66 results about "Energy conservation measure" patented technology

The invention relates to the technical field of tunnel sewage treatment, in particular to an intelligent sewage treatment system for an extra-long tunnel. Comprising a sewage monitoring module, a water quality analysis module, an automatic processing module, an energy consumption management module and an anomaly detection module. The sewage monitoring module monitors sewage parameters in real time through a distributed sensor array and transmits data. And the water quality analysis module constructs a water quality map through dynamic water quality balance calculation by using multispectral imaging and electrochemical sensing technologies, and generates reports and suggestions. And the automatic processing module adjusts processing parameters according to the report. And the energy consumption management module monitors and optimizes energy consumption, identifies a high-energy-consumption link through an algorithm and takes energy-saving measures. The abnormity detection module monitors the system in real time, identifies early warning abnormal conditions and sends early warning information and a fault report. According to the invention, efficient, accurate and intelligent treatment of sewage in the extra-long tunnel is realized, the sewage treatment efficiency is remarkably improved, and the energy consumption and the operation cost are remarkably reduced.

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 relates to the technical field of energy management, in particular to a public building energy consumption auditing statistical splitting method and system, and the method comprises the following steps: obtaining the operation time, power data and load change rate of air conditioning, lighting, elevator and office equipment, calculating the power and load contribution rate of the equipment, and constructing an equipment operation characteristic matrix. According to the method, accurate energy consumption distribution and optimization are realized through subdivision analysis and power contribution rate adjustment based on equipment characteristics, the identification accuracy of abnormal energy consumption is improved through detailed analysis of equipment operation data, and energy consumption waste is effectively reduced through fine measures of load adjustment, so that the energy consumption is reduced, and the energy consumption is reduced. According to the optimization measure, the energy consumption distribution is more reasonable, the pertinence and the implementation effect of the energy-saving measure are enhanced, and the efficiency and the effect of energy management are remarkably improved.

The invention discloses a traction energy-saving method and system based on dynamic load flow calculation for rail transit, electronic equipment and a readable storage medium, and the traction energy-saving method comprises the steps: planning a multi-vehicle collaborative energy-saving module through an ATO single-vehicle energy-saving + ATS working diagram, a dynamic diagram adjustment module according to passenger flow requirements, a regenerative braking energy feedback module and other modules; multi-dimensional coupling conditions of power supply, vehicles, signals, tracks and the like are dynamically analyzed, dynamic load flow calculation is performed from the perspective of energy flow through a dynamic load flow calculation and analysis module of the traction power supply system, technical means such as ATO energy saving, ATS energy saving and regenerative braking energy recovery device energy saving are dynamically coordinated, unified management and cooperative linkage are performed, and the dynamic load flow calculation and analysis module of the traction power supply system is realized. Energy consumption monitoring and statistical analysis are carried out for different traction energy-saving measures, and long-term energy-saving operation effect tracking and evaluation are carried out; and an energy-saving control strategy is continuously adjusted and optimized according to an analysis result through a prediction model between line energy consumption and operation data variables, so that a better energy-saving effect in a real sense is achieved.

The invention discloses a coal mine carbon emission prediction method and system based on digital twinning and LSTM, and the method comprises the steps: building a carbon emission source information coding standard based on a business demand and a digital twinning model of a mining area in combination with a production link and an auxiliary production link of the mining area; performing carbon emission edge calculation on the carbon emission energy consumption data of the carbon emission source by using a coal mine carbon emission accounting method to obtain carbon emission data; establishing a corresponding mapping relation between the coal mine digital twinborn model and a database of carbon edge computing equipment by utilizing a carbon emission source information coding standard; according to the mapping relation, mapping the carbon emission data of each link to a coal mine digital twinborn model one by one; and establishing a carbon emission prediction model by using an LSTM algorithm, inputting historical actual carbon emission data into the model, and predicting carbon emission data of the current day. According to the method, the current carbon emission situation of each link of coal mine production and auxiliary production can be known, the carbon emission trend of each emission point is mastered in real time, and the emission reduction strength of enterprise energy-saving measures is quantified.

The invention discloses a hydraulic engineering operation and maintenance management system based on digital twinning, and relates to the technical field of hydraulic engineering operation and maintenance management, and the system comprises an operation and maintenance management center which is in communication connection with the following modules: a sensor network module which is used for deploying multiple types of sensors in key equipment and links of a hydraulic engineering, and is used for providing a communication network for the key equipment and links of the hydraulic engineering; an intelligent sensor network is formed, and equipment operation data and energy consumption data of hydraulic engineering equipment and links are obtained. Operation data and energy consumption data of hydraulic engineering equipment and links are acquired through the sensor network, energy consumption simulation is performed in combination with the virtual model, and energy consumption distribution and fluctuation conditions of hydraulic engineering under different operation conditions are visually displayed, so that managers can accurately master the energy consumption conditions of the equipment and links, and the management efficiency is improved. Energy consumption abnormal points are found in time, a powerful basis is provided for formulating targeted energy-saving measures, energy waste is effectively avoided, and the energy utilization efficiency is improved.

The invention discloses a simplified calculation method for boiler thermal efficiency, and mainly relates to the field of thermal equipment performance state monitoring, analysis and diagnosis. Comprising the following steps: S1, determining various heat losses of a boiler, and constructing an anti-balance calculation model of boiler heat efficiency; s2, determining the heat efficiency of the boiler; by simplifying the measurement steps and the calculation formula, the thermal efficiency of the boiler can be quickly estimated without a high-precision instrument. According to the method, key parameters such as the exhaust gas temperature and the oxygen content are selected, and the empirical formula is combined, so that the complexity of measurement and calculation is remarkably reduced. The system is suitable for daily monitoring and operation adjustment of the boiler, helps an enterprise to timely discover low-efficiency conditions and take energy-saving measures, and reduces the operation cost while improving the operation convenience.

The invention discloses an energy-saving control method and system based on a building group spatial form, and relates to the technical field of intelligent building energy-saving control, and the method comprises the steps: inputting a building form characteristic parameter matrix into a multi-scale generative adversarial network, respectively extracting the overall layout characteristics, local spacing characteristics and surface detail characteristics of a building group, and calculating the overall layout characteristics, local spacing characteristics and surface detail characteristics of the building group; generating an energy consumption probability distribution thermodynamic diagram corresponding to the building group space distribution; and inputting the energy consumption probability distribution thermodynamic diagram into a discriminator of the multi-scale generative adversarial network, calculating an optimization gradient of each building morphological parameter, and generating a morphological adjustment instruction set in combination with a dynamic constraint function. The method not only improves the accuracy of energy consumption prediction, but also can identify a high-energy-consumption area and provide targeted energy-saving measures. The problems that in the prior art, energy consumption prediction precision is limited, and it is difficult to efficiently convert into operable energy-saving measures are solved.

The invention discloses a clustering method, system and equipment based on a sequential sequence and a storage medium, and the method comprises the steps: carrying out the reasonable classification and analysis of tree nodes through modular operation by employing a sequential sequence clustering method based on multi-view learning according to an energy consumption curve; different energy consumption curves are clustered, the evaluation content of each cluster is automatically output, and possible reasons are pushed, so that the operation and maintenance personnel can conveniently check, and the energy consumption check efficiency and accuracy are improved; and therefore, corresponding more reasonable and appropriate energy-saving measures can be formulated. In view of prominent energy consumption problem and high energy cost ratio caused by medical service demand increase and hospital scale expansion, the invention aims to reasonably classify tree nodes according to an energy consumption curve so as to formulate energy-saving measures.

The invention discloses an electric energy information acquisition and management method for an electric power system, and the method is characterized in that the method comprises the steps: S1, obtaining electric quantity, voltage and current data in real time through an electric energy acquisition device, and transmitting the data to a data acquisition system; s2, performing data cleaning, calibration, denoising and data visualization operation on the acquired electric quantity, voltage and current data; s3, performing energy consumption monitoring analysis according to the processed electric energy data, identifying an energy consumption peak period, and executing corresponding energy-saving measures for optimization; and S4, based on an optimization result, performing energy supply prediction by using a neural network. According to the invention, the neural network is introduced for power data acquisition, intelligent prediction of future energy demands is realized, and the problem of energy use optimization based on data driving is solved.

The invention discloses an enterprise energy efficiency evaluation and promotion model construction method based on electric power big data, and belongs to the field of data analysis. Comprising the steps of data collection; preprocessing the data; extracting characteristic indexes; constructing a model; evaluating and optimizing the model; and model interpretation application. Based on multi-dimensional electric power big data, an energy efficiency evaluation and improvement model is constructed, energy efficiency analysis and evaluation of key energy consumption enterprises are realized, the value of the electric power big data is fully mined, the enterprises are helped to understand own energy use efficiency, the energy efficiency improvement potential of the enterprises is mined, the enterprises are supported to formulate energy use strategies and energy saving measures, and the energy cost is reduced. And energy conservation, emission reduction and dual-carbon targets can be achieved. The method can be widely applied to the fields of power supply and consumption operation management and key energy consumption enterprise energy efficiency analysis and evaluation.

The invention discloses a building data intelligent management system and method based on artificial intelligence, and relates to the technical field of building data intelligent management, and the system comprises a building digital twin module, an Internet of Things intelligent service module, a regional analysis module, a historical data selection module and an intelligent sequence output module. The output end of the building digital twinning module is connected with the input end of the Internet of Things intelligent service module; the output end of the Internet of Things intelligent service module is connected with the input end of the regional analysis module; the output end of the regional analysis module is connected with the input end of the historical data selection module; and the output end of the historical data selection module is connected with the input end of the intelligent sequence output module. According to the invention, the construction efficiency can be improved, and monitoring and optimal management of energy consumption are realized through effective resource allocation and energy-saving measures; and in the aspect of improving integration, data pre-judgment is realized, and the probability of repeated operation and invalid construction is reduced.

The invention discloses an energy efficiency optimization system for training and optimizing according to actual data, and relates to the technical field of energy efficiency optimization. Comprising an energy consumption prediction model, a temperature optimization module, an adaptive control module, a data collection module, a model training module, a variable optimization module, a prediction maintenance module, a collaborative optimization module, a user operation module and an algorithm updating module. According to the energy efficiency optimization system for training and optimizing according to the actual data, the prediction model of the system is trained and optimized by using the actual data, and when the actual energy use condition changes, adjustment can be made in time to achieve the purpose of dynamic adjustment, so that the limitation of a fixed model and an algorithm is avoided, and the applicability of the system is improved; the method is particularly suitable for kitchen application scenes, and meanwhile, more input variables are continuously introduced in combination with actual situations to further improve the accuracy and reliability of the prediction model, so that the implementation effect of energy-saving measures is improved.

The invention relates to the technical field of energy management, in particular to a coking enterprise electric energy metering management method, which comprises the following steps of: forming a matrix metering network through an electric meter, a current sensor and a process control system deployed by a sensing layer; preprocessing the collected electric energy data through an edge computing gateway of the network layer and transmitting the electric energy data to the platform layer; the platform layer stores the electric energy data, performs correlation analysis in combination with the production data, and is used for completing dynamic calibration and peak-valley power consumption optimization; three-level electric energy metering management is completed in an application layer, and electric energy consumption, energy efficiency benchmarking and abnormal alarm information are displayed through a visual platform. The method has the advantages that step-by-step energy consumption monitoring and management from a whole plant to a workshop section to equipment are realized, links and equipment with high energy consumption can be accurately positioned, and a basis is provided for targeted energy-saving measures; and each level is clear in metering, and energy consumption indexes are decomposed layer by layer and are implemented to specific working sections and equipment, so that each level is clear to own energy consumption conditions.

A solar electric drainage device based on the Internet of Things technology comprises an electric drainage valve, a water conveying pipe, a liquid level switch, an I / O remote controller, an electric control box, a photovoltaic panel and a management cloud platform. The electric drain valve is connected with the steam pipe through the water conveying pipe, a liquid level switch is arranged on the water conveying pipe, the electric drain valve and the I / O remote controller are both installed in the electric control box, the photovoltaic panel is installed on the electric control box and used for providing power for equipment in the electric control box, the I / O remote controller is in control connection with the electric drain valve, and the management cloud platform is in monitoring connection with the I / O remote controller. The device is simple in structure, convenient to operate, capable of monitoring the working state and fault of equipment in real time, capable of mastering the drainage amount of the steam pipe network and estimating the heat loss of the pipe network through data analysis, capable of helping to formulate energy-saving measures, energy-saving, environment-friendly, flexible and practical.

A method includes receiving, by a distributed unit of an NG-RAN node (gNB-DU), a first message, the first message including a request for reporting an energy cost per network slice. The gNB-DU selects one or more user equipments (UEs), and transmits a second message to a central unit of the same NG-RAN node (gNB-CU), the second message including a report of the energy cost per network slice for the one or more UEs. The energy cost may be measured or predicted per UE and used to determine an energy saving action.

The invention relates to a method for assisting a driver (F) in driving a vehicle (104), wherein - fleet data (FD) relating to and influencing energy consumption of each fleet vehicle (101) (101.1 to 101.n) of a vehicle fleet (101) are transmitted to a central vehicle-external data processing device (105), - environment and / or infrastructure data (UID) describing the environment of the vehicle (104) and / or the respective fleet vehicle (101.1 to 101.n) are transmitted, - the fleet data (FD) and environment and / or infrastructure data (UID) are centralized and evaluated in the data processing device (105), - energy saving measures and associated driving recommendations (FE) for the operation of the vehicle (104) are determined by means of the data processing device (105), - the driving recommendations (FE) are issued inside the vehicle (104), - Driver's reactions (F) to the driving recommendations (FE) and resulting vehicle data relating to and influencing the vehicle's energy consumption (104) are recorded as feedback data (FBD) and - using the data processing device (105) to determine the effectiveness of the energy saving measures implemented by the driving recommendations (FE) based on the feedback data (FBD) and to take this into account in future determinations of energy saving measures.

The invention relates to the field of hotel management, in particular to an ESG index-based hotel data management system, which comprises a comprehensive management module and a plurality of guest room management modules, the comprehensive management module is connected with the plurality of guest room management modules, and each guest room is provided with one guest room management module; the guest room management module monitors guest room environment data and energy consumption data in real time, identifies guest check-in states and transmits the collected data to the comprehensive management module; and the comprehensive management module receives and judges the data transmitted by each guest room management module, takes energy-saving measures according to the judgment result, and transmits the control signal to the guest room management module to control the guest room management module to take the energy-saving measures. The technical problems that a traditional hotel is relatively dispersed in a data collection link and the subsequent analysis capability of collected data is insufficient are solved.

The present invention discloses an intelligent power consumption monitoring system for security equipment suitable for the maintenance of public security equipment, and belongs to the technical field of security monitoring. The present invention evaluates and predicts the power consumption of security equipment from two directions combining the operating status of the security equipment itself with the environmental conditions in which it is located, and estimates and analyzes the obtained equipment power consumption operating value HY and environmental impact value HQ to obtain an estimated power consumption value, and performs point plotting and drawing analysis on the estimated power consumption value and the actual power consumption value to obtain a current evaluation floating value, and finally performs a risk analysis on the current evaluation floating value and the historical power consumption floating value, and verifies the accuracy of the estimated power consumption according to the comparison result, so as to improve the accuracy of power consumption prediction, and in the case of combining the analysis from two perspectives of the equipment itself and the environment, not only can reasonable energy-saving measures be formulated according to the power consumption, but also the security equipment can be protected.

The application discloses an energy consumption monitoring method and system based on machine learning, and belongs to the technical field of data processing. The method can effectively avoid the problem of inaccurate identification caused by single data by acquiring multi-dimensional energy consumption related data corresponding to a to-be-monitored target. Machine learning training data is constructed according to the multi-dimensional energy consumption related data. The energy consumption prediction model is trained by using SLSPO based on the machine learning training data, which can effectively improve the identification accuracy of the energy consumption prediction model for data. Then, the trained energy consumption prediction model is used to identify the multi-dimensional energy consumption related data to be identified, and a future period energy consumption prediction value is obtained. Finally, energy consumption anomaly identification of the to-be-monitored target is performed according to the future period energy consumption prediction value, and energy consumption anomaly early warning is performed according to an energy consumption anomaly identification result obtained by energy consumption anomaly identification, thereby effectively improving the energy consumption anomaly monitoring accuracy and providing a strong reference for staff to implement energy-saving measures.

The present application provides an energy-saving method, device, equipment and storage medium for an electrostatic precipitator system, which are applied to the electrostatic precipitator system, and the method includes: obtaining an emission value indicating the outlet dust concentration of the electrostatic precipitator system and an energy consumption value indicating the power consumption of the electrostatic precipitator system; calculating the energy-saving space index of the electrostatic precipitator system according to the deviation of the emission value relative to the preset standard concentration, and the deviation of the energy consumption value relative to the rated power of the electrostatic precipitator system; the energy-saving space index is used to indicate the range of reduction of the power consumption of the electrostatic precipitator system under the condition of meeting the emission standards; determining and outputting the recommended energy-saving measures corresponding to the energy-saving space index. This scheme comprehensively considers the emission value and energy consumption value of the electrostatic precipitator system to give corresponding recommended energy-saving measures, avoiding the hidden danger of substandard emissions when energy-saving transformation is carried out considering only energy consumption.

Embodiments of the present disclosure relate to carbon-aware intelligent power managers for cluster nodes, and the present disclosure describes example power management devices and techniques. An example computing device includes one or more memories and one or more processors. The one or more processors are configured to determine node criticality metrics for cluster nodes based on executing the at least one machine learning model. The one or more processors are configured to determine an energy saving measure of the one or more energy saving measures to be applied to the node based on the node criticality metric. The one or more processors are configured to apply the energy saving measure to the node.

The invention provides a digital twinning-oriented campus energy consumption intelligent management method, which comprises the following steps of: projecting data into a three-dimensional campus model by adopting a space mapping algorithm according to an obtained energy consumption data set, and determining a distribution position of a high-energy-consumption area; if the determined high-energy-consumption area exceeds a preset threshold value, real-time data fluctuation is analyzed through an anomaly positioning algorithm, and a potential anomaly source is judged; obtaining the judged abnormal source information, and decomposing the energy consumption level from a macroscopic panorama to a microscopic building by adopting a multi-level analysis method to obtain a layered visual structure; for the obtained structure, rendering a dynamic thermodynamic diagram through a visual expression technology to obtain a visual spatial distribution view; extracting functional characteristic data from the obtained view, generating an energy-saving measure suggestion for the building position by adopting a decision tree algorithm, and determining an optimization path; and through the determined optimization path, the feedback cycle of the energy consumption data set is updated, and a continuous monitoring intelligent management framework is obtained.

A radio network node, which may be a terrestrial node or a non-terrestrial node, may determine to implement network energy saving measures based on a determined energy parameter value being determined to violate a corresponding energy parameter criterion. The node may transmit to a user equipment a paging delegation indication indicative that, during a paging occasion delegation period, at least one lightly loaded source paging occasion associated with the user equipment is to be temporarily suspended and delegated to a target paging occasion that may not be associated with the user equipment. The node may sleep during a delegated source paging occasion and may broadcast a paging message directed to the user equipment via the target paging occasion. The user equipment may avoid waking up during the delegated source paging occasion and instead may monitor the target paging occasion for paging messages during the paging occasion delegation period.

The invention discloses an analysis system and analysis method for the energy consumption of an electric loop of a distribution box, and the system comprises an acquisition unit which is used for collecting the current values of an input current port and an output current port in the electric loop, and a receiving unit which is used for receiving the power values of the input current port and the output current port collected by the acquisition unit. The processing unit is used for calculating the energy consumption of each power utilization loop according to the power value through a calculation model, the prediction unit calculates and predicts an energy consumption difference value by adopting a machine learning model based on the historical energy consumption data and the current energy consumption data of each power utilization loop, and finally the output unit outputs the predicted energy consumption difference value data of the power utilization loop. The method and the device are used for solving the technical problem that when a traditional electric energy management mode encounters the problems of abnormal energy consumption increase, low equipment efficiency or energy waste and the like, management lacking detailed data support is often difficult to quickly position the root of the problem, so that the optimal opportunity for taking effective energy-saving measures is delayed.

A radio network node, which may be a terrestrial node or a non-terrestrial node, may determine to implement network energy saving measures based on a determined energy parameter value being determined to violate a corresponding energy parameter criterion. The node may transmit to a user equipment a paging delegation indication indicative that, during a paging occasion delegation period, at least one lightly loaded source paging occasion associated with the user equipment is to be temporarily suspended and delegated to a target paging occasion that may not be associated with the user equipment. The node may sleep during a delegated source paging occasion and may broadcast a paging message directed to the user equipment via the target paging occasion. The user equipment may avoid waking up during the delegated source paging occasion and instead may monitor the target paging occasion for paging messages during the paging occasion delegation period.

The system according to this embodiment aims to provide optimal energy-saving measures based on the user's lifestyle and local climate. [Solution] The system according to the embodiment comprises a learning unit, a suggestion unit, an update unit, and a visualization unit. The learning unit learns the user's lifestyle habits related to energy consumption. The suggestion unit presents optimal energy saving measures based on the information learned by the learning unit. The update unit periodically improves and updates the saving measures presented by the suggestion unit based on the season and local climate. The visualization unit visualizes the user's degree of energy saving achievement.

The present invention discloses a traction energy-saving method, system, electronic device and readable storage medium based on dynamic power flow calculation for rail transit. The traction energy-saving method dynamically analyzes multi-dimensional coupling conditions such as power supply, vehicles, signals and tracks through modules such as ATO single-vehicle energy saving + ATS operation diagram planning multi-vehicle collaborative energy saving module, dynamic diagram adjustment module according to passenger flow requirements and regenerative braking energy feedback module, and performs dynamic power flow calculation from the perspective of energy flow through the traction power supply system power flow dynamic calculation and analysis module, dynamically coordinates technical means such as ATO energy saving, ATS energy saving and regenerative braking energy recovery device energy saving, conducts unified management and collaborative linkage, performs energy consumption monitoring and statistical analysis for different traction energy-saving measures, and tracks and evaluates long-term energy-saving operation effects; and continuously optimizes energy-saving control strategies based on analysis results through a prediction model between line energy consumption and operation data variables, thereby achieving truly better energy-saving effects.

The invention discloses an energy-saving analysis and control method for an intelligent air conditioner, relates to the technical field of intelligent air conditioners, and solves the problem that energy consumption adjustment of a traditional air conditioner lacks a quantitative basis and a dynamic optimization mechanism. High-frequency common energy consumption abnormity time periods are locked, energy consumption characteristics are bound, mismatching of energy-saving resources is avoided, and it is ensured that the time periods to be regulated and controlled are energy consumption abnormity scenes occurring at high frequency in a historical period through quantitative verification that the cross proportion ZB is larger than or equal to 0.5, and accidental fluctuation is avoided. According to the precise positioning mode, invalid regulation and control over normal energy consumption time periods are avoided, omission of key abnormal time periods is also prevented, clear action targets are provided for subsequent energy saving measures, and the problems of energy consumption waste or temperature control failure caused by blind regulation and control are reduced from the source.

To provide a technique for supporting energy saving planning.SOLUTION: In an energy saving planning support system, case information on energy saving measures in various buildings is accumulated in a DB, and at the time of retrieval, the DB is retrieved by retrieval conditions generated based on information designated on a screen by a user. When the number of hits F is equal to or larger than the number of display requests N, data is sorted in a sorting order designated by the user, and then top N pieces of the data are extracted, statistical work (calculation of an average value and the like) about an index value (an energy-saving rate, the amount of energy saving, etc.) is made for every energy saving measure about the extracted data, and a graph thereof and detailed data are collectively displayed on an extraction result screen. From information displayed on the screen, the user can easily check examples of energy saving measures in a building having requirements similar to those of the building managed by the user, and can easily determine what energy saving measures are effective in the building managed by the user.SELECTED DRAWING: Figure 13