CN105574325A - Method for forecasting mid-long-term electricity consumption in combination with population index - Google Patents
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Abstract
The invention provides a method for forecasting mid-long-term electricity consumption in combination with a population index. The method is characterized by comprising the steps of firstly, obtaining electricity consumption of each user and a resident population number in each of past k years in a to-be-forecast region; secondly, calculating a per-capita electricity consumption forecasting value by considering a per-capita electricity consumption increment coefficient; thirdly, calculating a resident population number forecasting value; fourthly, multiplying the per-capita electricity consumption forecasting value by the resident population number forecasting value to obtain a forecasting value of the sum of electricity consumption of residential users and commercial users; fifthly, calculating electricity consumption forecasting values of industrial users, electricity consumption forecasting values of non industrial users and electricity consumption forecasting values of other users; and finally, calculating a total electricity forecasting value. The forecasting method is simple in algorithm, can reduce data demand amount, is high in operability, effectively grasps key points of electricity consumption forecasting for different electricity users, is high in forecasting result precision, and can provide marketing decision support for power supply enterprises.
Description
Technical field
The present invention relates to electricity demand forecasting technical field, more particularly, relate to a kind of medium-term and long-term electricity demand forecasting method in conjunction with demographic indicator.
Background technology
By the impact of the factor such as economic transition, industry restructuring, population migration be frequent, the total electricity consumption of electrical network is also corresponding to change.Power quantity predicting is a groundwork in electricity market, correctly power quantity predicting is carried out in conjunction with trade power consumption feature, for power supply enterprise provides marketing decision-making support, to the construction and development of the safety and economic operation of electrical network and electricity market all tool be of great significance.
Power consumer is divided into resident, commercial user, industrial user, non-technical family and other users totally five classes according to electricity price type and part throttle characteristics by current China.Other users refer to the user except resident, commercial user, industrial user and non-technical family, such as agricultural production user and rice field irrigation and drainage user.Wherein, the electricity consumption main body of resident and commercial user is individual, and therefore the power consumption value effect of the size of population to resident and commercial user is larger.At present, mainly there are following 2 problems in electricity demand forecasting method, and one is pursue the fitting precision to the total electricity raw data of electrical network, and two is pursue the complicated algorithm based on Large Copacity sample prerequisite, and ignores the impact of population variety.In power quantity predicting work, because the power consumption impact of demographic factor on resident and commercial user two class user is larger, cause the power consumption of this two classes user may there is larger difference with the power consumption rule of development of industrial user, non-technical family and this three classes user of other users, if pursue the fitting precision of data simply, the generalization of Forecasting Methodology can be reduced; If too pursue complicated algorithm, then can reduce operability because demand data amount is too large.Above problem is unfavorable for that power supply enterprise carries out predicting and improving the precision that predicts the outcome for each regional power consumption; Therefore, need from new thinking, adopt new method to carry out medium-term and long-term electricity demand forecasting.
Summary of the invention
The object of the invention is to overcome shortcoming of the prior art with not enough, provide a kind of simple in conjunction with demographic indicator, algorithm, demand data amount can be reduced, operability is high, the precision that predicts the outcome is high, can be the medium-term and long-term electricity demand forecasting method that power supply enterprise provides marketing decision-making to support.
In order to achieve the above object, the present invention is achieved by following technical proposals: a kind of medium-term and long-term electricity demand forecasting method in conjunction with demographic indicator, is characterized in that, comprise the steps:
The first step, to obtain in the passing k in area to be predicted resident's power consumption in every year respectively
commercial user's power consumption
industrial user's power consumption
non-technical family power consumption
other user power utilization amounts
with permanent resident population's quantity
wherein, i is the time to be predicted; N ∈ [1, k]; K is integer and k>=3;
Second step, calculated toward in k every year resident's power consumption and commercial user's power consumption sum
Calculated toward power consumption q ' per capita in every year in k:
Calculate year to be predicted power consumption predicted value q per capita:
Q=(1+ α) × q'; Wherein, α is power consumption growth factor per capita;
3rd step, adopt prediction algorithm in passing k every year permanent resident population's amount R
i-nprocess, obtain annual permanent resident population's quantitative forecast value R to be predicted
i; Calculate the predicted value of annual resident's power consumption to be predicted and commercial user's power consumption sum
4th step, adopt prediction algorithm respectively in passing k every year industrial user's power consumption
non-technical family power consumption
with other user power utilization amounts
process, obtain annual industrial user's electricity demand forecasting value to be predicted
non-technical family electricity demand forecasting value
with other user power utilization amount predicted values
5th step, calculates year to be predicted total power quantity predicting value Q
i:
Forecasting Methodology of the present invention can reduce the complexity of algorithm, reduces demand data amount, improves operability widely.From permanent resident population's quantitative relation level of intimate, different prediction and calculation methods is adopted to the power consumer of different level of intimate according to power consumer; Due to resident's power consumption and commercial user's power consumption and permanent resident population's quantitative relation close, industrial user, non-technical family and other users are then little with permanent resident population's quantitative relation; Therefore in the forecasting process of resident's power consumption and commercial user's power consumption in conjunction with permanent resident population's quantity factor, effectively hold the key point of all kinds power consumer electricity demand forecasting; The precision that predicts the outcome is high, can be power supply enterprise and provides marketing decision-making support, ensures power grid security economical operation and electricity market sane development.
Further scheme is: in described 3rd step, adopt prediction algorithm in passing k every year permanent resident population's amount R
i-ncarry out process to refer to, what adopt Linear regression, weighted fitting rectilineal equation, cumulative linear fitting, grey forecasting model, hyperbolic model, logarithmic curve model, sigmoid curve model and fall in exponential model two or morely carries out the corresponding annual permanent resident population's quantitative forecast reference value to be predicted of process acquisition respectively; Annual permanent resident population's quantitative forecast reference value to be predicted is carried out arithmetic mean to obtain annual permanent resident population's quantitative forecast value R to be predicted
i.
In described 4th step, adopt prediction algorithm respectively in passing k every year industrial user's power consumption
non-technical family power consumption
with other user power utilization amounts
carry out process to refer to, what adopt Linear regression, weighted fitting rectilineal equation, cumulative linear fitting, grey forecasting model, hyperbolic model, logarithmic curve model, sigmoid curve model and fall in exponential model two or morely processes respectively, obtains corresponding annual industrial user's electricity demand forecasting reference value to be predicted, non-technical family electricity demand forecasting reference value and other user power utilization amount prediction reference;
Respectively annual industrial user's electricity demand forecasting reference value to be predicted, non-technical family electricity demand forecasting reference value and other user power utilization amount prediction reference are carried out arithmetic mean, to obtain annual industrial user's electricity demand forecasting value to be predicted
non-technical family electricity demand forecasting value
with other user power utilization amount predicted values
Because industrial user, non-technical family and other users are then little with permanent resident population's quantitative relation, therefore above-mentioned prediction algorithm can be adopted.Eight kinds of arithmetic result such as Linear regression are stablized, and are applicable to index that is few to data volume, that do not have cyclic swing and carry out prediction and calculation; Adopt many algorithms to carry out annual industrial user's electricity demand forecasting reference value to be predicted, non-technical family electricity demand forecasting reference value and other user power utilization amount prediction reference, carry out arithmetic mean more afterwards, high-precision predicting the outcome can be obtained.
The span of described α is :-5%≤α≤20%.The span of α draws by gathering historical data statistics, is conducive to improving the precision predicted the outcome.
Compared with prior art, tool of the present invention has the following advantages and beneficial effect:
1, Forecasting Methodology of the present invention can reduce the complexity of algorithm, reduces demand data amount, improves operability widely; From permanent resident population's quantitative relation level of intimate, different prediction and calculation methods is adopted to the power consumer of different level of intimate according to power consumer; Take into full account the impact of permanent resident population's quantity on resident's power consumption and commercial user's power consumption, the key point of effective assurance all kinds power consumer electricity demand forecasting, overcome the problem that current Forecasting Methodology only studies the development and change rule of power consumption own, make to predict the outcome more objective, more accurate; The precision that predicts the outcome is high, can be power supply enterprise and provides marketing decision-making support, ensures power grid security economical operation and electricity market sane development;
2, Forecasting Methodology of the present invention uses combined prediction algorithm to predict industrial user, non-technical family, other users and permanent resident population; Combined prediction algorithm assembles the information that multiple single model comprises, and combines result; The object improved and predict the outcome can be reached, for power supply enterprise carries out formulation marketing program and carry out Electric Power Network Planning providing important evidence more targetedly;
3, Forecasting Methodology of the present invention introduces power consumption growth factor per capita, objectively responds the development along with society, the fact that individual increases electricity consumption, and it is more accurate to make the prediction of resident's power consumption and commercial user's power consumption.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of Forecasting Methodology of the present invention.
Embodiment
Below in conjunction with accompanying drawing and embodiment, the present invention is described in further detail.
Embodiment one
The present embodiment is in conjunction with the medium-term and long-term electricity demand forecasting method of demographic indicator, and its flow process as shown in Figure 1; Comprise the steps:
The first step, obtain respectively area to be predicted in passing 3 years every year resident's power consumption
commercial user's power consumption
industrial user's power consumption
non-technical family power consumption
other user power utilization amounts
with permanent resident population's amount R
i-n; Wherein, i is the time to be predicted; N ∈ [1, k]; K is integer and k>=3;
Second step, calculated toward in k every year resident's power consumption and commercial user's power consumption sum
Calculated toward power consumption q ' per capita in every year in k:
Calculate year to be predicted power consumption predicted value q per capita:
Q=(1+ α) × q'; Wherein, α is power consumption growth factor per capita; For improving the precision predicted the outcome, gather historical data statistics, the span obtaining α is preferably :-5%≤α≤20%.
3rd step, adopt prediction algorithm in passing k every year permanent resident population's amount R
i-nprocess, obtain annual permanent resident population's quantitative forecast value R to be predicted
i; Particularly, what adopt Linear regression, weighted fitting rectilineal equation, cumulative linear fitting, grey forecasting model, hyperbolic model, logarithmic curve model, sigmoid curve model and fall in exponential model two or morely carries out the corresponding annual permanent resident population's quantitative forecast reference value to be predicted of process acquisition respectively; Annual permanent resident population's quantitative forecast reference value to be predicted is carried out arithmetic mean to obtain annual permanent resident population's quantitative forecast value R to be predicted
i;
Calculate the predicted value of annual resident's power consumption to be predicted and commercial user's power consumption sum
4th step, adopt prediction algorithm respectively in passing k every year industrial user's power consumption
non-technical family power consumption
with other user power utilization amounts
process, obtain annual industrial user's electricity demand forecasting value to be predicted
non-technical family electricity demand forecasting value
with other user power utilization amount predicted values
Particularly, what adopt Linear regression, weighted fitting rectilineal equation, cumulative linear fitting, grey forecasting model, hyperbolic model, logarithmic curve model, sigmoid curve model and fall in exponential model two or morely processes respectively, obtains corresponding annual industrial user's electricity demand forecasting reference value to be predicted, non-technical family electricity demand forecasting reference value and other user power utilization amount prediction reference;
Respectively annual industrial user's electricity demand forecasting reference value to be predicted, non-technical family electricity demand forecasting reference value and other user power utilization amount prediction reference are carried out arithmetic mean, to obtain annual industrial user's electricity demand forecasting value to be predicted
non-technical family electricity demand forecasting value
with other user power utilization amount predicted values
5th step, calculates year to be predicted total power quantity predicting value Q
i:
Forecasting Methodology of the present invention can reduce the complexity of algorithm, reduces demand data amount, improves operability widely.From permanent resident population's quantitative relation level of intimate, different prediction and calculation methods is adopted to the power consumer of different level of intimate according to power consumer; Due to resident's power consumption and commercial user's power consumption and permanent resident population's quantitative relation close, industrial user, non-technical family and other users are then little with permanent resident population's quantitative relation; Therefore in the forecasting process of resident's power consumption and commercial user's power consumption in conjunction with permanent resident population's quantity factor, effectively hold the key point of all kinds power consumer electricity demand forecasting; The precision that predicts the outcome is high, can be power supply enterprise and provides marketing decision-making support, ensures power grid security economical operation and electricity market sane development.In Forecasting Methodology of the present invention, k can get 3, adopts the area to be predicted data of passing 3 years as data basis; K also can get >=integer of 4, and adopt the area to be predicted data of passing more than 4 years as data basis.
Because industrial user, non-technical family and other users are then little with permanent resident population's quantitative relation, therefore above-mentioned prediction algorithm can be adopted.Eight kinds of arithmetic result such as Linear regression are stablized, and are applicable to index that is few to data volume, that do not have cyclic swing and carry out prediction and calculation; Adopt many algorithms to carry out annual industrial user's electricity demand forecasting reference value to be predicted, non-technical family electricity demand forecasting reference value and other user power utilization amount prediction reference, carry out arithmetic mean more afterwards, high-precision predicting the outcome can be obtained.
Embodiment two
The present embodiment is in conjunction with the medium-term and long-term electricity demand forecasting method of demographic indicator and the difference of embodiment one: in the present embodiment, the 3rd step, adopt prediction algorithm in passing k every year permanent resident population's amount R
i-ncarry out process to refer to, adopt Linear regression, weighted fitting rectilineal equation, cumulative linear fitting, grey forecasting model, hyperbolic model, logarithmic curve model, sigmoid curve model and all prediction algorithms fallen in exponential model to process respectively, obtain eight annual permanent resident population's quantitative forecast reference values to be predicted; Eight annual permanent resident population's quantitative forecast reference values to be predicted are carried out arithmetic mean to obtain annual permanent resident population's quantitative forecast value R to be predicted
i.
In 4th step, adopt prediction algorithm respectively in passing k every year industrial user's power consumption
non-technical family power consumption
with other user power utilization amounts
carry out process to refer to, adopt Linear regression, weighted fitting rectilineal equation, cumulative linear fitting, grey forecasting model, hyperbolic model, logarithmic curve model, sigmoid curve model and all prediction algorithms fallen in exponential model to process respectively, obtain eight industrial user's electricity demand forecasting reference values in year to be predicted, eight non-technical family electricity demand forecasting reference values and eight other user power utilization amount prediction reference respectively; Respectively eight industrial user's electricity demand forecasting reference values, eight non-technical family electricity demand forecasting reference values and eight other user power utilization amount prediction reference are carried out arithmetic mean, to obtain annual industrial user's electricity demand forecasting value to be predicted
non-technical family electricity demand forecasting value
with other user power utilization amount predicted values
Other flow process of the present embodiment is identical with embodiment one.
Above-described embodiment is the present invention's preferably embodiment; but embodiments of the present invention are not restricted to the described embodiments; change, the modification done under other any does not deviate from Spirit Essence of the present invention and principle, substitute, combine, simplify; all should be the substitute mode of equivalence, be included within protection scope of the present invention.
Claims (4)
1., in conjunction with a medium-term and long-term electricity demand forecasting method for demographic indicator, it is characterized in that, comprise the steps:
The first step, to obtain in the passing k in area to be predicted resident's power consumption in every year respectively
commercial user's power consumption
industrial user's power consumption
non-technical family power consumption
other user power utilization amounts
with permanent resident population's amount R
i-n; Wherein, i is the time to be predicted; N ∈ [1, k]; K is integer and k>=3;
Second step, calculated toward in k every year resident's power consumption and commercial user's power consumption sum
Calculated toward power consumption q ' per capita in every year in k:
Calculate year to be predicted power consumption predicted value q per capita:
Q=(1+ α) × q'; Wherein, α is power consumption growth factor per capita;
3rd step, adopt prediction algorithm in passing k every year permanent resident population's amount R
i-nprocess, obtain annual permanent resident population's quantitative forecast value R to be predicted
i; Calculate the predicted value of annual resident's power consumption to be predicted and commercial user's power consumption sum
4th step, adopt prediction algorithm respectively in passing k every year industrial user's power consumption
non-technical family power consumption
with other user power utilization amounts
process, obtain annual industrial user's electricity demand forecasting value to be predicted
non-technical family electricity demand forecasting value
with other user power utilization amount predicted values
5th step, calculates year to be predicted total power quantity predicting value Q
i:
2. the medium-term and long-term electricity demand forecasting method in conjunction with demographic indicator according to claim 1, is characterized in that, in described 3rd step, adopt prediction algorithm in passing k every year permanent resident population's amount R
i-ncarry out process to refer to, what adopt Linear regression, weighted fitting rectilineal equation, cumulative linear fitting, grey forecasting model, hyperbolic model, logarithmic curve model, sigmoid curve model and fall in exponential model two or morely carries out the corresponding annual permanent resident population's quantitative forecast reference value to be predicted of process acquisition respectively; Annual permanent resident population's quantitative forecast reference value to be predicted is carried out arithmetic mean to obtain annual permanent resident population's quantitative forecast value R to be predicted
i.
3. the medium-term and long-term electricity demand forecasting method in conjunction with demographic indicator according to claim 1, is characterized in that, in described 4th step, adopt prediction algorithm respectively in passing k every year industrial user's power consumption
non-technical family power consumption
with other user power utilization amounts
carry out process to refer to, what adopt Linear regression, weighted fitting rectilineal equation, cumulative linear fitting, grey forecasting model, hyperbolic model, logarithmic curve model, sigmoid curve model and fall in exponential model two or morely processes respectively, obtains corresponding annual industrial user's electricity demand forecasting reference value to be predicted, non-technical family electricity demand forecasting reference value and other user power utilization amount prediction reference;
Respectively annual industrial user's electricity demand forecasting reference value to be predicted, non-technical family electricity demand forecasting reference value and other user power utilization amount prediction reference are carried out arithmetic mean, to obtain annual industrial user's electricity demand forecasting value to be predicted
non-technical family electricity demand forecasting value
with other user power utilization amount predicted values
4. the medium-term and long-term electricity demand forecasting method in conjunction with demographic indicator according to any one of claim 1 to 3, it is characterized in that, the span of described α is :-5%≤α≤20%.
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| CN109858773A (en) * | 2019-01-07 | 2019-06-07 | 国网吉林省电力有限公司长春供电公司 | The urban power network planning method of multi-source big data fusion |
| CN112036777A (en) * | 2020-11-05 | 2020-12-04 | 江苏智臻能源科技有限公司 | Determination method for measuring and calculating user types based on power consumption of air conditioner and water heater |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106022546A (en) * | 2016-06-30 | 2016-10-12 | 中国电力科学研究院 | Load prediction method based on load growth period of residential cell |
| CN106022546B (en) * | 2016-06-30 | 2022-03-08 | 中国电力科学研究院 | Load prediction method based on residential community load growth cycle |
| CN106651058A (en) * | 2017-01-10 | 2017-05-10 | 广东电网有限责任公司中山供电局 | Optimization method and device for electricity consumption prediction model |
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| CN112036777A (en) * | 2020-11-05 | 2020-12-04 | 江苏智臻能源科技有限公司 | Determination method for measuring and calculating user types based on power consumption of air conditioner and water heater |
| CN112036777B (en) * | 2020-11-05 | 2021-03-16 | 江苏智臻能源科技有限公司 | Determination method for measuring and calculating user types based on power consumption of air conditioner and water heater |
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