CN111754037B - Long-term load hybrid prediction method for regional terminal integrated energy supply system - Google Patents
Long-term load hybrid prediction method for regional terminal integrated energy supply system Download PDFInfo
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Abstract
The invention belongs to a long-term load forecasting method, and particularly relates to a long-term load hybrid forecasting method for a regional terminal integrated energy supply system, which comprises the following steps: collecting original historical data of each load in the region, and performing normalization processing; calculating the collected load coupling degrees C; establishing an ARIMA prediction model; determining internal and external variables of a prediction model of each load system; building a VAR prediction model; and obtaining a final prediction result. The method can more accurately perform mixed prediction on the conditions of all long-term loads of the regional terminal integrated energy supply system, and can reasonably arrange the construction of energy supply facilities.
Description
Technical Field
The invention belongs to a long-term load forecasting method, and particularly relates to a long-term load hybrid forecasting method for a regional terminal integrated energy supply system.
Background
Along with the development of advanced science, the demand of people on energy is increased day by day, meanwhile, the problems of energy safety, environmental protection and the like are concerned about as the traditional fossil energy is exhausted day by day, and the conditions of low energy conversion efficiency, non-centralized distribution, high use cost and the like of the existing forms of cold, heat, electricity and the like generally exist, so that the energy and the environment become the main bottleneck restricting the sustainable development of national economy. The physical characteristic complementarity of an electric power system, a thermodynamic system and a gas system is strong, and the regional terminal integrated energy supply system is a novel energy system integrating the supply of electric power, natural gas, heat energy and cold energy, and has important promotion effects on optimizing an energy structure, improving the energy use efficiency and promoting the consumption of renewable energy. The load of the regional multi-energy supply system is influenced by various factors and has the characteristics of uncertainty and nonlinearity. The cold and hot electric loads of the system are not only related to the historical data of the system, but also influence each other.
For regional energy supply, the energy supply range is usually large, the result of load prediction in a region is influenced by various factors, and the influence degree can be more accurately represented only by reasonably quantizing various influence factors. The optimization planning method and the expansion planning method of the integrated energy supply system of the research area terminal are beneficial to realizing the cooperative and efficient supply of cold and hot electricity in a new energy increasing area and the comprehensive and graded utilization of energy in the existing industrial park, and have important practical significance for the comprehensive supply mode of the energy in the power grid innovation area, the improvement of the supply side and the realization of the industrial upgrading.
With regard to load prediction of an electric power system, foreign scholars often adopt methods related to a neural network and a support vector machine to perform load prediction. The scholars propose to select similar daily loads as input loads, then apply wavelet decomposition to decompose the loads into low-frequency components and high-frequency components, and finally use a single neural network to predict the future loads of the two components. And a learner also adopts the asymmetric quadratic loss function to support vector regression to accurately predict the load, so that the accuracy of the load prediction model is effectively improved. A new hybrid intelligent algorithm based on Wavelet Transform (WT) and fuzzy adaptive resonance theory mapping network is proposed, and the model is proved by extensive prediction comparison. Experts divide a time sequence into two parts by using an empirical mode decomposition method, respectively describe the trend and the energy consumption value of local oscillation, and then the two parts are used for training a support vector regression model. Experts also propose a short-term load prediction method based on a kernel machine, which provides a better short-term load prediction result.
Regarding heating system load prediction, kawashima et al abroad used the ANN model to predict the heat demand of a single family building in Sweden. Kalogirou utilizes an ANN model to predict the long-term characteristics of a Greek solar heating system. Chou and Bui use ANN to predict building thermal load. Yang et al use neural networks to predict building thermal loads and determine optimal startup times for heating systems. Kalogirou et al utilize a BP neural network for predicting building thermal loads and train with 225 building data. Ekici and Aksoy established a BP neural network with input variables of building orientation, building insulation thickness and transmittance parameters to predict the annual heat load demand of independent sweden households. Olofsson and Andersson build neural network models that utilize short-term (2-5 weeks) data for long-term thermal load prediction. Kreider et al predict building thermal loads using a BP neural network with input variables for time-by-time building energy consumption data. And the Yan and the Yao carry out prediction analysis on the building heat load data in different climatic regions by adopting a neural network model.
With respect to load prediction of an electric power system, domestic scholars have also developed various methods and algorithms in order to improve the accuracy and speed of load prediction. The learners take the voltage characteristics as basic quantities for describing the system state characteristics, and propose a state estimation model and an algorithm based on weighted least squares. And experts also realize attribute reduction of a rough set theory by adopting the global search capability of a genetic algorithm, and improve and perfect the load prediction model and algorithm of the least square support vector machine by optimizing the input variables of the model and automatically optimizing the parameters of the model by adopting a real-value genetic algorithm. The students also introduce human comfort indexes, comprehensively consider the influence of meteorological factors, establish a particle swarm parameter Optimization-based Support Vector Machine (PSO-SVM) prediction model by utilizing daily feature vectors and load data of similar days, and experiments prove that the prediction precision is high and the popularization capability is strong. The problems of data preprocessing, kernel function construction and selection, parameter optimization and the like in the application of the SVM in short-term load prediction are analyzed, the existing solution is summarized, and the key problem to be solved next step is provided. The method is characterized in that students specially study the power load prediction problem of local areas, and provide a novel multi-mode variable structure load prediction method based on self-adaptive clustering partition and support vector regression. The professor David in the Doe of cattle utilizes a data mining technology which has advantages in the aspects of processing large data volume, eliminating redundant information and the like to preprocess historical data to form a data sequence with highly similar meteorological characteristics, and the sequence is used as training data of an SVM (support vector machine), so that the data volume is reduced, the prediction speed and precision are improved, and the defects of a support vector machine are overcome.
As for the load prediction of a heating system, chinese researchers carry out theoretical and time research work on the application of an artificial neural network to the heat load prediction. The Du-jin et al explains how to determine the input variables and the output variables of the heat supply load prediction neural network, the prediction time step length, the number of hidden layer units and the like. Congying carries out comparative study on the effect of stepwise regression analysis and an artificial neural network on building heat load prediction.
In summary, the conventional load prediction methods are all directed to a single energy system, and the coupling relationship between multiple energy sources is not considered, so that it is necessary to perform uniform load prediction on a regional integrated energy supply system with multiple energy sources.
Disclosure of Invention
The invention aims to provide a long-term load hybrid prediction method for a regional terminal integrated energy supply system, aiming at the problems in the prior art, which can more accurately perform hybrid prediction on the conditions of each long-term load of the regional terminal integrated energy supply system and reasonably arrange the construction of energy supply facilities.
The technical scheme of the invention is as follows:
the long-term load hybrid prediction method for the area terminal integrated energy supply system comprises the following steps:
s1, collecting original historical data of each load in a region, and performing normalization processing;
s2, calculating the collected coupling degree C of each load;
s3, establishing an ARIMA prediction model;
s4, determining internal and external variables of a prediction model of each load system;
s5, building a VAR prediction model;
and S6, obtaining a final prediction result.
Specifically, the normalization processing step in step S1 is as follows:
a) The normalization of each data in the analysis range of the electric, gas, cold and heat loads is calculated according to the following formula
Chemical value
Wherein: l is emax 、L emin The maximum value and the minimum value in the electric load analysis range are obtained;
L gmax 、L gmin the maximum value and the minimum value in the gas load analysis range are obtained;
L cmax 、L cmin maximum and minimum values in the analysis range of the cold load;
L hmax 、L hmin maximum and minimum values in the thermal load analysis range;
l * e (i)、l e (i) The method comprises the following steps The electricity load of the ith day in the load analysis rangeA load normalized value and an actual value;
l * g (i)、l g (i) The method comprises the following steps The ith weather load normalization value and the actual value in the load analysis range;
l * c (i)、l c (i) The method comprises the following steps The cold load normalization value and the actual value on the ith day in the load analysis range;
l * h (i)、l h (i) The method comprises the following steps The normalized value and the actual value of the thermal load of the ith day in the load analysis range;
b) The electric, gas and cold and heat loads account for the specific weight of the system load in the ith day:
where Σ l * (i) Total load for system day i:
c) Day i load entropy value e (i):
e(i)＝-(ln4) -1 [r e (i)ln r e (i)+r g (i)ln r g (i)+r c (i)ln r c (i)+r h (i)ln r h (i)]；
d) Day i system load variability coefficient g (i): g (i) =1-e (i);
e) Day i system load weight ω (i):
f) Electricity, gas, cold and heat load comprehensive change index:
wherein gamma is e 、γ g 、γ c 、γ h Respectively are the comprehensive change indexes of the system electricity, gas, cold and heat loads.
Specifically, the load coupling degree calculation formula is as follows:
wherein C is e,g 、C e,c 、C e,h 、C g,h 、C g,c 、C c,h The coupling values of electric-gas, electric-cold, electric-heat and gas-cold, gas-heat loads in the analysis range are respectively, and the value range is [0, 1%]。
Specifically, the establishing step of the ARIMA prediction model in step S3 is as follows:
(1) Difference processing: d-order difference processing is carried out on the original time sequence [ Xt ] to obtain a stable time sequence [ Xt' ];
(2) Model identification and parameter scaling: calculating stationary time series [ Xt' ] autocorrelation and partial autocorrelation functions
Preliminarily determining model types (AR, MA and ARMA), and determining values of model parameters p and q by using an information criterion of a minimum information criterion (AIC);
(3) Parameter estimation: using the correlation moments to carry out parameter estimation on ai and bj, and determining a final ARIMA (p, q, d) model;
(4) And (3) data prediction: single-step or multi-step prediction is realized through the established ARIMA model.
Specifically, the expression of the VAR prediction model in step S5 is as follows:
y t ＝A 1 y t-1 +…+A p y t-p +Bx t +ε t t＝1，2、…，N
wherein y is t Is k dimension internal variable vector, N is sample number, k x k dimension matrix A1, \8230, ap is internal variable coefficient matrix, B is external variable coefficient matrix, epsilon t Is a k-dimensional perturbation vector.
Specifically, the building of the VAR prediction model in step S5 includes the following steps:
1) Determining model variables: determining endogenous variables and exogenous variables of the model through characteristic analysis of the relevant variables;
2) Estimating model parameters: determining stationary time series vector [ y ] 1t ，y 2t ，y 3t ，y Tt ，x t ]Calculating an endogenous variable coefficient matrix A and an exogenous variable coefficient matrix B by utilizing maximum likelihood estimation;
3) Determining the order of the model: determining the order p of the model by using Akaike's Information Criterion (AIC);
4) And (3) data prediction: single-step or multi-step prediction is realized through the established VAR (p) model.
The beneficial effects of the invention are: the prediction method provided by the invention fully considers the relevance among electric, heat, cold and gas loads in an interval, and performs quantitative analysis by using the coupling relation, and because the dimensions of various influence factors are different, in order to prevent the real action of partial influence factors from being distorted or even annihilated in the whole mapping effect due to the difference of value range, the invention uniformly performs standardized treatment on various influence factors, so that the threshold value fluctuates in the range of 0-1. The method provided by the invention has stronger tracking capability on each load, has smaller prediction error and has obvious advantages.
Drawings
FIG. 1 is a schematic technical route of embodiment 1;
FIG. 2 is a graph of the results of an electrical load coupling prediction;
FIG. 3 is a graph of the prediction of the degree of electrical cooling load coupling;
FIG. 4 is a graph of results of prediction of degree of coupling of electrical heating load;
FIG. 5 is a graph of air cooling load coupling prediction results;
FIG. 6 is a graph of the prediction of gas heat load coupling;
FIG. 7 is a graph of the predicted cold and heat load coupling;
FIG. 8 is a graph of the prediction results of the electrical load independent prediction model;
FIG. 9 is a graph of electrical load hybrid predictive model prediction results;
FIG. 10 is a graph of the air load independent prediction model prediction results;
FIG. 11 is a graph of the prediction results of the air load hybrid prediction model;
FIG. 12 is a graph of cold load independent prediction model prediction results;
FIG. 13 is a graph of cold load hybrid predictive model prediction results;
FIG. 14 is a graph of thermal load independent prediction model prediction results;
FIG. 15 is a graph of thermal load hybrid predictive model prediction results.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.
The invention provides a long-term load hybrid prediction method for a regional terminal integrated energy supply system, which comprises the following steps:
s1, collecting original historical data of each load in a region, and performing normalization processing;
s2, calculating the collected coupling degree C of each load;
s3, establishing an ARIMA prediction model;
s4, determining internal and external variables of a prediction model of each load system;
s5, building a VAR prediction model;
and S6, obtaining a final prediction result.
In the integrated energy supply system of the area terminal, the result of load prediction is influenced by various factors, and only by reasonably quantizing various influencing factors, the influence degree can be more accurately represented, and the influence degree is introduced into the modeling of a prediction model. Because various influencing factors have different dimensions, in order to prevent the true action of partial influencing factors from being distorted or even annihilated in the whole mapping effect due to the difference of value range, the various influencing factors are uniformly standardized in the text, so that the threshold value fluctuates within the range of 0-1. The time series normalization processing steps for each influence factor in the step S1 are as follows:
a) The normalization of each data in the analysis range of the electric, gas, cold and heat loads is calculated according to the following formula
Chemical value
Wherein: l is emax 、L emin The maximum value and the minimum value in the electric load analysis range are obtained;
L gmax 、L gmin the maximum value and the minimum value in the gas load analysis range are obtained;
L cmax 、L cmin maximum and minimum values in the analysis range of the cold load;
L hmax 、L hmin maximum and minimum values in the thermal load analysis range;
l * e (i)、l e (i) The method comprises the following steps The electricity load normalization value and the actual value of the ith day in the load analysis range;
l * g (i)、l g (i) The method comprises the following steps The ith weather load normalization value and the actual value in the load analysis range;
l * c (i)、l c (i) The method comprises the following steps The cold load normalization value and the actual value on the ith day in the load analysis range;
l * h (i)、l h (i) The method comprises the following steps The normalized value and the actual value of the thermal load of the ith day in the load analysis range;
b) The electric, gas and cold and heat loads account for the specific weight of the system load in the ith day:
where ∑ l * (i) Total load for system day i:
c) Day i load entropy value e (i):
e(i)＝-(ln4) -1 [r e (i)ln r e (i)+r g (i)ln r g (i)+r c (i)ln r c (i)+r h (i)ln r h (i)]；
d) Day i system load variability coefficient g (i): g (i) =1-e (i);
e) Day i system load weight ω (i):
f) Electricity, gas, cold and heat load comprehensive change index:
wherein gamma is e 、γ g 、γ c 、γ h Respectively are the comprehensive change indexes of the system electricity, gas, cold and heat loads.
The degree of coupling is the degree of interaction of the description systems, and the degree of influence between the loads of the integrated energy supply system of the electric-gas interconnection area terminal is defined as the degree of load coupling, and the value reflects the degree of interaction between the system loads. With reference to a capacity coupling concept (capacity coupling) and a capacity coupling coefficient model in physics, a coupling calculation model of a load of a regional terminal integrated energy supply system can be defined, and the load coupling calculation formula is as follows:
wherein C e,g 、C e,c 、C e,h 、C g,h 、C g,c 、C c,h The coupling values of electricity-gas, electricity-cold, electricity-heat and gas-cold, gas-heat loads in the analysis range are respectively, and the value range is [0, 1%]。
a) When the coupling degree function value C tends to 1, the coupling degree is maximum, which shows that the coupling relationship between every two four loads of the area terminal integrated energy supply system is strong;
b) When the coupling degree function value C =0, the coupling degree is extremely low, which indicates that no mutual influence relationship exists between the quadruple loads of the area terminal integrated energy supply system;
c) When C is more than 0 and less than or equal to 0.5, the loads of the area terminal integrated energy supply system are in a coupling state of a lower level, the interaction among the loads is not strong, and the coupling relation of the loads can not be considered in the process of load prediction;
d) When C is more than 0.5 and less than or equal to 1, the coupling degree between the loads is high, the interaction relation between the comprehensive energy loads is strong, and the interaction between the loads is fully considered in establishing a model for predicting the comprehensive energy loads so as to improve the prediction accuracy.
The building steps of the ARIMA prediction model in the step S3 are as follows:
(1) Difference processing: d-order difference processing is carried out on the original time sequence [ Xt ] to obtain a stable time sequence [ Xt' ];
(2) Model identification and parameter scaling: calculating stationary time series [ Xt' ] autocorrelation and partial autocorrelation functions
Counting, preliminarily determining model types (AR, MA and ARMA), and determining values of model parameters p and q by utilizing a minimum information criterion (AIC) information criterion;
(3) Parameter estimation: using the correlation moments to carry out parameter estimation on ai and bj, and determining a final ARIMA (p, q, d) model;
(4) And (3) data prediction: single-step or multi-step prediction is realized through the established ARIMA model.
The independent prediction model of the electric, gas, cold and heat loads of the area terminal integrated energy supply system is as follows:
electric load independent prediction model:
air load independent prediction model:
cold load independent prediction model:
heat load independent prediction model:
in the step S5, the VAR is a vector auto regression (vector auto regression) model established based on statistical properties of data, and the basic principle is that a time sequence is regarded as a random process, a mathematical model is established to describe or simulate the time sequence, so that dynamic characteristics and continuous characteristics of linear components of the time sequence can be well reflected, and a correlation between the past and future time of the time sequence and the present time is revealed. Generally denoted as VAR (p), where p is the model order, the general expression for VAR (p) is as follows:
y t ＝A 1 y t-1 +…+A p y t-p +Bx t +ε t t＝1，2、…，N
wherein y is t Is k dimension internal variable vector, N is sample number, k x k dimension matrix A1, \8230, ap is internal variable coefficient matrix, B is external variable coefficient matrix, epsilon t Is a k-dimensional perturbation vector.
The building of the VAR prediction model in the step S5 comprises the following steps:
1) Determining model variables: determining the endogenous and exogenous variables of a model by characteristic analysis of the relevant variables
A production variable;
2) Estimating model parameters: determining stationary time series vector [ y ] 1t ，y 2t ，y 3t ，y Tt ，x t ]Calculating an endogenous variable coefficient matrix A and an exogenous variable coefficient matrix B by utilizing maximum likelihood estimation;
3) Determining the order of the model: determining by using Akaike's Information Criterion (AIC)
The model order p;
4) And (3) data prediction: single-step or multi-step prediction is realized through the established VAR (p) model.
Example 1
In the embodiment, the VAR is used as a basic model for system load hybrid prediction, the endogenous variable and the exogenous variable of the VAR (p) are determined by combining the load coupling degree of one week before the daily load with prediction, and 12 years in 2017 are utilized
And modeling the load data of the integrated energy supply system of the regional terminal from 1 month to 12 months and 11 months in 2018, and predicting the load conditions of the system from 12 months and 12 days in 2018 to 1 month and 10 days in 2019. Fig. 1 is a circuit diagram of a load prediction technology of an area terminal integrated energy supply system.
As shown in fig. 2 to fig. 8, the load coupling degree prediction result curves of the week before the daily load of the terminal integrated energy supply system in the area from 12 months and 12 days in 2018 to 10 months and 1 month and 2019 are shown in table 1 below, and the prediction results are shown in the graphs as being high in prediction accuracy. It can be seen from the table that the air-cooling load coupling degree is less than 0.5 from day 22 to day 29, the cooling-heating load coupling degree is less than 0.5 from day 21 to day 29, the load coupling degrees of the other cases are all more than 0.5, the coupling conditions of the loads are comprehensively considered to determine the internal and external variables of the VAR prediction model of the loads, the load hybrid prediction model of the regional terminal integrated energy supply system is established, and the internal and external variable determination table of the load prediction VAR model of the regional terminal integrated energy supply system is shown in the following table 2.
TABLE 1
| C e，g | C e，c | C e，h | C g，c | C g，h | |
|
| 1 | 0.963437 | 1.004296 | 0.878808 | 0.962141 | 0.967959 | 0.87458 |
| 2 | 0.950128 | 1.000862 | 0.852101 | 0.938362 | 0.966143 | 0.834486 |
| 3 | 0.908921 | 0.998254 | 0.795197 | 0.910508 | 0.9704 | 0.8018 |
| 4 | 0.880099 | 0.997168 | 0.775733 | 0.863026 | 0.976234 | 0.755012 |
| 5 | 0.867569 | 0.998744 | 0.753112 | 0.880378 | 0.970673 | 0.773016 |
| 6 | 0.82645 | 0.995223 | 0.700012 | 0.872622 | 0.967626 | 0.753991 |
| 7 | 0.833792 | 0.993932 | 0.713403 | 0.874939 | 0.968008 | 0.757079 |
| 8 | 0.830698 | 0.993916 | 0.704748 | 0.87048 | 0.968509 | 0.752051 |
| 9 | 0.828322 | 0.994297 | 0.705659 | 0.871643 | 0.968375 | 0.754629 |
| 10 | 0.843782 | 0.992172 | 0.714766 | 0.899022 | 0.963679 | 0.779896 |
| 11 | 0.869187 | 0.987495 | 0.732348 | 0.935502 | 0.956345 | 0.815099 |
| 12 | 0.885563 | 0.991394 | 0.754873 | 0.928974 | 0.9602 | 0.810583 |
| 13 | 0.929578 | 0.998001 | 0.817621 | 0.946011 | 0.963306 | 0.842516 |
| 14 | 0.8915 | 0.98999 | 0.741661 | 0.949791 | 0.95231 | 0.830787 |
| 15 | 0.877639 | 0.983451 | 0.732355 | 0.938232 | 0.950002 | 0.807081 |
| 16 | 0.882935 | 0.988303 | 0.735795 | 0.925889 | 0.952946 | 0.793041 |
| 17 | 0.846487 | 0.997061 | 0.706094 | 0.8698 | 0.963646 | 0.73348 |
| 18 | 0.801971 | 1.002996 | 0.679536 | 0.783301 | 0.973987 | 0.659776 |
| 19 | 0.767692 | 0.999268 | 0.653648 | 0.717908 | 0.976048 | 0.605837 |
| 20 | 0.719618 | 0.989873 | 0.612723 | 0.636617 | 0.978554 | 0.533974 |
| 21 | 0.719223 | 0.93723 | 0.634587 | 0.510694 | 0.986218 | 0.43201 |
| 22 | 0.672029 | 0.881615 | 0.576674 | 0.42949 | 0.983424 | 0.357404 |
| 23 | 0.652076 | 0.814813 | 0.578747 | 0.366169 | 0.988111 | 0.311786 |
| 24 | 0.670301 | 0.753233 | 0.597514 | 0.344596 | 0.989021 | 0.296285 |
| 25 | 0.680893 | 0.743666 | 0.608673 | 0.353476 | 0.988937 | 0.304211 |
| 26 | 0.690526 | 0.757554 | 0.613232 | 0.364945 | 0.986657 | 0.310955 |
| 27 | 0.725195 | 0.713525 | 0.64812 | 0.343704 | 0.986634 | 0.289613 |
| 28 | 0.762506 | 0.734068 | 0.681925 | 0.401153 | 0.985945 | 0.343279 |
| 29 | 0.816763 | 0.783949 | 0.748862 | 0.495596 | 0.989198 | 0.436165 |
| 30 | 0.870904 | 0.850816 | 0.792387 | 0.595839 | 0.983663 | 0.516448 |
TABLE 2
Respectively recording the time sequences of the electric load, the gas load, the cold load and the heat load of the area terminal integrated energy supply system as follows: { l e (t)}，{l g (t)}，{l c (t)}，{l h (t) }, wherein t =1-N, N =406 is the number of sample spaces, namely the total days from 12 months 1 days in 2017 to 1 months 10 days in 2018, and the load condition of the last 30 days (one month) is predicted by modeling with the front 376 group data of the samples.
1) Electric load hybrid prediction model
The electrical load hybrid prediction VAR model comprises the following internal variables of an electrical load and a cold load, the air load and the heat load which are relatively weak in correlation with the electrical load are external variables, and the AIC criterion is used for determining the order p =3 of the model:
wherein L is e ＝[l e (t-1)，l e (t-2)，l e (t-3)] T ，L c ＝[l c (t-1)，l c (t-2)，l c (t-3)] T 。
2) Gas load hybrid prediction model
The method comprises the steps that on the 1 st to 21 st days and the 30 th days of an air load hybrid prediction VAR model, endogenous variables are air load and heat load, exogenous variables are electric load and cold load, and the model order p =2 is determined by using an AIC (air interface computer) criterion; from day 22 to day 29, the endogenous variables are gas load and heat load, the exogenous variables are electric load, and the model order is p =3:
wherein: l is a radical of an alcohol g ＝[l g (t-1)，l g (t-2)] T ，L h ＝[l h (t-1)，l h (t-2)] T ，L′ g ＝[l g (t-1)，l g (t-2)，l g (t-3)] T ，L′ h ＝[l h (t-1)，l h (t-2)，l h (t-3)] T 。
3) Cold load hybrid prediction model
The cold load hybrid prediction VAR model comprises the following steps that (1) day 1 to 21 day and 30 day internal variables are cold load and electric load, external variables are air load and heat load, and the model order p =3 is determined by using an AIC (air interface) criterion; from day 22 to day 29, the endogenous variables were the cooling load and the electrical load, the model order was p =2:
wherein L is e ＝[l e (t-1)，l e (t-2)，l e (t-3)] T ，L c ＝[l c (t-1)，l c (t-2)，l c (t-3)] T ，L′ e ＝[l′ e (t-1)，l′ e (t-2)] T ，L′ c ＝[l′ c (t-1)，l′ c (t-2)] T 。
4) Heat load collaborative prediction model
The method comprises the steps that on days 1 to 20 and 30 of a VAR model for hybrid prediction of heat load of a regional terminal integrated energy supply system, endogenous variables are air load and heat load, exogenous variables are electric load and cold load, and the number p =2 of the model is determined by using an AIC (advanced information center) criterion; from day 21 to day 29, the endogenous variables are gas load and heat load, the exogenous variables are electric load, and the model order is p =3:
wherein L is g ＝[l g (t-1)，l g (t-2)] T ，L h ＝[l h (t-1)，l h (t-2)] T ，L′ g ＝[l g (t-1)，l g (t-2)，l g (t-3)] T ，L′ h ＝[l h (t-1)，l h (t-2)，l h (t-3)] T 。
In order to verify the effectiveness of the load hybrid prediction method for the area terminal integrated energy supply system, an independent prediction ARIMA model and a hybrid prediction model are respectively adopted to predict the sample load, and as shown in FIGS. 8-15, the prediction result curves of the area terminal integrated energy supply system under two models of electricity, gas, cold and heat loads are respectively shown.
By comparing the prediction curves of the mixed prediction method and the independent load method of the regional terminal integrated energy supply system, the method provided by the invention has strong tracking capability on the load curve. In order to further evaluate the prediction effect of the electric, gas, cold and heat loads of the area terminal integrated energy supply system, the average absolute percentage error epsilon is adopted MAPE And the root mean square error ε RMSE Measuring the integral error degree and the deviation degree between the predicted value and the true value by adopting the maximum relative error epsilon M Reflecting the degree of local prediction error.
Wherein l i For the actual load value, l' i is the predicted load value and n is the total predicted load. The following table 3 shows the average absolute percentage error and the root mean square error of each load prediction under the two prediction methods, and the following table 4 shows the maximum relative error of each load prediction under the two prediction methods. Obviously, the load prediction method provided by the invention has stronger tracking capability on each load, has smaller prediction error and has obvious advantages.
TABLE 3
TABLE 4
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention and not to limit it; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that: modifications of the embodiments of the invention or equivalent substitutions for parts of the technical features are possible; without departing from the spirit of the present invention, it is intended to cover all aspects of the invention as defined by the appended claims.
Claims (5)
1. The long-term load hybrid prediction method of the area terminal integrated energy supply system is characterized by comprising the following steps:
s1, collecting original historical data of each load in a region, and performing normalization processing;
s2, calculating the collected coupling degree C of each load;
s3, establishing an ARIMA prediction model;
s4, determining internal and external variables of a prediction model of each load system;
s5, building a VAR prediction model;
s6, obtaining a final prediction result;
the normalization processing step in the step S1 is as follows:
a) The normalization of each data in the analysis range of the electric, gas, cold and heat loads is calculated according to the following formula
Chemical value
Wherein: l is a radical of an alcohol emax 、L emin The maximum value and the minimum value in the electric load analysis range are obtained;
L gmax 、L gmin the maximum value and the minimum value in the gas load analysis range are obtained;
L cmax 、L cmin maximum and minimum values in the analysis range of the cold load;
L hmax 、L hmin maximum and minimum values in the thermal load analysis range;
l * e (i)、l e (i) The method comprises the following steps The electricity load normalization value and the actual value in the ith day in the load analysis range;
l * g (i)、l g (i) The method comprises the following steps The ith weather load normalization value and the actual value in the load analysis range;
l * c (i)、l c (i) The method comprises the following steps The cold load normalization value and the actual value on the ith day in the load analysis range;
l * h (i)、l h (i) The method comprises the following steps The normalized value and the actual value of the thermal load of the ith day in the load analysis range;
b) The electric, gas and cold and heat loads account for the specific weight of the system load in the ith day:
where ∑ l * (i) Total load for system day i:
c) Day i load entropy value e (i):
e(i)＝-(ln4) -1 [r e (i)ln r e (i)+r g (i)ln r g (i)+r c (i)ln r c (i)+r h (i)ln r h (i)]；
d) Day i system load variability coefficient g (i): g (i) =1-e (i);
e) Day i system load weight ω (i):
f) Electricity, gas, cold and heat load comprehensive change index:
wherein gamma is e 、γ g 、γ c 、γ h Respectively are the comprehensive change indexes of the system electricity, gas, cold and heat loads.
2. The long-term load hybrid prediction method of the area terminal integrated energy supply system according to claim 1, wherein the load coupling degree calculation formula is as follows:
wherein C e,g 、C e,c 、C e,h 、C g,h 、C g,c 、C c,h The coupling values of electricity-gas, electricity-cold, electricity-heat and gas-cold, gas-heat loads in the analysis range are respectively, and the value range is [0, 1%]。
3. The method for predicting long-term load hybrid of an area terminal integrated energy supply system according to claim 1, wherein the establishing step of the ARIMA prediction model in the step S3 is as follows:
(1) Difference processing: d-order difference processing is carried out on the original time sequence [ Xt ] to obtain a stable time sequence [ Xt' ];
(2) Model identification and parameter scaling: calculating the autocorrelation and partial autocorrelation functions of a stationary time sequence [ Xt' ], preliminarily determining model categories (AR, MA and ARMA), and determining the values of model parameters p and q by using the minimum information criterion (AIC) information criterion;
(3) Parameter estimation: using the correlation moments to carry out parameter estimation on ai and bj, and determining a final ARIMA (p, q, d) model;
(4) And (3) data prediction: single-step or multi-step prediction is realized through the established ARIMA model.
4. The long-term load hybrid prediction method for the area terminal integrated energy supply system according to claim 1, wherein the expression of the VAR prediction model in the step S5 is as follows:
y t ＝A 1 y t-1 +…+A p y t-p +Bx t +ε t t＝1，2，…，N
wherein y is t Is k dimension internal variable vector, N is sample number, k x k dimension matrix A1, \8230, ap is internal variable coefficient matrix, B is external variable coefficient matrix, epsilon t Is a k-dimensional perturbation vector.
5. The mixed prediction method for long-term load of the area terminal integrated energy supply system according to claim 4, wherein the building of the VAR prediction model in the step S5 comprises the following steps:
1) Determining model variables: determining an endogenous variable and an exogenous variable of the model through characteristic analysis of the related variables;
2) Estimating model parameters: determining stationary time series vector [ y 1t ，y 2t ，y 3t ，y Tt ，x t ]Calculating an internal generation variable coefficient matrix A and an external generation variable coefficient matrix B by utilizing maximum likelihood estimation;
3) Model order determination: determining the model order p by using Akaike' information criterion (AIC);
4) And (3) data prediction: single-step or multi-step prediction is realized through the established VAR (p) model.
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