CN114239972A - Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology - Google Patents

Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology Download PDF

Info

Publication number
CN114239972A
CN114239972A CN202111567123.XA CN202111567123A CN114239972A CN 114239972 A CN114239972 A CN 114239972A CN 202111567123 A CN202111567123 A CN 202111567123A CN 114239972 A CN114239972 A CN 114239972A
Authority
CN
China
Prior art keywords
energy consumption
energy
data
campus
consumption
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111567123.XA
Other languages
Chinese (zh)
Inventor
谭福太
谢方静
余昭胜
林海
陈庆文
张渊晟
马晓茜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Huijin Energy Efficiency Technology Co ltd
Original Assignee
Guangzhou Huijin Energy Efficiency Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou Huijin Energy Efficiency Technology Co ltd filed Critical Guangzhou Huijin Energy Efficiency Technology Co ltd
Priority to CN202111567123.XA priority Critical patent/CN114239972A/en
Publication of CN114239972A publication Critical patent/CN114239972A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The invention discloses a campus energy efficiency and electrical safety management method and system based on an artificial intelligence technology, wherein the method comprises the following steps: s1: acquiring real-time energy consumption data and environmental information of a front end; s2: classifying and storing the front-end real-time energy data and the environmental information acquired in the step S1 and historical energy data; s3: establishing a BP neural network model according to the real-time energy consumption data, optimizing the BP neural network model by adopting a particle swarm algorithm to predict the campus energy consumption, and obtaining the energy waste position and reason according to the prediction of the campus energy consumption; s4: and finding out the corresponding energy consumption equipment according to the energy waste position, and automatically controlling the working state of the energy consumption equipment. The invention provides intelligent energy management technologies such as energy dynamic monitoring information, energy consumption data analysis and information publishing, equipment energy-saving analysis, electric power safety precaution and the like for a school management department.

Description

Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology
Technical Field
The invention relates to the field of energy and electrical safety problems of campuses, in particular to a campus energy efficiency and electrical safety management method and system based on an artificial intelligence technology.
Background
Along with the change of teaching environment and the development of teaching technology, the total energy consumption of schools continuously rises, a relatively serious phenomenon of energy resource waste exists, and schools belong to occasions with relatively high personnel density and have high attention to electrical safety. Therefore, the work of energy conservation, emission reduction and safety prevention is very important for schools. However, due to the fact that the school buildings are large in types and quantity and large in occupied area, the campus not only has public buildings such as teaching buildings, scientific research buildings and administrative office buildings, but also has residential buildings such as dormitory buildings and living auxiliary buildings such as canteens and bathrooms. Therefore, the energy management and fire prevention work has the following limitations: the school equipment is many, hardly unify monitoring in-service use condition, the difficult location leading cause of unusual power consumption condition, the school scene is big, lacks unified equipment management and control platform, the scene power consumption lacks unified management and control analysis, the school personnel are many, it is difficult to high-power dangerous equipment management and control, so many schools do not have reasonable energy management and electric fire prevention system.
At present, the existing school energy consumption and safety management system and method have the following defects: the energy consumption index is not clear, the real-time index cannot be dynamically monitored, the operability is poor, an energy-saving space is not further developed, and the electric fire early warning and automatic management technology is incomplete. Therefore, there is a need to develop an integrated management system based on artificial intelligence technology for energy and security in schools.
The prior art discloses a cloud computing big data based campus energy consumption monitoring system, which comprises a data acquisition layer, a data processing layer and a display layer, wherein the data acquisition layer is in communication connection with the data processing layer, and the data processing layer is in communication connection with the display layer; the invention has the beneficial effects that: the consumption data of water, electricity and gas in the campus are accurately acquired through the data acquisition layer, the data acquired by the data acquisition layer are calculated and stored through the data processing layer, and the energy consumption monitoring result is visually displayed in a circular ratio mode through the calculation result of the data processing layer by the display layer. The patent also has the defects of incomplete automatic management technology and the like.
Disclosure of Invention
The invention mainly aims to provide a campus energy efficiency and electrical safety management method based on an artificial intelligence technology, which promotes the refinement of school energy-saving management and achieves the purposes of safe energy utilization, energy conservation and emission reduction.
The invention further aims to provide a campus energy efficiency and electrical safety management system based on the artificial intelligence technology.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a campus energy efficiency and electrical safety management method based on an artificial intelligence technology comprises the following steps:
s1: acquiring real-time energy consumption data and environmental information of a front end;
s2: classifying and storing the front-end real-time energy data and the environmental information acquired in the step S1 and historical energy data;
s3: establishing a BP neural network model according to the real-time energy consumption data, optimizing the BP neural network model by adopting a particle swarm algorithm to predict the campus energy consumption, and obtaining the energy waste position and reason according to the prediction of the campus energy consumption;
s4: and finding out the corresponding energy consumption equipment according to the energy waste position, and automatically controlling the working state of the energy consumption equipment.
Preferably, the step S1 of collecting the front-end real-time energy consumption data and the environmental information specifically includes:
the system comprises a power consumption, a solar radiation value, solar energy, wind speed, wind energy, water consumption, natural gas consumption, direct heat supply, indoor temperature, a calendar table, people number and water pump power consumption.
Preferably, the establishing of the BP neural network model in step S3 specifically includes the following steps:
s201: setting an input neuron, an output neuron and a hidden layer neuron;
s202: setting the maximum training step number, the learning rate and the training stopping parameters;
s203: carrying out normalized mapping on the sample data between (0, 1);
s204: the output of the BP neural network model is subjected to inverse normalization processing;
s205: the BP neural network model is trained by using electricity consumption, solar radiation value, wind speed, water consumption, natural gas consumption, direct heat supply quantity, indoor temperature, universal calendar, people number and water pump electricity consumption as input data, and using electricity consumption, solar energy consumption, wind energy consumption, natural gas consumption, direct heat supply quantity, water consumption and water pump electricity consumption as output data;
s206: and when the error is smaller than the training stopping parameter, finishing the training to obtain the preliminarily trained BP neural network model.
Preferably, in step S3, optimizing the BP neural network model by using a particle swarm algorithm, specifically including the following steps:
s211: initializing maximum iteration times k, particle number m, inertia weight omega and learning factor c of particle swarm optimization1And c2；
S212: determining the variation range of the position and the speed of the particles, setting the variation range as a boundary value if the variation range exceeds the range D, and randomly selecting the initial position and the speed of each particle;
s213: calculating and comparing the fitness value of the particles by taking the prediction error of the BP neural network model as the fitness function of the particle swarm, and finding the optimal position of the particles;
s214: in each iteration process, the optimal solution P found by the particle at presenti＝(Pi1,Pi2,…,PiD) The best solution currently found for the whole population is Ps＝(Ps1,Ps2,…,PsD) Each particle updates its own velocity and position by the two optimal solutions, and the corresponding evolution equation is
vk+1＝ωvk+1r1(Pi-present)+2r2(Pg-present)
Pk+1＝Pk+vk+1
In the formula, vk+1、vkRespectively the velocity of the particle at the k-th iteration, Pk+1、PkRespectively, the position of the particle at the k-th iteration, the present represents the current position of the particle, r1、r2Is [0, 1 ]]Random numbers generated within the range;
s215: stopping running when the maximum iteration number k is exceeded; and searching the optimal network connection weight value within the preset iteration times, and optimizing the BP neural network model.
Preferably, the step S3 further includes establishing a prediction error estimation model, and when the predicted error of the campus energy usage is within the allowable range, comparing the current campus energy consumption situation with the campus energy consumption lowest working mode to form an energy cost report, so as to obtain the energy waste location and reason; when the predicted error of the campus energy consumption is not within the allowable range, the front-end real-time energy consumption data and the environmental information are collected again, the BP neural network model is built again, and the particle swarm optimization is adopted to optimize the BP neural network model.
Preferably, the prediction error estimation model specifically includes:
s221: predicting data result s of BP neural network model optimized by particle swarm optimizationfAnd historical energy data sqComparing, and calculating the actual average energy consumption s in different time periodsq-Actual fluctuation coefficient S of energy consumptionqaAnd fluctuations in energy use prediction valuesCoefficient Sqf：
Figure BDA0003422138700000031
Figure BDA0003422138700000032
Figure BDA0003422138700000041
n denotes the predicted data result sfAnd historical energy data sqThe number of (2);
s222: calculating a prediction error estimate EMAE：
EMAE＝0+1Sqa+2Sqf+3Sq-
In the formula, beta0Coefficient of constant term, beta1-β3Correlation coefficients for corresponding variables;
s223: carrying out multiple linear regression analysis by utilizing regression function and calculating to obtain correlation coefficient beta0、β1、β2、β3Calculating a confidence interval estimate
Figure BDA0003422138700000042
And
Figure BDA0003422138700000043
Figure BDA0003422138700000044
Figure BDA0003422138700000045
in the formula (I), the compound is shown in the specification,
Figure BDA0003422138700000046
are each beta0The lower limit and the upper limit of (c),
Figure BDA0003422138700000047
are each beta1The lower limit and the upper limit of (c),
Figure BDA0003422138700000048
are each beta2The lower limit and the upper limit of (c),
Figure BDA0003422138700000049
are each beta3Lower and upper limits of (d);
s224: obtaining an upper bound for a predicted value of energy usage data
Figure BDA00034221387000000410
And lower limit
Figure BDA00034221387000000411
Preferably, the method further comprises the step S5: and calculating an energy consumption index through the historical energy consumption data stored in the step S2, acquiring the energy consumption modes of the energy consumption systems in different areas in the campus by adopting a linear fitting method, fitting an energy consumption proportion equation of the energy consumption system equipment, and visualizing the energy saving proportion of the energy consumption equipment.
Preferably, the energy consumption index is calculated, the energy consumption modes of the energy consumption systems in different areas in the campus are obtained by adopting a linear fitting method, and an energy consumption proportion equation of the energy consumption system equipment is fitted, specifically:
the energy consumption index is the whole energy consumption level of schools and building buildings, and is divided into:
a. comprehensive energy consumption:
Figure BDA00034221387000000412
in the formula: e is real-time comprehensive energy consumption, and the unit is kgce; n is the number of energy types consumed; eiFor the ith energy quantity, k, actually consumed in production and/or service activitiesiIs of the ith kindThe standard coal coefficient of energy is reduced;
b. building area energy consumption: e.g. of the typeN＝N/N
In the formula: a. theNThe total area of different building buildings is expressed as m 2; eNThe unit is kgce for the comprehensive energy consumption of the building; e.g. of the typeNThe unit area energy consumption of the building is kgce/m2；
c. The per-capita energy consumption of a building is as follows: i.e. iN＝N/N
In the formula: pNThe total number of people in different building buildings; i.e. iNThe unit is kgce/p for the per-capita energy consumption of a building;
d. school area energy consumption: e ═ E/A
In the formula: a is the total area of the school, and the unit is m2(ii) a e is the area energy consumption of the school, and the unit is kgce/m2；
e. Per-capita energy consumption in schools: i ═ E/P
In the formula: p is the total number of schools; i is the per-capita energy consumption of the school, and the unit is kgce/p;
the energy consumption modes of energy consumption systems in different areas in the campus are obtained by adopting a linear fitting method, and an energy consumption proportion equation of energy consumption system equipment is fitted:
energy consumption data are classified according to building-room-function and divided into an air conditioning system, a lighting system, an elevator system, a water using system, a natural gas system and a water pump, and the comprehensive energy consumption of the energy using system is respectively Ea、Eb、Ec、Ed、EeAnd Ef：
EN＝w1Ea+w2Eb+w3Ec+w4Ed+w5Ee+w6Ef
In the formula, wkFor the weight coefficient, k is 1,2,3,4,5, 6.
The utility model provides a campus efficiency and electrical safety management system based on artificial intelligence technique, includes front end data acquisition module, system data platform, energy analysis module, historical power consumption analysis module, automatic supervision module and power consumption safety early warning module, wherein:
the front-end data acquisition module acquires front-end real-time energy consumption data and environmental information and transmits the front-end real-time energy consumption data and the environmental information to the system data platform in real time;
the system data platform stores the real-time energy consumption data and the environmental information of the front end collected in real time and historical energy consumption data in a classified manner;
the energy analysis module establishes a BP neural network model by utilizing real-time energy utilization data stored by the system data platform, optimizes the BP neural network model by adopting a particle swarm algorithm to predict campus energy consumption, and obtains energy waste positions and reasons according to the prediction of the campus energy consumption;
the historical energy consumption analysis module utilizes the historical energy consumption data stored by the system data platform to fit and acquire energy consumption modes of energy consumption systems in different areas in the campus, so that an energy consumption proportion equation of energy consumption system equipment is obtained, and the energy saving proportion of the energy consumption equipment is visualized;
the automatic supervision module utilizes the real-time energy utilization data stored by the system data platform to automatically control the running state of each energy consumption device and is provided with a reminding function;
and the intelligent early warning module compares the real-time energy consumption data stored by the system data platform with a set safety threshold, and when the real-time energy consumption data exceeds the safety threshold, the intelligent early warning module cuts off the power supply of the corresponding energy consumption equipment and sends early warning information.
Preferably, the front-end data acquisition module acquires real-time energy consumption data and environmental information of the front end through an infrared sensor, a multifunctional electric quantity monitoring instrument, a line temperature probe and a GIS (geographic information System), and specifically comprises the following steps:
acquiring an indoor infrared image by using an infrared sensor, converting the indoor infrared image into local temperature data of each indoor area, acquiring indoor and outdoor environment temperature and counting the number of people in the room environment;
collecting current, voltage, leakage current, power and power factor of indoor electric equipment by using a multifunctional electric quantity monitoring instrument;
collecting the temperature of a circuit wire by using a circuit temperature probe;
and acquiring meteorological data in the campus boundary by using a GIS (geographic information system) technical system.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
(1) the invention not only collects basic energy consumption data (electric energy, water energy and natural gas amount), but also sets a GIS technology to collect meteorological data in a campus area and collect available resource data of solar energy and wind energy, and establishes a comprehensive and specific energy consumption database;
(2) the invention provides an artificial intelligence technology based on a BP neural network and a particle swarm algorithm for campus energy consumption prediction, which has the self-adaptive optimization characteristic of self-learning capability, can accurately predict the energy consumption in a campus area and is convenient for searching energy waste positions;
(3) specific energy consumption indexes are calculated by using historical energy consumption data, a fitting equation of the energy consumption of a main energy consumption system and the total energy consumption of a building (public area) is established, and the energy-saving proportion of energy consumption equipment is visualized.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a flow chart diagram of the method for establishing the self-optimized PSO-BP neural network model of the present invention.
FIG. 3 is a block diagram of the system of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a campus energy efficiency and electrical safety management method based on an artificial intelligence technology, as shown in fig. 1, the method includes the following steps:
s1: acquiring real-time energy consumption data and environmental information of a front end;
s2: classifying and storing the front-end real-time energy data and the environmental information acquired in the step S1 and historical energy data;
s3: establishing a BP neural network model according to the real-time energy consumption data, optimizing the BP neural network model by adopting a particle swarm algorithm to predict the campus energy consumption, and obtaining the energy waste position and reason according to the prediction of the campus energy consumption;
s4: and finding out the corresponding energy consumption equipment according to the energy waste position, and automatically controlling the working state of the energy consumption equipment.
In step S1, acquiring front-end real-time energy consumption data and environmental information specifically includes:
the system comprises a power consumption, a solar radiation value, solar energy, wind speed, wind energy, water consumption, natural gas consumption, direct heat supply, indoor temperature, a calendar table, people number and water pump power consumption.
In step S3, a BP neural network model is established, and the prediction of campus energy usage is performed by optimizing the BP neural network model using a particle swarm optimization, so as to obtain the establishing step of the adaptive optimization PSO-BP neural network model shown in fig. 2.
Establishing a BP neural network model in step S3, specifically comprising the following steps:
s201: setting 10 input neurons, 7 output neurons and 10 hidden layer neurons, wherein the data prediction structure of the neural network is 5-12-1, data values sequentially arranged into 5 points are selected as network input, and the energy use data value of the 6 th following point is predicted;
s202: set the maximum training step number of 1000 steps, learning rate of 0.01 and stop training parameter of 10-3；
S203: the S-heart activation function is adopted, and the normalization function mapminmax in the MATLAB is utilized to map the sample data between (0,1) in a normalization manner, so that the neural network operation is quicker and more accurate;
s204: the output of the BP neural network model is subjected to inverse normalization processing;
s205: the BP neural network model is trained by using electricity consumption, solar radiation value, wind speed, water consumption, natural gas consumption, direct heat supply quantity, indoor temperature, universal calendar, people number and water pump electricity consumption as input data, and using electricity consumption, solar energy consumption, wind energy consumption, natural gas consumption, direct heat supply quantity, water consumption and water pump electricity consumption as output data;
s206: and when the error is smaller than the training stopping parameter, finishing the training to obtain the preliminarily trained BP neural network model.
In step S3, optimizing the BP neural network model by using a particle swarm algorithm, specifically including the steps of:
s211: initializing the maximum iteration number k of the particle swarm algorithm to 200, the number m of the particles to 40, the inertia weight omega and the learning factor c1And c2；
S212: determining the variation range of the position and the speed of the particles, setting the variation range as a boundary value if the variation range exceeds the range D, and randomly selecting the initial position and the speed of each particle;
s213: calculating and comparing the fitness value of the particles by taking the prediction error of the BP neural network model as the fitness function of the particle swarm, and finding the optimal position of the particles;
s214: in each iteration process, the optimal solution P found by the particle at presenti＝(Pi1,Pi2,…,PiD) The best solution currently found for the whole population is Ps＝(Ps1,Ps2,…,PsD) Each particle updates its own velocity and position by the two optimal solutions, and the corresponding evolution equation is
vk+1＝ωvk+c1r1(Pi-present)+c2r2(Pg-present)
Pk+1＝Pk+vk+1
In the formula, vk+1、vkAre respectively the k-th iterationVelocity of the particles, Pk+1、PkRespectively, the position of the particle at the k-th iteration, the present represents the current position of the particle, r1、r2Is [0, 1 ]]Random numbers generated within the range;
s215: stopping running when the maximum iteration number k is exceeded; and searching the optimal network connection weight value within the preset iteration times, and optimizing the BP neural network model.
Example 2
The embodiment is based on embodiment 1, and the step S3 further includes establishing a prediction error estimation model, and when the predicted error of the campus energy consumption is within the allowable range, comparing the current campus energy consumption situation with the campus energy consumption lowest working mode to form an energy cost report, so as to obtain the energy waste location and reason; when the predicted error of the campus energy consumption is not within the allowable range, the front-end real-time energy consumption data and the environmental information are collected again, the BP neural network model is built again, and the particle swarm optimization is adopted to optimize the BP neural network model.
The prediction error estimation model specifically comprises:
s221: predicting data result s of BP neural network model optimized by particle swarm optimizationfAnd historical energy data sqComparing, and calculating the actual average energy consumption s in different time periodsq-Actual fluctuation coefficient S of energy consumptionqaAnd predicting the fluctuation coefficient S using the energyqf：
Figure BDA0003422138700000081
Figure BDA0003422138700000091
Figure BDA0003422138700000092
n denotes the predicted data result sfAnd historical energy data sqThe number of (2);
s222: calculating a prediction error estimate EMAE：
EMAE＝β0+β1Sqa+β2Sqf+β3Sq-
In the formula, beta0Coefficient of constant term, beta1-β3Correlation coefficients for corresponding variables;
s223: carrying out multiple linear regression analysis by utilizing regression function and calculating to obtain correlation coefficient beta0、β1、β2、β3Calculating a confidence interval estimate
Figure BDA0003422138700000093
And
Figure BDA0003422138700000094
set up biAnd bintiThe regression coefficient estimated values and all the regression coefficient confidence interval estimated values are respectively:
Figure BDA0003422138700000095
Figure BDA0003422138700000096
Figure BDA0003422138700000097
in the formula (I), the compound is shown in the specification,
Figure BDA0003422138700000098
are each beta0The lower limit and the upper limit of (c),
Figure BDA0003422138700000099
are each beta1The lower limit and the upper limit of (c),
Figure BDA00034221387000000910
are each beta2The lower limit and the upper limit of (c),
Figure BDA00034221387000000911
are each beta3Lower and upper limits of (d);
s224: obtaining an upper bound for a predicted value of energy usage data
Figure BDA00034221387000000912
And lower limit
Figure BDA00034221387000000913
Energy consumption indexes are calculated by taking one day as a period through historical data stored in a universal calendar table and a system data platform, and energy consumption modes of energy utilization systems in different regional periods in a campus are obtained by adopting a regression analysis method and are searched for regularity.
Further comprising step S5: and calculating an energy consumption index through the historical energy consumption data stored in the step S2, acquiring the energy consumption modes of the energy consumption systems in different areas in the campus by adopting a linear fitting method, fitting an energy consumption proportion equation of the energy consumption system equipment, and visualizing the energy saving proportion of the energy consumption equipment.
The energy consumption index is calculated, the energy consumption modes of the energy consumption systems in different areas in the campus are obtained by adopting a linear fitting method, and an energy consumption proportion equation of the energy consumption system equipment is fitted, and the method specifically comprises the following steps:
the energy consumption index is the whole energy consumption level of schools and building buildings, and is divided into:
a. comprehensive energy consumption:
Figure BDA0003422138700000101
in the formula: e is real-time comprehensive energy consumption, and the unit is kgce; n is the number of energy types consumed; eiFor the ith energy quantity, k, actually consumed in production and/or service activitiesiThe standard coal coefficient is the standard coal coefficient of the ith energy;
b. building floor area energy consumption：eN＝EN/AN
In the formula: a. theNThe total area of different building buildings is expressed as m 2; eNThe unit is kgce for the comprehensive energy consumption of the building; e.g. of the typeNThe unit area energy consumption of the building is kgce/m2；
c. The per-capita energy consumption of a building is as follows: i.e. iN＝EN/PN
In the formula: pNThe total number of people in different building buildings; i.e. iNThe unit is kgce/p for the per-capita energy consumption of a building;
d. school area energy consumption: e ═ E/A
In the formula: a is the total area of the school, and the unit is m2(ii) a e is the area energy consumption of the school, and the unit is kgce/m2；
e. Per-capita energy consumption in schools: i ═ E/P
In the formula: p is the total number of schools; i is the per-capita energy consumption of the school, and the unit is kgce/p;
the energy consumption modes of energy consumption systems in different areas in the campus are obtained by adopting a linear fitting method, and an energy consumption proportion equation of energy consumption system equipment is fitted:
energy consumption data are classified according to building-room-function and divided into an air conditioning system, a lighting system, an elevator system, a water using system, a natural gas system and a water pump, and the comprehensive energy consumption of the energy using system is respectively Ea、Eb、Ec、Ed、EeAnd Ef：
EN＝w1Ea+w2Eb+w3Ec+w4Ed+w5Ee+w6Ef
In the formula, wkFor the weight coefficient, k is 1,2,3,4,5, 6.
After data statistics, a judgment coefficient R is output2And weight parameter wk of fitting equation when R2The closer to 1, the better the fit to the regression equation, when R2In [0.4,1 ]]Can prove the accuracy of the fitting equation
Example 3
The utility model provides a campus efficiency and electric safety management system based on artificial intelligence technique, as shown in fig. 3, includes front end data acquisition module, system data platform, energy analysis module, historical power consumption analysis module, automatic supervision module and power consumption safety precaution module, wherein:
the front-end data acquisition module acquires front-end real-time energy consumption data and environmental information and transmits the front-end real-time energy consumption data and the environmental information to the system data platform in real time;
the system data platform stores the real-time energy consumption data and the environmental information of the front end collected in real time and historical energy consumption data in a classified manner;
the energy analysis module establishes a BP neural network model by utilizing real-time energy utilization data stored by the system data platform, optimizes the BP neural network model by adopting a particle swarm algorithm to predict campus energy consumption, and obtains energy waste positions and reasons according to the prediction of the campus energy consumption;
the historical energy consumption analysis module utilizes the historical energy consumption data stored by the system data platform to fit and acquire energy consumption modes of energy consumption systems in different areas in the campus, so that an energy consumption proportion equation of energy consumption system equipment is obtained, and the energy saving proportion of the energy consumption equipment is visualized;
the automatic supervision module utilizes the real-time energy utilization data stored by the system data platform to automatically control the running state of each energy consumption device and is provided with a reminding function;
and the intelligent early warning module compares the real-time energy consumption data stored by the system data platform with a set safety threshold, and when the real-time energy consumption data exceeds the safety threshold, the intelligent early warning module cuts off the power supply of the corresponding energy consumption equipment and sends early warning information.
The front end data acquisition module collects real-time energy consumption data and environmental information of the front end through an infrared sensor, a multifunctional electric quantity monitoring instrument, a line temperature probe and a GIS technical system, and specifically comprises the following steps:
acquiring an indoor infrared image by using an infrared sensor, converting the indoor infrared image into local temperature data of each indoor area, acquiring indoor and outdoor environment temperature and counting the number of people in the room environment;
collecting current, voltage, leakage current, power and power factor of indoor electric equipment by using a multifunctional electric quantity monitoring instrument;
collecting the temperature of a circuit wire by using a circuit temperature probe;
and acquiring meteorological data in the campus boundary by using a GIS (geographic information system) technical system.
And the automatic monitoring module automatically adjusts/closes the electric equipment of the air conditioner and the lighting system according to a data source collected by the data of the infrared sensor.
The electricity safety early warning module obtains the circuit running state by using the multifunctional electric quantity monitor meter and the circuit temperature probe, and discovers potential safety hazards of the electric circuit and the electric equipment in real time.
The system data platform is used for providing relevant information such as administrator setting, energy consumption type, real-time data division type, historical information, calendar marks and the like, and comprises forced start and stop of equipment and adjustment of key operation parameters.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A campus energy efficiency and electrical safety management method based on an artificial intelligence technology is characterized by comprising the following steps:
s1: acquiring real-time energy consumption data and environmental information of a front end;
s2: classifying and storing the front-end real-time energy data and the environmental information acquired in the step S1 and historical energy data;
s3: establishing a BP neural network model according to the real-time energy consumption data, optimizing the BP neural network model by adopting a particle swarm algorithm to predict the campus energy consumption, and obtaining the energy waste position and reason according to the prediction of the campus energy consumption;
s4: and finding out the corresponding energy consumption equipment according to the energy waste position, and automatically controlling the working state of the energy consumption equipment.
2. The method for managing the campus energy efficiency and the electrical safety based on the artificial intelligence technology as claimed in claim 1, wherein the step S1 of collecting the front-end real-time energy consumption data and the environmental information specifically includes:
the system comprises a power consumption, a solar radiation value, solar energy, wind speed, wind energy, water consumption, natural gas consumption, direct heat supply, indoor temperature, a calendar table, people number and water pump power consumption.
3. The artificial intelligence technology-based campus energy efficiency and electrical safety management method according to claim 2, wherein the establishing of the BP neural network model in step S3 specifically includes the following steps:
s201: setting an input neuron, an output neuron and a hidden layer neuron;
s202: setting the maximum training step number, the learning rate and the training stopping parameters;
s203: carrying out normalized mapping on the sample data between (0, 1);
s204: the output of the BP neural network model is subjected to inverse normalization processing;
s205: the BP neural network model is trained by using electricity consumption, solar radiation value, wind speed, water consumption, natural gas consumption, direct heat supply quantity, indoor temperature, universal calendar, people number and water pump electricity consumption as input data, and using electricity consumption, solar energy consumption, wind energy consumption, natural gas consumption, direct heat supply quantity, water consumption and water pump electricity consumption as output data;
s206: and when the error is smaller than the training stopping parameter, finishing the training to obtain the preliminarily trained BP neural network model.
4. The artificial intelligence technology-based campus energy efficiency and electrical safety management method according to claim 3, wherein in step S3, a particle swarm algorithm is adopted to optimize the BP neural network model, and the method specifically comprises the following steps:
s211: initializing maximum iteration times k, particle number m, inertia weight omega and learning factor c of particle swarm optimization1And c2；
S212: determining the variation range of the position and the speed of the particles, setting the variation range as a boundary value if the variation range exceeds the range D, and randomly selecting the initial position and the speed of each particle;
s213: calculating and comparing the fitness value of the particles by taking the prediction error of the BP neural network model as the fitness function of the particle swarm, and finding the optimal position of the particles;
s214: in each iteration process, the optimal solution P found by the particle at presenti＝(Pi1,Pi2,…,PiD) The best solution currently found for the whole population is Ps＝(Ps1,Ps2,…,PsD) Each particle updates its own velocity and position by the two optimal solutions, and the corresponding evolution equation is
vk+1＝ωvk+c1r1(Pi-present)+c2r2(Pg-present)
Pk+1＝Pk+vk+1
In the formula, vk+1、vkRespectively the velocity of the particle at the k-th iteration, Pk+1、PkRespectively, the position of the particle at the k-th iteration, the present represents the current position of the particle, r1、r2Is [0, 1 ]]Random numbers generated within the range;
s215: stopping running when the maximum iteration number k is exceeded; and searching the optimal network connection weight value within the preset iteration times, and optimizing the BP neural network model.
5. The method for campus energy efficiency and electrical safety management based on artificial intelligence technology according to claim 4, wherein step S3 further includes establishing a prediction error estimation model, and when the prediction error of the campus energy usage is within an allowable range, comparing the current campus energy consumption situation with the campus energy consumption lowest working mode to form an energy cost report, and obtaining the energy waste location and reason; when the predicted error of the campus energy consumption is not within the allowable range, the front-end real-time energy consumption data and the environmental information are collected again, the BP neural network model is built again, and the particle swarm optimization is adopted to optimize the BP neural network model.
6. The artificial intelligence technology-based campus energy efficiency and electrical safety management method according to claim 5, wherein the prediction error estimation model specifically comprises:
s221: predicting data result s of BP neural network model optimized by particle swarm optimizationfAnd historical energy data sqComparing, and calculating the actual average energy consumption s in different time periodsq-Actual fluctuation coefficient S of energy consumptionqaAnd predicting the fluctuation coefficient S using the energyqf：
Figure FDA0003422138690000031
Figure FDA0003422138690000032
Figure FDA0003422138690000033
n denotes the predicted data result sfAnd historical energy data sqThe number of (2);
s222: calculating a prediction error estimate EMAE：
EMAE＝β0+β1Sqa+β2Sqf+β3Sq-
In the formula, beta0Coefficient of constant term, beta1-β3Correlation coefficients for corresponding variables;
s223: carrying out multiple linear regression analysis by utilizing regression function and calculating to obtain correlation coefficient beta0、β1、β2、β3Calculating a confidence interval estimate
Figure FDA0003422138690000034
And
Figure FDA0003422138690000035
Figure FDA0003422138690000036
Figure FDA0003422138690000037
in the formula (I), the compound is shown in the specification,
Figure FDA0003422138690000038
are each beta0The lower limit and the upper limit of (c),
Figure FDA0003422138690000039
are each beta1The lower limit and the upper limit of (c),
Figure FDA00034221386900000310
are each beta2The lower limit and the upper limit of (c),
Figure FDA00034221386900000311
are each beta3Lower and upper limits of (d);
s224: obtaining an upper bound for a predicted value of energy usage data
Figure FDA00034221386900000312
And lower limit
Figure FDA00034221386900000313
7. The artificial intelligence technology-based campus energy efficiency and electrical safety management method according to claim 6, further comprising step S5: and calculating an energy consumption index through the historical energy consumption data stored in the step S2, acquiring the energy consumption modes of the energy consumption systems in different areas in the campus by adopting a linear fitting method, fitting an energy consumption proportion equation of the energy consumption system equipment, and visualizing the energy saving proportion of the energy consumption equipment.
8. The artificial intelligence technology-based campus energy efficiency and electrical safety management method according to claim 7, wherein the energy consumption index is calculated, energy consumption modes of energy consumption systems in different areas in a campus are obtained by adopting a linear fitting method, and an energy consumption proportion equation of energy consumption system equipment is fitted, specifically:
the energy consumption index is the whole energy consumption level of schools and building buildings, and is divided into:
a. comprehensive energy consumption:
Figure FDA00034221386900000314
in the formula: e is real-time comprehensive energy consumption, and the unit is kgce; n is the number of energy types consumed; eiFor the ith energy quantity, k, actually consumed in production and/or service activitiesiThe standard coal coefficient is the standard coal coefficient of the ith energy;
b. building area energy consumption: e.g. of the typeN＝EN/AN
In the formula: a. theNThe total area of different building buildings is expressed as m 2; eNThe unit is kgce for the comprehensive energy consumption of the building; e.g. of the typeNThe unit area energy consumption of the building is kgce/m2；
c. The per-capita energy consumption of a building is as follows: i.e. iN＝EN/PN
In the formula: pNThe total number of people in different building buildings; i.e. iNThe unit is kgce/p for the per-capita energy consumption of a building;
d. school area energy consumption: e ═ E/A
In the formula: a is the total area of the school, and the unit is m2(ii) a e is the area energy consumption of the school, and the unit is kgce/m2；
e. Per-capita energy consumption in schools: i ═ E/P
In the formula: p is the total number of schools; i is the per-capita energy consumption of the school, and the unit is kgce/p;
the energy consumption modes of energy consumption systems in different areas in the campus are obtained by adopting a linear fitting method, and an energy consumption proportion equation of energy consumption system equipment is fitted:
energy consumption data are classified according to building-room-function and divided into an air conditioning system, a lighting system, an elevator system, a water using system, a natural gas system and a water pump, and the comprehensive energy consumption of the energy using system is respectively Ea、Eb、Ec、Ed、EeAnd Ef：
EN＝w1Ea+w2Eb+w3Ec+w4Ed+w5Ee+w6Ef
In the formula, wkFor the weight coefficient, k is 1,2,3,4,5, 6.
9. The utility model provides a campus efficiency and electrical safety management system based on artificial intelligence technique which characterized in that, includes front end data acquisition module, system data platform, energy analysis module, historical power consumption analysis module, automatic supervision module and power consumption safety precaution module, wherein:
the front-end data acquisition module acquires front-end real-time energy consumption data and environmental information and transmits the front-end real-time energy consumption data and the environmental information to the system data platform in real time;
the system data platform stores the real-time energy consumption data and the environmental information of the front end collected in real time and historical energy consumption data in a classified manner;
the energy analysis module establishes a BP neural network model by utilizing real-time energy utilization data stored by the system data platform, optimizes the BP neural network model by adopting a particle swarm algorithm to predict campus energy consumption, and obtains energy waste positions and reasons according to the prediction of the campus energy consumption;
the historical energy consumption analysis module utilizes the historical energy consumption data stored by the system data platform to fit and acquire energy consumption modes of energy consumption systems in different areas in the campus, so that an energy consumption proportion equation of energy consumption system equipment is obtained, and the energy saving proportion of the energy consumption equipment is visualized;
the automatic supervision module utilizes the real-time energy utilization data stored by the system data platform to automatically control the running state of each energy consumption device and is provided with a reminding function;
and the intelligent early warning module compares the real-time energy consumption data stored by the system data platform with a set safety threshold, and when the real-time energy consumption data exceeds the safety threshold, the intelligent early warning module cuts off the power supply of the corresponding energy consumption equipment and sends early warning information.
10. The campus energy efficiency and electrical safety management system based on artificial intelligence technology as claimed in claim 1, wherein said front end data collection module collects front end real-time energy consumption data and environmental information through infrared sensor, multifunctional electric quantity monitoring instrument, line temperature probe and GIS technology system, specifically:
acquiring an indoor infrared image by using an infrared sensor, converting the indoor infrared image into local temperature data of each indoor area, acquiring indoor and outdoor environment temperature and counting the number of people in the room environment;
collecting current, voltage, leakage current, power and power factor of indoor electric equipment by using a multifunctional electric quantity monitoring instrument;
collecting the temperature of a circuit wire by using a circuit temperature probe;
and acquiring meteorological data in the campus boundary by using a GIS (geographic information system) technical system.
CN202111567123.XA 2021-12-20 2021-12-20 Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology Pending CN114239972A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111567123.XA CN114239972A (en) 2021-12-20 2021-12-20 Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111567123.XA CN114239972A (en) 2021-12-20 2021-12-20 Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology

Publications (1)

Publication Number Publication Date
CN114239972A true CN114239972A (en) 2022-03-25

Family

ID=80759811

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111567123.XA Pending CN114239972A (en) 2021-12-20 2021-12-20 Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology

Country Status (1)

Country Link
CN (1) CN114239972A (en)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114897241A (en) * 2022-05-10 2022-08-12 南京英诺森软件科技有限公司 Intelligent building energy efficiency supervision and prediction method based on digital twins
CN116523276A (en) * 2023-07-04 2023-08-01 天津市蓟州区民力新能源科技有限公司 High-efficiency energy utilization management platform based on intelligent control system
CN116562739A (en) * 2023-07-12 2023-08-08 江苏泓鑫科技有限公司 Liquid chemical engineering wharf operation flow planning and dynamic monitoring system
CN116579506A (en) * 2023-07-13 2023-08-11 陕西通信规划设计研究院有限公司 Building energy consumption data intelligent management method and system based on big data
ES2949156A1 (en) * 2023-03-23 2023-09-26 Gonzalez Alejandro Antonio Diaz SYSTEM AND PROCEDURE FOR COMPREHENSIVE ENERGY MANAGEMENT (Machine-translation by Google Translate, not legally binding)
CN117077899A (en) * 2023-10-16 2023-11-17 四川邕合科技有限公司 Intelligent park energy consumption anomaly monitoring and analyzing method, system, terminal and medium
CN117590763A (en) * 2024-01-18 2024-02-23 中网华信科技股份有限公司 Intelligent park energy data management and control system

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106524295A (en) * 2016-11-21 2017-03-22 北京建筑技术发展有限责任公司 Regional building energy consumption predicting method
CN106874581A (en) * 2016-12-30 2017-06-20 浙江大学 A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model
CN107942960A (en) * 2017-10-23 2018-04-20 中国科学院地理科学与资源研究所 A kind of intelligentized information processing system
CN109146121A (en) * 2018-06-25 2019-01-04 华北电力大学 The power predicating method stopped in the case of limited production based on PSO-BP model
CN110276393A (en) * 2019-06-19 2019-09-24 西安建筑科技大学 A kind of compound prediction technique of green building energy consumption
CN110544180A (en) * 2019-08-22 2019-12-06 万洲电气股份有限公司 Building energy-saving system based on energy consumption prediction and analysis diagnosis
CN111200298A (en) * 2020-01-13 2020-05-26 国网内蒙古东部电力有限公司 Energy storage power supply capacity optimization method based on wind power prediction error estimation
CN111310257A (en) * 2019-12-27 2020-06-19 任惠 Regional building energy consumption prediction method under BIM environment
CN112365030A (en) * 2020-10-21 2021-02-12 深圳市紫衡技术有限公司 Building energy consumption management method and system, electronic equipment and computer storage medium
CN112987617A (en) * 2021-03-15 2021-06-18 国网电力科学研究院武汉能效测评有限公司 Near-zero energy consumption building digital management system and energy efficiency monitoring method

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106524295A (en) * 2016-11-21 2017-03-22 北京建筑技术发展有限责任公司 Regional building energy consumption predicting method
CN106874581A (en) * 2016-12-30 2017-06-20 浙江大学 A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model
CN107942960A (en) * 2017-10-23 2018-04-20 中国科学院地理科学与资源研究所 A kind of intelligentized information processing system
CN109146121A (en) * 2018-06-25 2019-01-04 华北电力大学 The power predicating method stopped in the case of limited production based on PSO-BP model
CN110276393A (en) * 2019-06-19 2019-09-24 西安建筑科技大学 A kind of compound prediction technique of green building energy consumption
CN110544180A (en) * 2019-08-22 2019-12-06 万洲电气股份有限公司 Building energy-saving system based on energy consumption prediction and analysis diagnosis
CN111310257A (en) * 2019-12-27 2020-06-19 任惠 Regional building energy consumption prediction method under BIM environment
CN111200298A (en) * 2020-01-13 2020-05-26 国网内蒙古东部电力有限公司 Energy storage power supply capacity optimization method based on wind power prediction error estimation
CN112365030A (en) * 2020-10-21 2021-02-12 深圳市紫衡技术有限公司 Building energy consumption management method and system, electronic equipment and computer storage medium
CN112987617A (en) * 2021-03-15 2021-06-18 国网电力科学研究院武汉能效测评有限公司 Near-zero energy consumption building digital management system and energy efficiency monitoring method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘博等: "基于云服务的智慧医院能源效率管理系统的研究", 《计算机应用与软件》 *
朱成名 等: "基于PSO-BP神经网络的风电功率备用容量估计模型", 《电子科技》 *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114897241A (en) * 2022-05-10 2022-08-12 南京英诺森软件科技有限公司 Intelligent building energy efficiency supervision and prediction method based on digital twins
ES2949156A1 (en) * 2023-03-23 2023-09-26 Gonzalez Alejandro Antonio Diaz SYSTEM AND PROCEDURE FOR COMPREHENSIVE ENERGY MANAGEMENT (Machine-translation by Google Translate, not legally binding)
CN116523276A (en) * 2023-07-04 2023-08-01 天津市蓟州区民力新能源科技有限公司 High-efficiency energy utilization management platform based on intelligent control system
CN116523276B (en) * 2023-07-04 2023-09-08 天津市蓟州区民力新能源科技有限公司 High-efficiency energy utilization management platform based on intelligent control system
CN116562739A (en) * 2023-07-12 2023-08-08 江苏泓鑫科技有限公司 Liquid chemical engineering wharf operation flow planning and dynamic monitoring system
CN116562739B (en) * 2023-07-12 2023-09-22 江苏泓鑫科技有限公司 Liquid chemical engineering wharf operation flow planning and dynamic monitoring system
CN116579506A (en) * 2023-07-13 2023-08-11 陕西通信规划设计研究院有限公司 Building energy consumption data intelligent management method and system based on big data
CN116579506B (en) * 2023-07-13 2023-09-19 陕西通信规划设计研究院有限公司 Building energy consumption data intelligent management method and system based on big data
CN117077899A (en) * 2023-10-16 2023-11-17 四川邕合科技有限公司 Intelligent park energy consumption anomaly monitoring and analyzing method, system, terminal and medium
CN117077899B (en) * 2023-10-16 2023-12-22 四川邕合科技有限公司 Intelligent park energy consumption anomaly monitoring and analyzing method, system, terminal and medium
CN117590763A (en) * 2024-01-18 2024-02-23 中网华信科技股份有限公司 Intelligent park energy data management and control system
CN117590763B (en) * 2024-01-18 2024-03-19 中网华信科技股份有限公司 Intelligent park energy data management and control system

Similar Documents

Publication Publication Date Title
CN114239972A (en) Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology
CN107942960B (en) A kind of intelligentized information processing system
CN110864414B (en) Air conditioner power utilization load intelligent control scheduling method based on big data analysis
CN107862099A (en) A kind of intelligent public energy source regulator control system
CN105956752A (en) Energy efficiency evaluation and diagnosis analysis method used for green public building operation phase
CN112947158B (en) Building intelligent electrical control system
CN105320184A (en) Intelligent monitoring system of indoor environment of building
CN111638692B (en) Intelligent park management system
Wang et al. A review of load forecasting of the distributed energy system
CN114258178B (en) Intelligent illumination remote on-line monitoring, regulating and controlling management system for solar street lamp
CN211667970U (en) Power supply diagnosis and alarm device based on air conditioning energy control of communication machine room
CN112307675B (en) Neural network-based temperature-sensitive load separation identification method and system
CN110895018A (en) Power supply diagnosis and alarm method and device based on air conditioning energy control of communication machine room
CN115906396A (en) Passive building energy management system and use method thereof
CN116295645A (en) Building energy consumption monitoring system and control method thereof
CN110533264A (en) Building performance evaluating method
CN116027687A (en) Intelligent energy consumption building energy consumption monitoring system and energy saving effect monitoring method
CN116241958A (en) Building wisdom energy consumption management system based on BIM technique
CN111178768A (en) Smart energy management system based on cloud platform
Yu et al. Research on Energy Consumption and Environmental Parameters Evaluation of Nearly Zero Energy Building Based on LoRa IOT
TWI536282B (en) Campus energy conservation neural network decision spport system and method thereof
CN112383583A (en) Automatic energy-saving control platform based on cloud computing service
Yu et al. Application of Internet of Things Technology in Building Energy Consumption Intelligent Monitoring and Prediction System
CN211123744U (en) High and large space intelligent environment ventilation control system
CN212842111U (en) Energy consumption adjustment system for central air conditioner

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination