CN110796307A - Distributed load prediction method and system for comprehensive energy system - Google Patents
Distributed load prediction method and system for comprehensive energy system Download PDFInfo
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Abstract
The invention discloses a distributed load prediction method and a distributed load prediction system for a comprehensive energy system. The method comprises the following steps: acquiring an electric load, a thermal load and an air load of the comprehensive energy system, an electric load time sequence curve, a thermal load time sequence curve and an air load time sequence curve, and acquiring external factor data; calculating a load characteristic index according to the electric load, the thermal load and the air load; carrying out load clustering according to the load characteristic index, the electric load time sequence curve, the thermal load time sequence curve and the air load time sequence curve; establishing an offline load prediction model for each type of load according to external factor data; performing online load prediction by adopting an offline load prediction model according to the current daily load data and the external factor data of the day to be predicted to obtain the daily load to be predicted; and summing the daily loads to be predicted of each type of load to obtain the total load of the daily comprehensive energy system to be predicted. By adopting the method and the system, the load prediction precision of the comprehensive energy system can be improved.
Description
Technical Field
The invention relates to the technical field of load prediction, in particular to a distributed load prediction method and system of a comprehensive energy system.
Background
In the face of the challenges of resource shortage, environmental pollution and the like, the comprehensive energy system becomes an important energy utilization mode in the energy transformation process, and has obvious effects of improving the energy utilization efficiency and reducing pollutant emission. Compared with the traditional power system, the thermodynamic system and the natural gas system, the comprehensive energy system is different in independent planning, independent design and independent operation, integrates energy supply, energy conversion and energy storage equipment in various forms, realizes coupling of different types of energy in different links such as source, network, load and the like, enables the connection among the power system, the thermal system and the gas system to be tighter, and the comprehensive energy load prediction can become an important part in the economic dispatching and optimized operation of the comprehensive energy system.
Most of the existing load prediction methods are respectively predicting electric, thermal and gas loads, and the mutual influence among different types of loads cannot be considered cooperatively. Meanwhile, along with the popularization of terminal equipment such as an intelligent electric meter and the like, on one hand, the electricity utilization data of a user is continuously updated along with time, rapidly increases and is in a massive situation; on the other hand, the data acquisition points are distributed on the user side, and the data acquisition points have extremely strong dispersibility. The existing prediction method is difficult to process large-scale distributed data. Therefore, intensive research needs to be carried out on a data-driven distributed load prediction method of the comprehensive energy system, massive distributed data information is fully utilized, and the electricity-heat-gas load coupling characteristics are analyzed to realize high-precision prediction of the load of the comprehensive energy system.
Disclosure of Invention
The invention aims to provide a distributed load forecasting method and a distributed load forecasting system for a comprehensive energy system, which consider the mutual influence among different types of loads, analyze the electricity-heat-gas load coupling characteristics by utilizing distributed data information and improve the load forecasting precision of the comprehensive energy system.
In order to achieve the purpose, the invention provides the following scheme:
a distributed load prediction method for an integrated energy system comprises the following steps:
acquiring an electric load and an air load of the comprehensive energy system, acquiring an electric load time sequence curve, a heat load time sequence curve and an air load time sequence curve, and acquiring external factor data; the external factor data comprises electricity price, gas price and meteorological condition data;
calculating a load characteristic index from the electrical load, the thermal load, and the air load;
carrying out load clustering according to the load characteristic index, the electric load time sequence curve, the heat load time sequence curve and the gas load time sequence curve to obtain various loads;
establishing an offline load prediction model for each type of load according to the external factor data;
acquiring load data of the current day and external factor data of a day to be predicted aiming at each type of load, and performing online load prediction by adopting the offline load prediction model according to the load data of the current day and the external factor data of the day to be predicted to obtain a day load to be predicted;
and summing the daily loads to be predicted of each type of load to obtain the total load of the daily comprehensive energy system to be predicted.
Optionally, before calculating the load characteristic index according to the electrical load, the thermal load and the air load, the method further includes:
correcting deviation data of the electrical load, the thermal load and the gas load by adopting a data curve fitting method;
and performing normalization processing on the corrected electric load, the corrected heat load and the corrected gas load to obtain the normalized electric load, the normalized heat load and the normalized gas load.
Optionally, the calculating a load characteristic index according to the electrical load, the thermal load, and the air load specifically includes:
the daily average load is calculated according to the following formula:
the daily load rate is calculated according to the following formula:
the peak energy consumption rate was calculated according to the following formula:
the valley time energy consumption rate is calculated according to the following formula:
in the formula, KavDenotes the daily average load, EallExpressing daily energy, wherein the daily energy is one of daily electricity consumption, daily heat and daily gas, T represents the number of daily time segments, T is 24, KdRepresenting the daily load rate, PavDenotes the daily average load, PmaxRepresents the daily maximum load, ρpRepresents the peak specific energy consumption, EpRepresenting energy used during peak hours, pvIndicating the energy consumption rate at valley time, EvIndicating energy usage during the daily trough period.
Optionally, the load clustering is performed according to the load characteristic index, the electrical load time sequence curve, the thermal load time sequence curve, and the gas load time sequence curve to obtain multiple types of loads, and the method specifically includes:
load clustering was performed according to the following formula:
wherein F represents a clustering objective function, K represents the number of clusters, M represents the number of load time sequence curves, and hk,mRepresenting an identification variable, h when the load curve m belongs to the load class kk,mThe value is 1, when the load curve m does not belong to the load class k, hk,mWith a value of 0, α represents a weighting factor,
representing the load characteristic distance between the load curve m and the load class k,representing the load time-series curve distance, λ, between the load curve m and the load class k1Denotes a first coefficient, λ2Denotes a second coefficient, λ3Denotes a third coefficient, λ4Represents a fourth coefficient and satisfies λ1+λ2+λ3+λ4＝1；Kav,mDenotes the daily average load factor, K, of the load curve mav,kDenotes the daily average load factor, K, of the load class Kd,mRepresenting the daily load factor, K, of the load curve md,kDenotes the daily load rate, ρ, of the load class kp,mRepresents the peak-time energy consumption rate, ρ, of the load curve mp,kRepresents the peak specific energy consumption, ρ, of the load class kv,mThe energy consumption rate at valley time, ρ, expressed as the load curve mv,kRepresenting the valley time energy consumption rate of the load curve k; pm,tLoad value of load curve m to be clustered in t period，Pk,tAnd representing the load value conforming to the class k to be clustered in the period t.
Optionally, the establishing an offline load prediction model for each type of load according to the external factor data specifically includes:
acquiring one type of data in the external factor data; the data of one type is one of electricity price, gas price and meteorological condition data;
calculating the correlation coefficient of the class data and each class of load;
comparing the correlation coefficient with a preset threshold value; if the correlation coefficient is larger than the preset threshold value, determining the class of data as a correlation factor corresponding to the load type; otherwise, determining whether other types of data in the external factor data are correlation factors corresponding to the load types;
and establishing an offline load prediction model according to the correlation factors and the load data corresponding to the correlation factors.
Optionally, before performing online load prediction, the method further includes:
acquiring a current daily prediction error of each type of load, a next-day predicted daily load of each type of load and correlation factors corresponding to the load types;
and establishing a load prediction error model according to the prediction error of the current day, the next day prediction day load and the correlation factor corresponding to the load type.
Optionally, the obtaining of load data of a current day and external factor data of a day to be predicted for each type of load, and performing online load prediction by using the offline load prediction model according to the load data of the current day and the external factor data of the day to be predicted to obtain a day load to be predicted specifically includes:
generating a comprehensive energy system load prediction model according to the offline load prediction model and the load prediction error model;
and acquiring a current day prediction error, current day load data and external factor data of a day to be predicted aiming at each type of load, and performing online load prediction by adopting the comprehensive energy system load prediction model according to the current day prediction error, the current day load data and the external factor data of the day to be predicted to obtain the daily load to be predicted.
The invention also provides a distributed load prediction system of the comprehensive energy system, which comprises the following components:
the data acquisition module is used for acquiring the electric load, the heat load and the air load of the comprehensive energy system, acquiring an electric load time sequence curve, a heat load time sequence curve and an air load time sequence curve, and acquiring external factor data; the external factor data comprises electricity price, gas price and meteorological condition data;
a load characteristic index calculation module for calculating a load characteristic index from the electrical load, the thermal load, and the air load;
the load clustering module is used for carrying out load clustering according to the load characteristic index, the electric load time sequence curve, the heat load time sequence curve and the gas load time sequence curve to obtain various loads;
the offline load prediction model establishing module is used for establishing an offline load prediction model for each type of load according to the external factor data;
the online load prediction module is used for acquiring load data of the current day and external factor data of a day to be predicted aiming at each type of load, and performing online load prediction by adopting the offline load prediction model according to the load data of the current day and the external factor data of the day to be predicted to obtain a daily load to be predicted;
and the comprehensive energy system total load calculation module is used for summing the daily loads to be predicted of each type of load to obtain the daily comprehensive energy system total load to be predicted.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a distributed load prediction method and a distributed load prediction system for a comprehensive energy system, which consider external factor data of electricity price, gas price and meteorological condition data; calculating a load characteristic index according to the electric load, the thermal load and the air load; carrying out load clustering according to the load characteristic index, the electric load time sequence curve, the thermal load time sequence curve and the air load time sequence curve; establishing an offline load prediction model for each type of load according to external factor data, and performing online load prediction according to the prediction model to obtain a daily load to be predicted; and finally, summing the daily loads to be predicted of each type of load to obtain the total load of the daily comprehensive energy system to be predicted, considering the mutual influence among different types of loads, analyzing the electricity-heat-gas load coupling characteristics by using distributed data information, and improving the load prediction precision of the comprehensive energy system.
In addition, deviation data correction is carried out on the electric load, the heat load and the gas load by adopting a data curve fitting method, and normalization processing is carried out on the corrected electric load, the corrected heat load and the corrected gas load, so that the load prediction precision of the comprehensive energy system is further improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a distributed load forecasting method of an integrated energy system according to an embodiment of the present invention;
fig. 2 is a structural diagram of a distributed load prediction system of the integrated energy system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a distributed load forecasting method and a distributed load forecasting system for a comprehensive energy system, which consider the mutual influence among different types of loads, analyze the electricity-heat-gas load coupling characteristics by utilizing distributed data information and improve the load forecasting precision of the comprehensive energy system.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Examples
Fig. 1 is a flowchart of a distributed load prediction method of an integrated energy system according to an embodiment of the present invention, and as shown in fig. 1, the embodiment provides a distributed load prediction method of an integrated energy system, including:
step 101: acquiring an electric load, a thermal load and an air load of the comprehensive energy system, acquiring an electric load time sequence curve, a thermal load time sequence curve and an air load time sequence curve, and acquiring external factor data; the external factor data includes electricity price, natural gas price, and weather condition data.
For the fact that poor data may exist in the collected original data of the comprehensive energy system, deviation data correction needs to be carried out on the electric load, the heat load and the air load by adopting a data curve fitting method.
The data curve fitting method specifically comprises the following steps: acquiring deviation data; selecting front and back normal data of the deviation data point according to a time sequence by taking the deviation data point as a center; performing curve fitting on the selected normal data to obtain a data fitting function; and calculating a function value at the deviation data point according to the data fitting function, and taking the function value at the deviation data point as a correction value of the deviation data.
And (4) considering dimensional differences of data such as electricity, heat and gas loads, and performing normalization processing on the corrected electricity load, the corrected heat load and the corrected gas load by adopting a min-max method to obtain the normalized electricity load, the normalized heat load and the normalized gas load.
The normalization formula is as follows:
in the formula, x,Respectively the original value and the normalized value of the electrical load (or thermal load, gas load); x is the number ofmax、xminRespectively, the maximum value and the minimum value in the raw data of the electric load (or the heat load and the air load).
Step 102: and calculating the load characteristic index according to the electric load, the heat load and the air load.
The daily average load is calculated according to the following formula:
the daily load rate is calculated according to the following formula:
the peak energy consumption rate was calculated according to the following formula:
the valley time energy consumption rate is calculated according to the following formula:
in the formula, KavDenotes the daily average load, EallThe daily energy is one of daily electricity, daily heat and daily gas, T represents the number of daily time periods, T is 24, KdRepresenting the daily load rate, PavDenotes the daily average load, PmaxRepresents the daily maximum load, ρpRepresents the peak specific energy consumption, EpRepresenting energy used during peak hours, pvIndicating the energy consumption rate at valley time, EvRepresenting energy usage during the daily trough period。
Step 103: and carrying out load clustering according to the load characteristic index, the electric load time sequence curve, the thermal load time sequence curve and the air load time sequence curve to obtain various loads.
Load clustering was performed according to the following formula:
wherein F represents a clustering objective function, K represents the number of clusters, M represents the number of load time sequence curves, and hk,mRepresenting an identification variable, h when the load curve m belongs to the load class kk,mThe value is 1, when the load curve m does not belong to the load class k, hk,mWith a value of 0, α represents a weighting factor,
representing the load characteristic distance between the load curve m and the load class k,
representing the load time-series curve distance, λ, between the load curve m and the load class k1Denotes a first coefficient, λ2Denotes a second coefficient, λ3Denotes a third coefficient, λ4Represents a fourth coefficient and satisfies λ1+λ2+λ3+λ4＝1；Kav,mDenotes the daily average load factor, K, of the load curve mav,kDenotes the daily average load factor, K, of the load class Kd,mRepresenting the daily load factor, K, of the load curve md,kDenotes the daily load rate, ρ, of the load class kp,mRepresents the peak-time energy consumption rate, ρ, of the load curve mp,kRepresents the peak specific energy consumption, ρ, of the load class kv,mThe energy consumption rate at valley time, ρ, expressed as the load curve mv,kRepresenting the valley time energy consumption rate of the load category k; pm,tRepresenting the load value, P, of the load curve m to be clustered during the t periodk,tAnd representing the load value of the load category k to be clustered in the period t.
Step 104: and establishing an offline load prediction model for each type of load according to the external factor data.
Acquiring one type of data in the external factor data; one type of data is one of electricity price, gas price, and meteorological condition (temperature, humidity) data; calendar information (weekday and holiday) may also be obtained.
And calculating the correlation coefficient of one type of data and each type of load. The correlation between the electrical load and the meteorological condition is analyzed by taking the electrical load and the meteorological condition as an example, and the correlation coefficient calculation formula is as follows:
wherein X, Y are electrical load and meteorological condition input data, respectively; cov (X, Y) is the covariance between X and Y, D (X), D (Y) are the mean square deviations of X and Y, respectively.
Comparing the correlation coefficient with a preset threshold value; if the correlation coefficient is larger than a preset threshold (for example, 0.6), determining a type of data as a correlation factor corresponding to the load type; otherwise, determining whether other types of data in the external factor data are relevant factors of the corresponding load types, including respectively determining whether the electricity price and the gas price are relevant factors of the corresponding load types.
And establishing an offline load prediction model according to the correlation factors and the load data corresponding to the correlation factors.
For the kth load, a load classification prediction model based on a long-short term memory network (LSTM) is constructed, and the LSTM is one of deep learning algorithms. Obtaining J having correlation with the kth class load according to the calculation result of the correlation coefficientkFactor(s) J to be obtainedkThe related data of the factors and the kth class load history data are used as the input of the LSTM, and the kth class load history is used as the output of the LSTM; through having been already provided withAnd (4) training the LSTM by a large amount of input and output data to obtain a k-th load prediction model. The offline load prediction model is as follows:
wherein the content of the first and second substances,
as input data of the LSTM model, yk,d+1Outputting a k-th type off-line load prediction model; in the LSTM model training process, input and output data are historical data, and y isk，d、yk,d+1Historical load data for the kth class load on day d and day d +1 respectively,
d +1 calendar history data of the jth class loading jth class factor, J being 1,2, …, Jk。
After step 104, further comprising:
and acquiring the current daily prediction error of each type of load, the next-day predicted daily load of each type of load and the correlation factor corresponding to the load type.
And establishing a load prediction error model according to the prediction error of the current day, the next day prediction day load and the correlation factor corresponding to the load type.
The load prediction error model is built by using the LSTM as follows:
the training process of the load prediction error model is similar to the construction method of the off-line load prediction model. In the training process of the model f (-), the input data and the output data of the model are historical data. Wherein, the load prediction error epsilon of the kth load at d +1 th dayk,d+1Load prediction error ε at day d of kth class loadk,dOff-line load prediction value y of the kth class load on day d +1k,d+1D +1 st day JkIs a factor ofThe prediction data is used as the input of the model; load prediction error epsilon on day d +1k,d+1As output of the model.
Step 105: and acquiring load data of the current day and external factor data of the day to be predicted aiming at each type of load, and performing online load prediction by adopting an offline load prediction model according to the load data of the current day and the external factor data of the day to be predicted to obtain the load of the day to be predicted.
Step 105, specifically comprising:
and generating a comprehensive energy system load prediction model according to the offline load prediction model and the load prediction error model. The comprehensive energy system load prediction model comprises the following steps:
wherein, gk() And fkAnd the k-th class load offline load prediction model and the load prediction error model are respectively. In the case of load on-line prediction, yk,dAnd εk,dLoad data and load prediction error data of the current day of the kth class load are respectively,
predicted values of day d +1 of the k-th class load are taken into consideration for prediction error correction.
And acquiring a current day prediction error, current day load data and external factor data of a day to be predicted aiming at each type of load, and performing online load prediction by adopting a comprehensive energy system load prediction model according to the current day prediction error, the current day load data and the external factor data of the day to be predicted to obtain the daily load to be predicted.
Step 106: and summing the daily loads to be predicted of each type of load to obtain the total load of the daily comprehensive energy system to be predicted.
The total load calculation formula is as follows:
wherein G is the total load of the daily comprehensive energy system to be predicted;
for the predicted load of the kth class, K represents the total number of clusters.
Fig. 2 is a structural diagram of a distributed load prediction system of an integrated energy system according to an embodiment of the present invention, and as shown in fig. 2, the embodiment provides a distributed load prediction system of an integrated energy system, including:
the data acquisition module 201 is configured to acquire an electrical load, a thermal load, and an air load of the integrated energy system, acquire an electrical load time sequence curve, a thermal load time sequence curve, and an air load time sequence curve, and acquire external factor data; the external factor data includes electricity price, gas price, and weather condition data.
And a load characteristic index calculation module 202, configured to calculate a load characteristic index according to the electrical load, the thermal load, and the air load.
The load clustering module 203 is used for carrying out load clustering according to the load characteristic index, the electric load time sequence curve, the thermal load time sequence curve and the air load time sequence curve to obtain various loads;
and an offline load prediction model establishing module 204, configured to establish an offline load prediction model for each type of load according to the external factor data.
And the online load prediction module 205 is configured to obtain load data of the current day and external factor data of a day to be predicted for each type of load, and perform online load prediction by using an offline load prediction model according to the load data of the current day and the external factor data of the day to be predicted to obtain a daily load to be predicted.
And the comprehensive energy system total load calculating module 206 is configured to sum and calculate the daily loads to be predicted of each type of load to obtain the daily comprehensive energy system total load to be predicted.
For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
By adopting the distributed load prediction method and the distributed load prediction system for the comprehensive energy system, the load prediction method and the distributed load prediction system can be applied to the load prediction of the comprehensive energy system in an actual region. The basic load data based on the load forecasting method comprises historical data of electricity, heat and gas loads, electricity price, gas price and meteorological condition (temperature and humidity) data, and accords with the actual situation of a regional comprehensive energy system; the popularization of terminal equipment such as an intelligent electric meter and the like is considered, and the load is classified by adopting a clustering method, so that the distributed prediction of the load of the comprehensive energy system is facilitated; by calculating and excavating the load characteristics off line, the refined modeling of the electric, thermal and gas loads is realized; the unification of the accuracy and the timeliness of the load prediction of the comprehensive energy system is realized by combining the offline calculation and the online prediction; the load prediction result is corrected by constructing a load prediction error model of the comprehensive energy system, so that the load prediction precision of the comprehensive energy system is improved, massive data information can be fully utilized, the high-precision prediction of the load of the comprehensive energy system is realized, and the safe and reliable operation of the comprehensive energy system is facilitated.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In summary, this summary should not be construed to limit the present invention.
Claims (8)
1. A distributed load prediction method for an integrated energy system is characterized by comprising the following steps:
acquiring an electric load, a thermal load and an air load of the comprehensive energy system, acquiring an electric load time sequence curve, a thermal load time sequence curve and an air load time sequence curve, and acquiring external factor data; the external factor data comprises electricity price, gas price and meteorological condition data;
calculating a load characteristic index from the electrical load, the thermal load, and the air load;
carrying out load clustering according to the load characteristic index, the electric load time sequence curve, the heat load time sequence curve and the gas load time sequence curve to obtain various loads;
establishing an offline load prediction model for each type of load according to the external factor data;
acquiring load data of the current day and external factor data of a day to be predicted aiming at each type of load, and performing online load prediction by adopting the offline load prediction model according to the load data of the current day and the external factor data of the day to be predicted to obtain a day load to be predicted;
and summing the daily loads to be predicted of each type of load to obtain the total load of the daily comprehensive energy system to be predicted.
2. The integrated energy system distributed load forecasting method of claim 1, further comprising, prior to calculating a load characteristic indicator based on the electrical load, the thermal load, and the gas load:
correcting deviation data of the electrical load, the thermal load and the gas load by adopting a data curve fitting method;
and performing normalization processing on the corrected electric load, the corrected heat load and the corrected gas load to obtain the normalized electric load, the normalized heat load and the normalized gas load.
3. The distributed load forecasting method of the integrated energy system according to claim 2, wherein the calculating a load characteristic index from the electrical load, the thermal load, and the gas load includes:
the daily average load is calculated according to the following formula:
the daily load rate is calculated according to the following formula:
the peak energy consumption rate was calculated according to the following formula:
the valley time energy consumption rate is calculated according to the following formula:
in the formula, KavDenotes the daily average load, EallExpressing daily energy, wherein the daily energy is one of daily electricity consumption, daily heat and daily gas, T represents the number of daily time segments, T is 24, KdRepresenting the daily load rate, PavDenotes the daily average load, PmaxRepresents the daily maximum load, ρpRepresents the peak specific energy consumption, EpRepresenting energy used during peak hours, pvIndicating the energy consumption rate at valley time, EvIndicating energy usage during the daily trough period.
4. The distributed load prediction method of the integrated energy system according to claim 3, wherein the load clustering is performed according to the load characteristic index, the electrical load time sequence curve, the thermal load time sequence curve and the gas load time sequence curve to obtain multiple types of loads, and specifically includes:
load clustering was performed according to the following formula:
wherein F represents a clustering objective function, K represents the number of clusters, M represents the number of load time sequence curves, and hk,mRepresenting an identification variable, h when the load curve m belongs to the load class kk,mThe value is 1, when the load curve m does not belong to the load class k, hk,mWith a value of 0, α represents a weighting factor,
representing the load characteristic distance between the load curve m and the load class k,representing the load time-series curve distance, λ, between the load curve m and the load class k1Denotes a first coefficient, λ2Denotes a second coefficient, λ3Denotes a third coefficient, λ4Represents a fourth coefficient and satisfies λ1+λ2+λ3+λ4＝1；Kav,mDenotes the daily average load factor, K, of the load curve mav,kDenotes the daily average load factor, K, of the load class Kd,mRepresenting the daily load factor, K, of the load curve md,kDenotes the daily load rate, ρ, of the load class kp,mRepresents the peak-time energy consumption rate, ρ, of the load curve mp,kRepresents the peak specific energy consumption, ρ, of the load class kv,mThe energy consumption rate at valley time, ρ, expressed as the load curve mv,kRepresenting the valley time energy consumption rate of the load category k; pm,tRepresenting the load value, P, of the load curve m to be clustered during the t periodk,tAnd representing the load value of the load category k to be clustered in the period t.
5. The distributed load prediction method for the integrated energy system according to claim 4, wherein the establishing of the offline load prediction model for each type of load according to the external factor data specifically comprises:
acquiring one type of data in the external factor data; the data of one type is one of electricity price, gas price and meteorological condition data;
calculating the correlation coefficient of the class data and each class of load;
comparing the correlation coefficient with a preset threshold value; if the correlation coefficient is larger than the preset threshold value, determining the class of data as a correlation factor corresponding to the load type; otherwise, determining whether other types of data in the external factor data are correlation factors corresponding to the load types;
and establishing an offline load prediction model according to the correlation factors and the load data corresponding to the correlation factors.
6. The distributed load forecasting method of the integrated energy system according to claim 5, further comprising, before performing the online load forecasting:
acquiring a current daily prediction error of each type of load, a next-day predicted daily load of each type of load and correlation factors corresponding to the load types;
and establishing a load prediction error model according to the prediction error of the current day, the next day prediction day load and the correlation factor corresponding to the load type.
7. The distributed load prediction method of the integrated energy system according to claim 6, wherein the acquiring load data of a current day and external factor data of a day to be predicted for each type of load, and performing online load prediction by using the offline load prediction model according to the load data of the current day and the external factor data of the day to be predicted to obtain a daily load to be predicted specifically comprises:
generating a comprehensive energy system load prediction model according to the offline load prediction model and the load prediction error model;
and acquiring a current day prediction error, current day load data and external factor data of a day to be predicted aiming at each type of load, and performing online load prediction by adopting the comprehensive energy system load prediction model according to the current day prediction error, the current day load data and the external factor data of the day to be predicted to obtain the daily load to be predicted.
8. An integrated energy system distributed load prediction system, comprising:
the data acquisition module is used for acquiring the electric load, the heat load and the air load of the comprehensive energy system, acquiring an electric load time sequence curve, a heat load time sequence curve and an air load time sequence curve, and acquiring external factor data; the external factor data comprises electricity price, gas price and meteorological condition data;
a load characteristic index calculation module for calculating a load characteristic index from the electrical load, the thermal load, and the air load;
the load clustering module is used for carrying out load clustering according to the load characteristic index, the electric load time sequence curve, the heat load time sequence curve and the gas load time sequence curve to obtain various loads;
the offline load prediction model establishing module is used for establishing an offline load prediction model for each type of load according to the external factor data;
the online load prediction module is used for acquiring load data of the current day and external factor data of a day to be predicted aiming at each type of load, and performing online load prediction by adopting the offline load prediction model according to the load data of the current day and the external factor data of the day to be predicted to obtain a daily load to be predicted;
and the comprehensive energy system total load calculation module is used for summing the daily loads to be predicted of each type of load to obtain the daily comprehensive energy system total load to be predicted.
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