CN111126778A - Hotel energy consumption evaluation model construction method based on analytic hierarchy process - Google Patents
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Abstract
The invention discloses a hotel energy consumption evaluation model construction method based on an analytic hierarchy process, and belongs to the technical field of electric power. The existing scheme is not scientific and reasonable in evaluation grading scheme, and cannot accurately evaluate the energy consumption of a hotel, so that an effective energy-saving scheme cannot be provided for the hotel with high energy consumption. According to the method, the influence factors of the hotel energy consumption are determined by utilizing the Pearson correlation coefficient, and various influence factors influencing the hotel energy consumption are fully considered; and then establishing a hotel energy consumption evaluation index model by using an analytic hierarchy process, and performing qualitative and quantitative combined, multi-target and hierarchical analysis on the hotel energy consumption. The invention can accurately evaluate the energy consumption of the hotel, and the evaluation scheme is scientific and reasonable, so that an effective energy-saving scheme can be provided for the hotel with high energy consumption.
Description
Technical Field
The invention relates to a hotel energy consumption evaluation model construction method based on an analytic hierarchy process, and belongs to the technical field of electric power.
Background
Chinese patent (publication No. CN 107392481A) discloses a hotel energy consumption calculation method, device and system, which establishes various hotel room models through terminal execution; processing whether the hotel room corresponding to the pre-acquired hotel room model is rental state data and whether the acquired data of people in the hotel room acquired by the sensor module are acquired, and acquiring the people in-room rate data of the hotel room; and then according to the data input by the user, modifying and setting the hotel room model: based on a preset calculation rule, modifying a set hotel room model and the human room rate data of the hotel room, and obtaining the total load of the hotel room corresponding to the hotel room model; and obtaining the total energy consumption of the hotel rooms corresponding to the hotel room model based on the prestored equipment model and the total load of the hotel rooms, so that the terminal can obtain accurate human room rate data of the hotel rooms, and the accuracy of the total energy consumption of the hotel rooms is improved.
However, the above scheme simply estimates the energy consumption of the hotel through the electricity load of the guest room, does not consider many factors such as the scale, the geographical position, the climate and the like of each hotel, cannot perform deeper influence factor identification and evaluation according to the actual situation, is not scientific and reasonable in evaluation grading scheme, cannot accurately evaluate the energy consumption of the hotel, and cannot provide an effective energy-saving scheme for the hotel with high energy consumption.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an influence factor for determining hotel energy consumption by utilizing a Pearson correlation coefficient, and various influence factors influencing the hotel energy consumption are fully considered; then, establishing a hotel energy consumption evaluation index model by using an analytic hierarchy process, and carrying out qualitative and quantitative combined, multi-target and hierarchical analysis on the hotel energy consumption; the hotel energy consumption evaluation model construction method based on the analytic hierarchy process can accurately evaluate the hotel energy consumption, and the evaluation scheme is scientific and reasonable.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a hotel energy consumption evaluation model construction method based on an analytic hierarchy process,
the method comprises the following steps:
first, data acquisition
Acquiring data information of the hotel, wherein the data information comprises hotel management data and hotel electric quantity data;
second, data processing
The data sources for hotel energy consumption analysis are diversified, and besides structural data stored in a database, comment text data also exist, so that the data needs to be processed, and the main contents include data cleaning, data integration, data transformation and data protocols;
third, model construction
Hotels generally use intuitive unit cost indexes for comparison, and the evaluation standard uses three indexes, namely, the ratio of total energy cost to building area, the ratio of total energy cost to room ratio and the ratio of total energy cost to total business income;
the method comprises the following steps of selecting five indexes of unit business income power consumption, per-capita power consumption, unit area power consumption, unit guest room power consumption and unit business expense power consumption as factors influencing an energy consumption evaluation index by combining experience in the hotel industry and suggestions of experts in the aspects of electric power and energy;
therefore, the energy consumption evaluation index formula is as follows:
the energy consumption evaluation index is A, unit business income and electricity consumption score + B, average person electricity consumption score + C, unit area electricity consumption + D, unit guest room electricity consumption score + E, unit business expense and electricity consumption score;
A. b, C, D, E are correlation coefficient values;
the method specifically comprises the following steps:
s1, the Pearson correlation coefficient can reflect the correlation between the index and the influence factor; using Pearson linear correlation coefficient, an index having a correlated influence on the electric quantity: building area, the total number of the hotel, business income, business expense and the number of rooms for living in are subjected to correlation analysis one by one;
s2, carrying out related setting on the weight by using an analytic hierarchy process; taking the energy consumption evaluation index as a target layer, and taking the unit business income power consumption, the per-person power consumption, the unit guest room power consumption and the unit business expense power consumption as a scheme layer;
the analytic hierarchy process takes a complex problem as a general target, takes a problem solution as a plurality of different factors, and forms a hierarchical structure model through the mutual influence among the factors and the combination of all layers; the analytic hierarchy process converts the problem into the relative weight of the bottom layer to the factor of the previous layer, and then the weight of the total target is obtained through weighting and summing;
s3, assigning the bin division interval
The energy consumption of the hotel is greatly influenced by seasons, and the model is divided into three seasons, namely spring and autumn, winter and summer, and is used for respectively carrying out value assignment on each section of unit business income power consumption, per-person power consumption, unit guest room power consumption and unit business expense power consumption; dividing the score values into 25, 50, 75 and 100, and calculating through SPSS to obtain range values whether the three seasons correspond to each other;
the fourth step, model output
Establishing an energy consumption evaluation model of the hotel by the analytic hierarchy process, and analyzing the energy consumption of the specific hotel by the model;
and further provides an energy-saving scheme for hotels with high energy consumption.
According to the method, the influence factors of the hotel energy consumption are determined by utilizing the Pearson correlation coefficient, and various influence factors influencing the hotel energy consumption are fully considered; and then establishing a hotel energy consumption evaluation index model by using an analytic hierarchy process, and performing qualitative and quantitative combined, multi-target and hierarchical analysis on the hotel energy consumption. The invention can accurately evaluate the energy consumption of the hotel, and the evaluation scheme is scientific and reasonable, so that an effective energy-saving scheme can be provided for the hotel with high energy consumption.
As a preferable technical measure:
in the first step, the hotel management data includes: building area, total number of hotels, business income, business expenses and number of rooms in which to live;
the hotel electric quantity data comprises: household number, total electric quantity, peak valley electric quantity, electricity price type, active power, reactive power, power factor and the like.
As a preferable technical measure:
in the second step, the first step is carried out,
(1) data cleaning: the method mainly comprises the steps of deleting irrelevant data and repeated data in collected hotel original data sets, smoothing noise data, screening data irrelevant to mining topics, and processing missing values and abnormal values; the specific common methods include deletion method, substitution method and interpolation method;
(2) data integration: integrating the collected distributed heterogeneous hotel data sources which are associated with each other, so that a user can access the data sources in a transparent mode; the specific integration method comprises a mode integration method, a data replication method and a comprehensive integration method;
(3) data transformation: a process of changing collected hotel data from one representation form to another representation form; commonly used transformation methods are logarithmic transformation, square root transformation and reciprocal transformation;
(4) data specification: on the basis of keeping the integrity of the data set as much as possible, the collected hotel data is subjected to protocol processing, so that the data set mining is more effective; the method mainly adopts modes of a characteristic specification, a sample specification, a characteristic value specification and the like.
As a preferable technical measure:
in the third step, the first step is carried out,
and (3) constructing a pair comparison matrix, namely a positive and reciprocal matrix:
after comparing every two factors, then scheduling the relative quality sequence of each evaluation factor according to the 9-division ratio, and sequentially constructing a judgment matrix of the evaluation factors; calculating the weight of each alternative factor by using a geometric average method or a canonical column average method according to a certain standard;
weighting each factor:
if the importance of the two factors is between the two evaluation grades, taking a middle score, and if the factor i has a score value relative to the factor j, making the score of the factor j relative to the factor i into the reciprocal of the factor j; the relative importance of the factors is determined according to the comparison of two factors at the same level, if the influence importance of the two factors is considered to be equivalent, the value is 1, if the influence importance of the two factors is slightly more important than the influence importance of the two factors is 3, if the influence importance of the two factors is slightly less important than the influence importance of the two factors, the value is 1/3, and similarly, if the influence importance of the two factors is considered to be more important than the influence importance of the two factors, the value is 5, if the influence of.
As a preferable technical measure:
the geometric mean method comprises the following calculation steps:
a) calculating the product of each factor mi of each row of the judgment matrix A;
b) calculating the n-th square root of mi;
c) carrying out normalization processing on the vector;
d) the vector is the weight vector being determined.
The calculation steps of the canonical column average method are as follows:
a) calculating the sum of each factor mi of each row of the judgment matrix A;
b) normalizing the sum of the factors of each row of A;
c) the vector is the weight vector being determined.
As a preferable technical measure:
after a judgment matrix is constructed, calculating the relative weight of each factor aiming at a certain rule layer according to the judgment matrix, and carrying out consistency check;
although the judgment matrix A is not required to be consistent when constructed, the judgment deviation is not allowed to be too large, so that the consistency check needs to be carried out on the judgment matrix A;
calculating the maximum characteristic root and the corresponding characteristic vector of each pair comparison array, and performing consistency check by using consistency factors, random consistency factors and consistency ratios;
if the verification is passed, the feature vector is a weight vector; if not, the comparison matrix needs to be reconstructed;
for the difference in cognition, the matrix judgment may not have consistency, and a "random consistency ratio" may be used to check that CR is CI/RI, where CI represents a consistency factor and RI represents an average random consistency factor;
when CR is less than 0.10, the single-level ordering is effective; when CR > - [ 0.10 ], the difference is too large and is not effective.
As a preferable technical measure:
in the fourth step, the first step is carried out,
the energy-saving scheme comprises the following steps: the peak-valley electricity price is reasonably utilized, the new energy is reasonably utilized, and the shallow geothermal energy is reasonably utilized.
As a preferable technical measure: the scheme of reasonably utilizing peak-to-valley electricity is an ice storage technology, and energy is stored by utilizing the valley period electricity of a power grid through ice making during the night valley period; and at the peak time of the day, the stored ice blocks and the refrigerating unit work together to release energy to achieve the refrigerating effect.
As a preferable technical measure: the scheme of rational utilization new forms of energy is for selecting installation photovoltaic, through the spontaneous self-service of photovoltaic, reduces and purchases the electricity from the electric wire netting, and then reduces the charges of electricity expenditure.
As a preferable technical measure: the scheme of reasonably utilizing the shallow geothermal energy is to adopt a ground source heat pump technology, and the ground source heat pump is an air conditioning system capable of supplying cold and heat by utilizing the shallow geothermal energy.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the influence factors of the hotel energy consumption are determined by utilizing the Pearson correlation coefficient, and various influence factors influencing the hotel energy consumption are fully considered; and then establishing a hotel energy consumption evaluation index model by using an analytic hierarchy process, and performing qualitative and quantitative combined, multi-target and hierarchical analysis on the hotel energy consumption. The invention can accurately evaluate the energy consumption of the hotel, and the evaluation scheme is scientific and reasonable, so that an effective energy-saving scheme can be provided for the hotel with high energy consumption.
Drawings
FIG. 1 is a hierarchical diagram of energy consumption index indices in accordance with the present invention;
FIG. 2 is a flow chart of the data processing of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
As shown in fig. 1, a hotel energy consumption evaluation model construction method based on an analytic hierarchy process,
the method comprises the following steps:
first, data acquisition
And acquiring data information of the hotel, wherein the data information comprises hotel management data and hotel electric quantity data.
Second, data processing
The data sources for hotel energy consumption analysis are diversified, and besides structural data stored in a database, comment text data also exist, so that the data needs to be processed, and the main contents include data cleaning, data integration, data transformation and data protocols.
Third, model construction
Hotels generally use intuitive unit cost indexes for comparison, and the evaluation standard uses three indexes, namely, the ratio of total energy cost to building area, the ratio of total energy cost to room ratio, and the ratio of total energy cost to total business income.
The five indexes of unit business income power consumption, per capita power consumption, unit area power consumption, unit guest room power consumption and unit business expense power consumption are selected as factors influencing the energy consumption evaluation index according to the experience in the hotel industry and the suggestions of experts in the aspects of electric power and energy.
Therefore, the energy consumption evaluation index formula is as follows:
the energy consumption evaluation index is a unit revenue power consumption score + B unit average power consumption score + C unit area power consumption + D unit guest room power consumption score + E unit operating cost power consumption score.
A. B, C, D, E are correlation coefficient values.
The method specifically comprises the following steps:
s1, the Pearson correlation coefficient can reflect the correlation between the index and the influence factor. Using Pearson linear correlation coefficient, an index having a correlated influence on the electric quantity: the building area, the total number of the hotel, the business income, the business expense and the number of rooms for living are analyzed one by one to obtain the correlation analysis, which is shown in the following table.
| Influence index | Coefficient of correlation | Strong and weak correlation |
| Area of building | 0.029 | Weak (weak) |
| Number of hotel | 0.460 | High strength |
| Income of business | 0.701 | High strength |
| Business expenses | 0.522 | High strength |
| Number of living rooms | 0.642 | High strength |
TABLE 1 correlation analysis of Pearson correlation coefficients
And finally determining the unit business income power consumption, the per-capita power consumption, the unit guest room power consumption and the unit business expense power consumption as final model input indexes according to the strong or weak Pearson correlation.
And S2, performing related setting on the weight by using an analytic hierarchy process. And taking the energy consumption evaluation index as a target layer, and taking the unit business income power consumption, the per-capita power consumption, the unit guest room power consumption and the unit business expense power consumption as a scheme layer.
The analytic hierarchy process takes a complex problem as a general target, takes a problem solution as a plurality of different factors, and forms a hierarchical structure model through the mutual influence among the factors and the combination of all layers. The analytic hierarchy process converts the problem into the relative weight of the bottom layer to the previous layer factor, and then obtains the weight of the total target through weighted sum.
S3, assigning the bin division interval
The hotel energy consumption is greatly influenced by seasons, and the model is divided into three seasons of spring and autumn, winter and summer, and assigns value to unit business income power consumption, per-capita power consumption, unit guest room power consumption and unit business expense power consumption in each section. The score values are divided into 25, 50, 75 and 100, and whether the range values correspond to the three seasons is obtained through SPSS calculation.
Now take the unit business income and power consumption as an example
The units are shown in the following table, with four months of 6, 7, 8, and 9 as summer, three months of 12, 1, and 2 as winter, and five months of 3, 4, 5, 10, and 11 as spring and autumn: kilowatt-hour/yuan.
TABLE 2 assignment table for unit business income and power consumption division box
And the assignment of each section of power consumption per person, unit guest room power consumption and unit business expense power consumption can be obtained.
The fourth step, model output
An energy consumption evaluation model of the hotel is established through the analytic hierarchy process, and energy consumption analysis is carried out on the specific hotel through the model.
And further provides an energy-saving scheme for hotels with high energy consumption.
According to the method, the influence factors of the hotel energy consumption are determined by utilizing the Pearson correlation coefficient, and various influence factors influencing the hotel energy consumption are fully considered. And then establishing a hotel energy consumption evaluation index model by using an analytic hierarchy process, and performing qualitative and quantitative combined, multi-target and hierarchical analysis on the hotel energy consumption. The invention can accurately evaluate the energy consumption of the hotel, and the evaluation scheme is scientific and reasonable, so that an effective energy-saving scheme can be provided for the hotel with high energy consumption.
The invention discloses a specific embodiment of hotel data, which comprises the following steps:
in the first step, the hotel management data includes: building area, total number of hotels, business income, business expenses and number of rooms in which to live;
the hotel electric quantity data comprises: household number, total electric quantity, peak valley electric quantity, electricity price type, active power, reactive power, power factor and the like.
As shown in fig. 2, a specific embodiment of the data processing of the present invention:
(1) data cleaning: the method mainly comprises the steps of deleting irrelevant data and repeated data in collected hotel original data sets, smoothing noise data, screening data irrelevant to mining topics, and processing missing values and abnormal values; the specific common methods include deletion method, substitution method and interpolation method;
(2) data integration: integrating the collected distributed heterogeneous hotel data sources which are associated with each other, so that a user can access the data sources in a transparent mode; the specific integration method comprises a mode integration method, a data replication method and a comprehensive integration method;
(3) data transformation: a process of changing collected hotel data from one representation form to another representation form; commonly used transformation methods are logarithmic transformation, square root transformation and reciprocal transformation;
(4) data specification: on the basis of keeping the integrity of the data set as much as possible, the collected hotel data is subjected to protocol processing, so that the data set mining is more effective; the method mainly adopts modes of a characteristic specification, a sample specification, a characteristic value specification and the like.
As shown in tables 3-4, the present invention is constructed as one specific example of a comparative array:
and (3) constructing a pair comparison matrix, namely a positive and reciprocal matrix:
after comparing every two factors, then scheduling the relative quality sequence of each evaluation factor according to the 9-division ratio, and sequentially constructing a judgment matrix of the evaluation factors; and calculating the weight of each alternative factor by using a geometric average method or a canonical column average method aiming at a certain standard.
TABLE 3 judgment matrix for each factor
Each dimension index is an evaluation scale of the plan layer, and table 3 is an evaluation scale table as shown in the following table.
TABLE 4-evaluation ruler-chart
Weighting each factor:
if the importance of the two factors is between the two evaluation grades, taking a middle score, and if the factor i has a score value relative to the factor j, making the score of the factor j relative to the factor i into the reciprocal of the factor j; the relative importance of the factors is determined according to the comparison of two factors at the same level, if the influence importance of the two factors is considered to be equivalent, the value is 1, if the influence importance of the two factors is slightly more important than the influence importance of the two factors is 3, if the influence importance of the two factors is slightly less important than the influence importance of the two factors, the value is 1/3, and similarly, if the influence importance of the two factors is considered to be more important than the influence importance of the two factors, the value is 5, if the influence of.
Experts on professional posts in each city are selected for questionnaire survey, and because different respondents have different understandings on the importance of each index and have conditions such as understandings or filling errors, the final judgment matrix is obtained after revising and weighting the questionnaire results, as shown in table 5.
TABLE 5 decision matrix
The specific embodiment of the invention for calculating the weight is as follows:
the geometric mean method comprises the following calculation steps:
a) calculating the product of each factor mi of each row of the judgment matrix A;
b) calculating the n-th square root of mi;
c) carrying out normalization processing on the vector;
d) the vector is the weight vector being determined.
The calculation steps of the canonical column average method are as follows:
a) calculating the sum of each factor mi of each row of the judgment matrix A;
b) normalizing the sum of the factors of each row of A;
c) the vector is the weight vector being determined.
The invention carries out the specific embodiment of the consistency check on the matrix:
after a judgment matrix is constructed, calculating the relative weight of each factor aiming at a certain rule layer according to the judgment matrix, and carrying out consistency check;
although the judgment matrix A is not required to be consistent when constructed, the judgment deviation is not allowed to be too large, so that the consistency check needs to be carried out on the judgment matrix A;
calculating the maximum characteristic root and the corresponding characteristic vector of each pair comparison array, and performing consistency check by using consistency factors, random consistency factors and consistency ratios;
if the verification is passed, the feature vector is a weight vector; if not, the comparison matrix needs to be reconstructed;
for the difference in cognition, the matrix judgment may not have consistency, and a "random consistency ratio" may be used to check that CR is CI/RI, where CI represents a consistency factor and RI represents an average random consistency factor;
when CR is less than 0.10, the single-level ordering is effective; when CR > - [ 0.10 ], the difference is too large and is not effective.
The specific embodiment of the energy-saving scheme of the invention comprises the following steps:
the energy-saving scheme comprises the following steps: the peak-valley electricity price is reasonably utilized, the new energy is reasonably utilized, and the shallow geothermal energy is reasonably utilized.
The invention reasonably utilizes the specific embodiment of peak-to-valley electricity:
the scheme of reasonably utilizing peak-to-valley electricity is an ice storage technology, and energy is stored by utilizing the valley period electricity of a power grid through ice making during the night valley period; and at the peak time of the day, the stored ice blocks and the refrigerating unit work together to release energy to achieve the refrigerating effect.
The power supply voltage is 1-10kV for general industry and commerce, the peak time period power price is 1.2376 yuan/kilowatt hour, the valley time period power price is 0.4276 yuan/kilowatt hour, and the difference between the peak time period power price and the valley time period power price is about 3 times. Therefore, the peak time period power consumption is reasonably avoided, the valley time period power consumption is selected, and the electricity charge expenditure can be effectively reduced. The power output of the cold machine set in the peak time period is reduced by the ice cold storage technology, and the cost can be reduced by 30-50% approximately by utilizing the 3-time electricity price difference in the peak-valley time period. The ice cold storage technology has the advantages of long service life, no pollution, high efficiency and low investment cost, and becomes an energy-saving technology favored by the hotel industry.
The invention reasonably utilizes the specific embodiment of new energy:
the scheme of rational utilization new forms of energy is for selecting installation photovoltaic, through the spontaneous self-service of photovoltaic, reduces and purchases the electricity from the electric wire netting, and then reduces the charges of electricity expenditure. The photovoltaic can be selected to be installed in some hotels with large roof areas, electricity is purchased from the power grid through photovoltaic self-generation, and then electricity charge expenditure is reduced. In recent years, with the issuance of national photovoltaic policies, the cost of photovoltaic power generation has been reduced by one third compared to before. Although the solar energy resource is abundant, due to the intermittent and fluctuating defects, the photovoltaic power generation cannot be used as a main energy supply mode in the hotel industry.
The invention reasonably utilizes the specific embodiment of shallow geothermal energy:
the scheme of reasonably utilizing the shallow geothermal energy is to adopt a ground source heat pump technology, and the ground source heat pump is an air conditioning system capable of supplying cold and heat by utilizing the shallow geothermal energy.
The shallow geothermal energy refers to the heat energy resource in the earth with the temperature lower than 25 ℃ and the development and utilization value under the current technical and economic conditions within a certain depth range (generally constant temperature zone to 200m buried depth). The ground source heat pump has considerable energy-saving effect, and can obtain heat or cold with the energy of 4kWh or more when consuming 1 kWh.
The ground source heat pump has no pollution to the environment, one machine has multiple purposes and wide application range. Most of ground source heat pump equipment is either buried underground or indoors, and moving parts of the ground source heat pump equipment are fewer than those of other systems, so that the system maintenance cost is low, and the service life is long.
Application scenarios of the invention
(1) Monitoring energy consumption data: monitoring data of a hotel air conditioning system, a living water pump, a heating system and the like; and monitoring energy consumption of places such as hotel rooms, kitchens, restaurants, meeting rooms and the like.
(2) Analyzing and calculating energy consumption: monthly electricity, daily electricity, annual electricity and hourly electricity, wherein the hourly electricity needs to use 96 points of electricity.
(3) Energy consumption reports: energy consumption ranking data and energy consumption analysis reports.
(4) After sale of electric power: and (4) informing power transmission stopping information and power transmission planning information.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A hotel energy consumption evaluation model construction method based on an analytic hierarchy process is characterized in that,
the method comprises the following steps:
first, data acquisition
Acquiring data information of the hotel, wherein the data information comprises hotel management data and hotel electric quantity data;
second, data processing
The data sources for hotel energy consumption analysis are diversified, and besides structural data stored in a database, comment text data also exist, so that the data needs to be processed, and the main contents include data cleaning, data integration, data transformation and data protocols;
third, model construction
Hotels generally use intuitive unit cost indexes for comparison, and the evaluation standard uses three indexes, namely, the ratio of total energy cost to building area, the ratio of total energy cost to room ratio and the ratio of total energy cost to total business income;
the method comprises the following steps of selecting five indexes of unit business income power consumption, per-capita power consumption, unit area power consumption, unit guest room power consumption and unit business expense power consumption as factors influencing an energy consumption evaluation index by combining experience in the hotel industry and suggestions of experts in the aspects of electric power and energy;
therefore, the energy consumption evaluation index formula is as follows:
energy consumption evaluation index = a + B + per-person electricity consumption score + C + per-area electricity consumption + D + per-guest room electricity consumption score + E + per business charge electricity consumption score;
A. b, C, D, E are correlation coefficient values;
the method specifically comprises the following steps:
s1, the Pearson correlation coefficient can reflect the correlation between the index and the influence factor; using Pearson linear correlation coefficient to perform correlation analysis on indexes having correlation influence on electric quantity, such as building area, total number of people in the hotel, business income, business cost and number of rooms in which people live;
s2, carrying out related setting on the weight by using an analytic hierarchy process; taking the energy consumption evaluation index as a target layer, and taking the unit business income power consumption, the per-person power consumption, the unit guest room power consumption and the unit business expense power consumption as a scheme layer;
the analytic hierarchy process takes a complex problem as a general target, takes a problem solution as a plurality of different factors, and forms a hierarchical structure model through the mutual influence among the factors and the combination of all layers; the analytic hierarchy process converts the problem into the relative weight of the bottom layer to the factor of the previous layer, and then the weight of the total target is obtained through weighting and summing;
s3, assigning the bin division interval
The energy consumption of the hotel is greatly influenced by seasons, and the model is divided into three seasons, namely spring and autumn, winter and summer, and is used for respectively carrying out value assignment on each section of unit business income power consumption, per-person power consumption, unit guest room power consumption and unit business expense power consumption; dividing the score values into 25, 50, 75 and 100, and calculating through SPSS to obtain range values whether the three seasons correspond to each other;
the fourth step, model output
Establishing an energy consumption evaluation model of the hotel by the analytic hierarchy process, and analyzing the energy consumption of the specific hotel by the model;
and further provides an energy-saving scheme for hotels with high energy consumption.
2. The modeling method of hotel energy consumption evaluation based on analytic hierarchy process as claimed in claim 1,
in the first step, the hotel management data includes: building area, total number of hotels, business income, business expenses and number of rooms in which to live;
the hotel electric quantity data comprises: household number, total electric quantity, peak valley electric quantity, electricity price type, active power, reactive power and power factor.
3. The modeling method of hotel energy consumption evaluation based on analytic hierarchy process as claimed in claim 1,
in the second step, the first step is carried out,
(1) data cleaning: the method mainly comprises the steps of deleting irrelevant data and repeated data in collected hotel original data sets, smoothing noise data, screening data irrelevant to mining topics, and processing missing values and abnormal values; the specific common methods include deletion method, substitution method and interpolation method;
(2) data integration: integrating the collected distributed heterogeneous hotel data sources which are associated with each other, so that a user can access the data sources in a transparent mode; the specific integration method comprises a mode integration method, a data replication method and a comprehensive integration method;
(3) data transformation: a process of changing collected hotel data from one representation form to another representation form; commonly used transformation methods are logarithmic transformation, square root transformation and reciprocal transformation;
(4) data specification: on the basis of keeping the integrity of the data set as much as possible, the collected hotel data is subjected to protocol processing, so that the data set mining is more effective; the method mainly adopts modes of a characteristic specification, a sample specification, a characteristic value specification and the like.
4. The method as claimed in claim 1, wherein the model for hotel energy consumption evaluation is constructed based on an analytic hierarchy process,
it is characterized in that the preparation method is characterized in that,
in the third step, the first step is carried out,
and (3) constructing a pair comparison matrix, namely a positive and reciprocal matrix:
after comparing every two factors, then scheduling the relative quality sequence of each evaluation factor according to the 9-division ratio, and sequentially constructing a judgment matrix of the evaluation factors; calculating the weight of each alternative factor by using a geometric average method or a canonical column average method according to a certain standard;
weighting each factor:
if the importance of the two factors is between the two evaluation grades, taking a middle score, and if the factor i has a score value relative to the factor j, making the score of the factor j relative to the factor i into the reciprocal of the factor j; the relative importance of the factors is determined according to the comparison of two factors at the same level, if the influence importance of the two factors is considered to be equivalent, the value is 1, if the influence importance of the two factors is slightly more important than the influence importance of the two factors is 3, if the influence importance of the two factors is slightly less important than the influence importance of the two factors, the value is 1/3, and similarly, if the influence importance of the two factors is considered to be more important than the influence importance of the two factors, the value is 5, if the influence of.
5. The method as claimed in claim 4, wherein the model for evaluating energy consumption of hotel is based on analytic hierarchy process,
it is characterized in that the preparation method is characterized in that,
the geometric mean method comprises the following calculation steps:
a) calculating the product of each factor mi of each row of the judgment matrix A;
b) calculating the n-th square root of mi;
c) carrying out normalization processing on the vector;
d) the vector is the weight vector;
the calculation steps of the canonical column average method are as follows:
a) calculating the sum of each factor mi of each row of the judgment matrix A;
b) normalizing the sum of the factors of each row of A;
c) the vector is the weight vector being determined.
6. The method as claimed in claim 5, wherein the model for hotel energy consumption evaluation is constructed based on an analytic hierarchy process,
it is characterized in that the preparation method is characterized in that,
after a judgment matrix is constructed, calculating the relative weight of each factor aiming at a certain rule layer according to the judgment matrix, and carrying out consistency check;
although the judgment matrix A is not required to be consistent when constructed, the judgment deviation is not allowed to be too large, so that the consistency check needs to be carried out on the judgment matrix A;
calculating the maximum characteristic root and the corresponding characteristic vector of each pair comparison array, and performing consistency check by using consistency factors, random consistency factors and consistency ratios;
if the verification is passed, the feature vector is a weight vector; if not, the comparison matrix needs to be reconstructed;
for the difference in cognition, the matrix judgment may not have consistency, and CR = CI/RI may be checked with a "random consistency ratio", where CI represents a consistency factor and RI represents an average random consistency factor;
when CR < =0.10, the hierarchy single ordering is effective; when CR > =0.10, the difference is too large and is not effective.
7. The hotel energy consumption evaluation model construction method based on the analytic hierarchy process as in any one of claims 1 to 6,
in the fourth step, the first step is carried out,
the energy-saving scheme comprises the following steps: the peak-valley electricity price is reasonably utilized, the new energy is reasonably utilized, and the shallow geothermal energy is reasonably utilized.
8. The modeling method of hotel energy consumption evaluation based on analytic hierarchy process as claimed in claim 7, wherein the scheme of reasonably utilizing peak-to-valley electricity is ice storage technology, which stores energy by making ice by utilizing valley-period electricity of the power grid during the night valley period; and at the peak time of the day, the stored ice blocks and the refrigerating unit work together to release energy to achieve the refrigerating effect.
9. The hotel energy consumption evaluation model building method based on the analytic hierarchy process as claimed in claim 8, wherein the scheme of reasonably utilizing new energy is to select photovoltaic installation, and electricity purchasing from a power grid is reduced through photovoltaic spontaneous self-use, so that electricity charge expenditure is reduced.
10. The analytic hierarchy process-based hotel energy consumption evaluation model building method as claimed in claim 9, wherein the reasonable utilization of shallow geothermal energy is to adopt a ground source heat pump technology, and the ground source heat pump is an air conditioning system capable of supplying cold and heat by using shallow geothermal energy.
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