adjusted according to a plurality of parameter indexes, and the method comprises the following steps that the current outlet water temperature and the target water temperature are judged to obtain a first parameter index, and a first adjusting instruction is determined according to the first parameter index; judging the sizes of the target loading position and the current loading position so as to obtain a second parameter index, and determining a second adjustment instruction according to the second parameter index; determining a load adjusting instruction output to the unit according to the first adjusting instruction and the second adjusting instruction; after the unit is adjusted according to the load adjusting instruction, the water valve opening degree is adjusted according to the first parameter index. The control system adjusts the automatic adjustable load position according to the water outlet temperature target value, the current load of the compressor and the target load of the wind turbine, the wind energy fluctuation and the heat load requirement of a user are balanced, and energy storage dependence is reduced.

The invention relates to the technical field of energy, and provides an energy load prediction method based on a layered space-time fusion graph LSTM network, and the method comprises the steps: defining an energy load prediction task, and constructing the layered space-time fusion graph LSTM network; for an energy load prediction task, a physical topological graph and a semantic function graph are constructed according to original data or prior knowledge by analyzing historical energy load examples; dynamically aggregating spatial context information from the physical topological graph and the semantic function graph by using a gating mechanism to obtain fusion features; and performing spatio-temporal joint memory evolution on the fusion features by using a spatio-temporal alternating LSTM unit, and outputting an energy load prediction result. According to the method provided by the invention, the importance of different graph information can be dynamically learned on each node and time step, so that the dependency relationship of a microscopic level and a macroscopic level is modeled at the same time, and experimental results show that the method is stably superior to an existing advanced model on various indexes, and better prediction performance is obtained.

A computer-implemented system for dispatching devices in a computer data center, has a plurality of energy-using devices, a plurality of energy-providing devices, and a dispatcher programmed to (a) identify future energy loads for the computer data center and available energy-using and energy-generating devices using data received from sources internal to the computer data center and sources external to the computer data center, (b) select particular ones of the available energy-using and energy-generating devices to serve the identified future energy loads, and (c) generate control signals to cause the selected particular ones of the available energy-using and energy-generating devices to serve the identified future energy loads.

The invention relates to the technical field of energy scheduling, in particular to a park comprehensive energy optimal scheduling system based on multi-energy flow collaboration, and the system comprises a park comprehensive energy scheduling data preparation module which obtains a photovoltaic output reference value of a park and obtains an output coefficient; predicting the future output coefficient to obtain a final photovoltaic predicted output; the park energy load judgment module is used for analyzing the power consumption time period of each load device in the park in the statistical period and determining the load time fluctuation degree of each load device; natural gas supply adequacy is determined; obtaining a substitution elastic coefficient of the load equipment, and determining a load type of the load equipment; and the park comprehensive energy scheduling module is used for constructing an optimal scheduling model taking the minimum total cost as a target, obtaining constraint conditions of the optimal scheduling model, and solving to obtain an optimal energy scheduling scheme. The invention aims to reduce the comprehensive energy consumption cost of the park through multi-energy flow collaborative optimization.

Embodiments of the present application disclose a radiation source positioning method, a computer device and a computer storage medium. A general radiation source positioning technology based on an efficient neural network is proposed. For a light-weight electromagnetic spectrum sensing terminal, the energy load is limited, the computing power is limited, the computing chip carried by the terminal has limited operators, and the real-time requirement of the positioning algorithm. Therefore, research on a general target positioning technology based on an efficient neural network is needed. In the embodiments, network parameters are optimally selected to realize a sensing field covering a full image, so that the computing cost is reduced, the output delay is reduced, the computing efficiency of the neural network model is improved, and a higher degree of general operator is used, so that the algorithm can be deployed to a wider computing platform, and practical deployment is possible.

The invention discloses a shared energy storage optimization method of a multi-agent integrated energy system based on a cooperative game and an adaptive alternating direction multiplier method (A-ADMM), and belongs to the field of shared energy storage optimization scheduling. According to the method, aiming at the problems of high energy storage independent configuration cost, low utilization rate, difficulty in multi-agent income coordination and the like under the condition of coexistence of multiple microgrids, an energy storage agent is introduced as a neutral coordination agent, and a cooperative game model among an integrated energy micro-grid (IEM) alliance, a community energy storage (CES) system and the energy storage agent is constructed. Firstly, a multi-agent cooperative operation and income distribution mechanism is established based on the Nash negotiation theory, and the overall cooperative income maximization is realized on the premise of guaranteeing the basic income of each participant. Secondly, under a distributed optimization framework, unified modeling is carried out on electric and thermal multi-energy load demand response and a shared energy storage scheduling process, an A-ADMM method is provided, multi-body decoupling solution and privacy protection are realized, and the convergence speed and stability of the algorithm are effectively improved. Through example verification, the method provided by the invention can significantly reduce the operation cost of the integrated energy microgrid, improve the shared energy storage utilization efficiency and the new energy consumption capability, and enhance the operation flexibility and cooperative stability of the system.

The invention relates to a virtual power plant internal building cluster electricity and carbon sharing method, device and equipment and a storage medium. The method comprises the steps of determining a building cluster corresponding to each building based on acquired multi-energy load information and predicted power generation information; based on the building cluster load information corresponding to each building cluster obtained based on the multi-energy load information corresponding to each building corresponding to each building cluster, obtaining carbon emission flow distribution in the virtual power plant network to determine a target carbon emission factor corresponding to each building cluster; obtaining a sharable electric quantity and a sharable carbon quota corresponding to each building cluster, a first resource sharing cost value corresponding to the sharable electric quantity and a second resource sharing cost value corresponding to the sharable carbon quota based on the decision model corresponding to each building cluster, therefore, the exchange benefit of each building and the operation cost of each building accord with the target condition. By adopting the method, the electricity-carbon sharing enthusiasm of building users can be effectively improved.