CN116384548A - Environmental feedback-based power grid short-term power load prediction method - Google Patents
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Abstract
The invention provides a power grid short-term power load prediction method based on environmental feedback, which comprises the following steps: acquiring power grid information of a user in the using process, receiving the power grid information by a power grid analysis module, and analyzing based on the power grid information to obtain power operation parameters; the load information storage module stores the electric power operation parameters obtained through analysis and transmits the electric power operation parameters to the electric load calculation module; the electric load calculation module calculates electric power operation reference data and transmits the calculated electric power operation reference data to the load prediction module; the load prediction module predicts the power load according to the received power operation reference data to obtain prediction data; the predicted data obtained by prediction is transmitted to a power utilization control module for power utilization control; according to the power grid operation information prediction method, a plurality of temperature intervals are set based on the environment information, statistics is carried out on the power grid operation information in different temperature intervals, power prediction is carried out according to the environment, and the accuracy of power prediction is improved.
Description
Technical Field
The invention relates to the technical field of power load prediction, in particular to a power grid short-term power load prediction method based on environmental feedback.
Background
Electric loads, also known as "electrical loads". The sum of the electric power taken by the electric equipment of the electric energy user to the electric power system at a certain moment is called an electric load, and the electric load can be divided into various industrial loads, agricultural loads, transportation loads, life electric loads and the like according to different load characteristics of the electric energy user. The total load of the power system is the sum of the total power consumption of all electric equipment in the system; adding the power consumed by industry, agriculture, post and telecommunications, traffic, municipal administration, business and urban and rural residents to obtain the comprehensive electricity load of the electric power system; the power which is consumed by the combined electric load and the network is the power which should be supplied by each power plant in the system, and is called the power supply load (power supply amount) of the power system; the power supply load is added with the power consumed by each power plant (namely, the station service power), namely, the power which is supposed to be generated by each generator in the system, and the power is called the power generation load (power generation capacity) of the system.
In the prior art, in the process of predicting the power load, the power load is usually predicted and analyzed according to the power consumption condition of the power grid, and the prediction is inaccurate due to the influence of the environment on the power load under different environments, so that the prediction effect is influenced.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a power grid short-term power load prediction method based on environmental feedback.
In order to achieve the above object, the present invention is realized by the following technical scheme: a method of grid short-term power load prediction based on environmental feedback, the power load prediction comprising the steps of:
step S1: the information acquisition module acquires power grid information of a user in the using process, the power grid information is transmitted to the power grid analysis module, the power grid analysis module receives the power grid information, and analysis is carried out based on the power grid information to obtain power operation parameters;
step S2: the load information storage module stores the electric power operation parameters obtained through analysis, and transmits the electric power operation parameters stored in the time period T to the electric load calculation module;
step S3: the electric load calculation module receives the electric power operation parameters to calculate electric power operation reference data, and the calculated electric power operation reference data are transmitted to the load prediction module;
step S4: the load prediction module predicts the power load according to the received power operation reference data to obtain prediction data; and transmitting the predicted data obtained by prediction to a power utilization control module for power utilization control.
Further, the grid information includes power operation information and environmental information;
the power grid analysis module receives the power operation information and the environment information for analysis, and the specific analysis steps are as follows:
step S11: acquiring power operation information in a time period T; the data analysis module acquires the power operation information in the time period T, the power use time value, the power value and the power user value in the power operation information, and the temperature value in the environment information;
step S12: the value of the user is set as follows: YDHSz; calculating the electricity consumption according to the time value and the power value; setting the number of years included in the T time period as n years, acquiring the temperature value in the n years, and acquiring the minimum temperature value as follows: WDSZmin: the maximum temperature value is: WDSZmax: obtaining a difference value between a maximum temperature and a minimum temperature, wherein the set temperature difference value is as follows: WDCz; generating a temperature interval according to the acquired difference value, and respectively setting a first temperature interval, a second temperature interval, a third temperature interval, a fourth temperature interval and a fifth temperature interval;
step S13: counting the days of a first temperature interval, a second temperature interval, a third temperature interval, a fourth temperature interval and a fifth temperature interval in the first year respectively to obtain the electricity consumption of a first electricity user in the first temperature interval, the electricity consumption of a second electricity user in the first temperature interval and the electricity consumption of a third electricity user in the first temperature interval … … YDSsz electricity consumption of the third electricity user in the first temperature interval;
step S14: calculating the average value of the power consumption level of each interval according to the power consumption; obtaining a level average value for a first interval a1, a level average value for a second interval a1, a level average value for a third interval a1, a level average value for a fourth interval a1 and a level average value for a fifth interval a 1;
step S15: thus, the electricity consumption amounts in the second and third years … … and n-th years are respectively different in temperature intervals.
Further, in the step S12, the first temperature interval threshold is [ WDSZmin, WDSZmin +wdcz/5]; the second temperature interval threshold is (WDSZmin+WDCz/5, 2× (WDSZmin+WDCz)/5), the third temperature interval threshold is (2× (WDSZmin+WDCz)/5, 3× (WDSZmin+WDCz)/5, the fourth temperature interval threshold is (3×WDSZmin+WDCz/5,4× (WDSZmin+WDCz)/5), and the fifth temperature interval threshold is (4× (WDSZmin+WDCz)/5, WDSZmax ].
Further, in the step S3, when the electrical load calculation module calculates, the specific steps are as follows:
step S31: the electric load calculation module receives the average value of the power utilization level of each interval in the electric power operation parameters; the method comprises the steps of acquiring a level average value for a first interval a1, a level average value for a first interval a2, a level average value … … for a first interval a3, and acquiring a power consumption change value of a first temperature interval in each year;
step S32: the method comprises the steps of acquiring a value in a level average value for a second interval a1, a level average value for a second interval a2, a level average value … … and a level average value for a second interval an, and acquiring a power consumption change value of a second temperature interval in each year;
step S33: the method comprises the steps of acquiring a value in a level average value for a third interval a1, a level average value for a third interval a2, a level average value … … and a level average value for a third interval an, and acquiring a power consumption change value of a third temperature interval in each year;
step S34: the method comprises the steps of acquiring a value in a level average value for a fourth interval a1, a level average value for a fourth interval a2, a level average value … … and a level average value for a fourth interval an, and acquiring a power consumption change value of a fourth temperature interval in each year;
step S35: the method comprises the steps that a level average value is used for a fifth interval a1, a level average value is used for a fifth interval a2, a value in the level average value is used for a fifth interval a3, the level average value … … and the value in the level average value is used for a fifth interval an, and a power consumption change value of a fifth temperature interval in each year is obtained;
step S36: and defining the acquired electric quantity change value as electric power operation reference data, and transmitting the electric power operation reference data to a load prediction module.
Further, in the step S15, when the electricity consumption in the different temperature intervals in the second year and the third year … … n is obtained, the specific steps are as follows:
obtaining a level average value for a first interval a2, a level average value for a second interval a2, a level average value for a third interval a2, a level average value for a fourth interval a2 and a level average value for a fifth interval a2 in the second year;
……
in the nth year, the level average value for a first interval an, the level average value for a second interval an, the level average value for a third interval an, the level average value for a fourth interval an and the level average value for a fifth interval an are obtained; and defining the acquired average value of the power level of each interval as a power operation parameter.
Further, the load prediction module performs power load prediction as follows:
the load prediction module obtains a temperature change value of a first section, a temperature change value of a second section, a temperature change value of a third section, a temperature change value of a fourth section and a temperature change value of a fifth section in the power operation reference data; the power load of the next year is predicted based on the temperature change value.
The invention has the beneficial effects that:
1. according to the power grid information prediction method and the power grid information prediction device, based on the power grid information obtained by a user in the using process, the power operation information and the environment information are analyzed according to the power grid information, a plurality of temperature intervals are set according to the environment information, the power grid operation information in different temperature intervals is counted, the power prediction is carried out according to the environment, and the accuracy of the power prediction is improved.
2. According to the method, the power operation information in a certain time period is obtained, the change information of the power operation information due to different environments is obtained, the power operation is comprehensively judged and analyzed based on the environment change, and then the short-term power load of the power grid is predicted.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a method step diagram of a grid short-term power load prediction method based on environmental feedback according to the present invention;
fig. 2 is a schematic block diagram of a method for predicting short-term power load of a power grid based on environmental feedback.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
In the invention, referring to fig. 1 and 2, a method for predicting short-term power load of a power grid based on environmental feedback includes an information acquisition module, a power grid analysis module, an electric load calculation module, a load prediction module, a power consumption control module, a load information storage module and a server; the information acquisition module, the power grid analysis module, the electric load calculation module, the load prediction module, the power utilization control module and the load information storage module are respectively connected with the server in a data mode;
the information acquisition module acquires power grid information of a user in the using process, the power grid information is transmitted to the power grid analysis module, the power grid analysis module receives the power grid information, and analysis is carried out based on the power grid information to obtain power operation parameters;
in this embodiment, the grid information includes power operation information and environmental information;
the power grid analysis module receives the power operation information and the environment information for analysis, and the specific analysis is as follows:
acquiring power operation information in a time period T;
the data analysis module acquires the power operation information in the time period T, the power use time value, the power value and the power user value in the power operation information, and the temperature value in the environment information;
the value of the user is set as follows: YDHSz; calculating the electricity consumption according to the time value and the power value;
setting the number of years included in the T time period as n years, acquiring the temperature value in the n years, and acquiring the minimum temperature value as follows: WDSZmin: the maximum temperature value is: WDSZmax:
obtaining a difference value between a maximum temperature and a minimum temperature, wherein the set temperature difference value is as follows: WDCz; generating a temperature interval according to the acquired difference value, and setting a first temperature interval threshold value to be [ WDSZmin, WDSZmin +WDCz/5]; the second temperature interval threshold is (WDSZmin+WDCz/5, 2× (WDSZmin+WDCz)/5), the third temperature interval threshold is (2× (WDSZmin+WDCz)/5, 3× (WDSZmin+WDCz)/5, the fourth temperature interval threshold is (3×WDSZmin+WDCz/5,4× (WDSZmin+WDCz)/5), and the fifth temperature interval threshold is (4× (WDSZmin+WDCz)/5, WDSZmax);
counting the days of a first temperature interval, a second temperature interval, a third temperature interval, a fourth temperature interval and a fifth temperature interval in the first year respectively to obtain the electricity consumption of a first electricity user in the first temperature interval, the electricity consumption of a second electricity user in the first temperature interval and the electricity consumption of a third electricity user in the first temperature interval … … YDSsz electricity consumption of the third electricity user in the first temperature interval;
calculating the average value of the power consumption level of each interval according to the power consumption; obtaining a level average value for a first interval a1, a level average value for a second interval a1, a level average value for a third interval a1, a level average value for a fourth interval a1 and a level average value for a fifth interval a 1;
thus, the electricity consumption in the second year and the third year … … are respectively in different temperature intervals;
obtaining a level average value for a first interval a2, a level average value for a second interval a2, a level average value for a third interval a2, a level average value for a fourth interval a2 and a level average value for a fifth interval a2 in the second year;
……
in the nth year, the level average value for a first interval an, the level average value for a second interval an, the level average value for a third interval an, the level average value for a fourth interval an and the level average value for a fifth interval an are obtained;
defining the obtained average value of the power level of each interval as a power operation parameter;
it should be noted that: the time period T represents a time unit, and can be valued for 2 years, 3 years or 5 years when specific value is taken;
the load information storage module stores the electric power operation parameters obtained through analysis, and transmits the electric power operation parameters stored in the time period T to the electric load calculation module; the electric load calculation module receives the electric power operation parameters to calculate electric power operation reference data, and the calculated electric power operation reference data are transmitted to the load prediction module;
the electric load calculation module receives the average value of the power utilization level of each interval in the electric power operation parameters;
the method comprises the steps of acquiring a level average value for a first interval a1, a level average value for a first interval a2, a level average value … … for a first interval a3, and acquiring a power consumption change value of a first temperature interval in each year;
the method comprises the steps of acquiring a value in a level average value for a second interval a1, a level average value for a second interval a2, a level average value … … and a level average value for a second interval an, and acquiring a power consumption change value of a second temperature interval in each year;
the method comprises the steps of acquiring a value in a level average value for a third interval a1, a level average value for a third interval a2, a level average value … … and a level average value for a third interval an, and acquiring a power consumption change value of a third temperature interval in each year;
the method comprises the steps of acquiring a value in a level average value for a fourth interval a1, a level average value for a fourth interval a2, a level average value … … and a level average value for a fourth interval an, and acquiring a power consumption change value of a fourth temperature interval in each year;
the method comprises the steps that a level average value is used for a fifth interval a1, a level average value is used for a fifth interval a2, a value in the level average value is used for a fifth interval a3, the level average value … … and the value in the level average value is used for a fifth interval an, and a power consumption change value of a fifth temperature interval in each year is obtained;
defining the acquired electric quantity change value as electric power operation reference data, and transmitting the electric power operation reference data to a load prediction module;
the load prediction module predicts the power load according to the received power operation reference data to obtain prediction data;
the load prediction module obtains a temperature change value of a first section, a temperature change value of a second section, a temperature change value of a third section, a temperature change value of a fourth section and a temperature change value of a fifth section in the power operation reference data; predicting the power load of the next year based on the temperature change value;
and transmitting the predicted data obtained by prediction to a power utilization control module for power utilization control.
The invention discloses a power grid short-term power load prediction method based on environmental feedback, which specifically comprises the following steps when power load prediction is carried out:
step S1: the information acquisition module acquires power grid information of a user in the using process, the power grid information is transmitted to the power grid analysis module, the power grid analysis module receives the power grid information, and analysis is carried out based on the power grid information to obtain power operation parameters;
the power grid information comprises power operation information and environment information;
the power grid analysis module receives the power operation information and the environment information for analysis, and the specific analysis steps are as follows:
step S11: acquiring power operation information in a time period T; the data analysis module acquires the power operation information in the time period T, the power use time value, the power value and the power user value in the power operation information, and the temperature value in the environment information;
step S12: the value of the user is set as follows: YDHSz; calculating the electricity consumption according to the time value and the power value; setting the number of years included in the T time period as n years, acquiring the temperature value in the n years, and acquiring the minimum temperature value as follows: WDSZmin: the maximum temperature value is: WDSZmax: obtaining a difference value between a maximum temperature and a minimum temperature, wherein the set temperature difference value is as follows: WDCz; generating a temperature interval according to the acquired difference value, and setting a first temperature interval threshold value to be [ WDSZmin, WDSZmin +WDCz/5]; the second temperature interval threshold is (WDSZmin+WDCz/5, 2× (WDSZmin+WDCz)/5), the third temperature interval threshold is (2× (WDSZmin+WDCz)/5, 3× (WDSZmin+WDCz)/5, the fourth temperature interval threshold is (3×WDSZmin+WDCz/5,4× (WDSZmin+WDCz)/5), and the fifth temperature interval threshold is (4× (WDSZmin+WDCz)/5, WDSZmax);
step S13: counting the days of a first temperature interval, a second temperature interval, a third temperature interval, a fourth temperature interval and a fifth temperature interval in the first year respectively to obtain the electricity consumption of a first electricity user in the first temperature interval, the electricity consumption of a second electricity user in the first temperature interval and the electricity consumption of a third electricity user in the first temperature interval … … YDSsz electricity consumption of the third electricity user in the first temperature interval;
step S14: calculating the average value of the power consumption level of each interval according to the power consumption; obtaining a level average value for a first interval a1, a level average value for a second interval a1, a level average value for a third interval a1, a level average value for a fourth interval a1 and a level average value for a fifth interval a 1;
step S15: thus, the electricity consumption in the second year and the third year … … are respectively in different temperature intervals;
obtaining a level average value for a first interval a2, a level average value for a second interval a2, a level average value for a third interval a2, a level average value for a fourth interval a2 and a level average value for a fifth interval a2 in the second year;
……
in the nth year, the level average value for a first interval an, the level average value for a second interval an, the level average value for a third interval an, the level average value for a fourth interval an and the level average value for a fifth interval an are obtained; defining the obtained average value of the power level of each interval as a power operation parameter;
step S2: the load information storage module stores the electric power operation parameters obtained through analysis, and transmits the electric power operation parameters stored in the time period T to the electric load calculation module;
step S3: the electric load calculation module receives the electric power operation parameters to calculate electric power operation reference data, and the calculated electric power operation reference data are transmitted to the load prediction module;
when the electric load calculation module calculates, the specific steps are as follows:
step S31: the electric load calculation module receives the average value of the power utilization level of each interval in the electric power operation parameters; the method comprises the steps of acquiring a level average value for a first interval a1, a level average value for a first interval a2, a level average value … … for a first interval a3, and acquiring a power consumption change value of a first temperature interval in each year;
step S32: the method comprises the steps of acquiring a value in a level average value for a second interval a1, a level average value for a second interval a2, a level average value … … and a level average value for a second interval an, and acquiring a power consumption change value of a second temperature interval in each year;
step S33: the method comprises the steps of acquiring a value in a level average value for a third interval a1, a level average value for a third interval a2, a level average value … … and a level average value for a third interval an, and acquiring a power consumption change value of a third temperature interval in each year;
step S34: the method comprises the steps of acquiring a value in a level average value for a fourth interval a1, a level average value for a fourth interval a2, a level average value … … and a level average value for a fourth interval an, and acquiring a power consumption change value of a fourth temperature interval in each year;
step S35: the method comprises the steps that a level average value is used for a fifth interval a1, a level average value is used for a fifth interval a2, a value in the level average value is used for a fifth interval a3, the level average value … … and the value in the level average value is used for a fifth interval an, and a power consumption change value of a fifth temperature interval in each year is obtained;
step S36: defining the acquired electric quantity change value as electric power operation reference data, and transmitting the electric power operation reference data to a load prediction module;
step S4: the load prediction module predicts the power load according to the received power operation reference data to obtain prediction data; and transmitting the predicted data obtained by prediction to a power utilization control module for power utilization control.
The load prediction module is used for predicting the power load, and specifically comprises the following steps:
the load prediction module obtains a temperature change value of a first section, a temperature change value of a second section, a temperature change value of a third section, a temperature change value of a fourth section and a temperature change value of a fifth section in the power operation reference data; the power load of the next year is predicted based on the temperature change value.
The above formulas are all formulas for removing dimensions and taking numerical calculation, the formulas are formulas for obtaining the latest real situation by collecting a large amount of data and performing software simulation, preset parameters in the formulas are set by a person skilled in the art according to the actual situation, if weight coefficients and proportion coefficients exist, the set sizes are specific numerical values obtained by quantizing the parameters, the subsequent comparison is convenient, and the proportional relation between the weight coefficients and the proportion coefficients is not influenced as long as the proportional relation between the parameters and the quantized numerical values is not influenced.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied therein.
The above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. A method for predicting short-term power load of a power grid based on environmental feedback, wherein the power load prediction comprises the following steps:
step S1: the information acquisition module acquires power grid information of a user in the using process, the power grid information is transmitted to the power grid analysis module, the power grid analysis module receives the power grid information, and analysis is carried out based on the power grid information to obtain power operation parameters;
step S2: the load information storage module stores the electric power operation parameters obtained through analysis, and transmits the electric power operation parameters stored in the time period T to the electric load calculation module;
step S3: the electric load calculation module receives the electric power operation parameters to calculate electric power operation reference data, and the calculated electric power operation reference data are transmitted to the load prediction module;
step S4: the load prediction module predicts the power load according to the received power operation reference data to obtain prediction data; and transmitting the predicted data obtained by prediction to a power utilization control module for power utilization control.
2. The method for predicting short-term power load of a power grid based on environmental feedback of claim 1, wherein the power grid information comprises power operation information and environmental information;
the power grid analysis module receives the power operation information and the environment information for analysis, and the specific analysis steps are as follows:
step S11: acquiring power operation information in a time period T; the data analysis module acquires the power operation information in the time period T, the power use time value, the power value and the power user value in the power operation information, and the temperature value in the environment information;
step S12: the value of the user is set as follows: YDHSz; calculating the electricity consumption according to the time value and the power value; setting the number of years included in the T time period as n years, acquiring the temperature value in the n years, and acquiring the minimum temperature value as follows: WDSZmin: the maximum temperature value is: WDSZmax: obtaining a difference value between a maximum temperature and a minimum temperature, wherein the set temperature difference value is as follows: WDCz; generating a temperature interval according to the acquired difference value, and respectively setting a first temperature interval, a second temperature interval, a third temperature interval, a fourth temperature interval and a fifth temperature interval;
step S13: counting the days of a first temperature interval, a second temperature interval, a third temperature interval, a fourth temperature interval and a fifth temperature interval in the first year respectively to obtain the electricity consumption of a first electricity user in the first temperature interval, the electricity consumption of a second electricity user in the first temperature interval and the electricity consumption of a third electricity user in the first temperature interval … … YDSsz electricity consumption of the third electricity user in the first temperature interval;
step S14: calculating the average value of the power consumption level of each interval according to the power consumption; obtaining a level average value for a first interval a1, a level average value for a second interval a1, a level average value for a third interval a1, a level average value for a fourth interval a1 and a level average value for a fifth interval a 1;
step S15: thus, the electricity consumption amounts in the second and third years … … and n-th years are respectively different in temperature intervals.
3. The method for predicting short-term power load of a power grid based on environmental feedback according to claim 2, wherein in the step S12, the first temperature interval threshold is [ WDSZmin, WDSZmin +wdcz/5]; the second temperature interval threshold is (WDSZmin+WDCz/5, 2× (WDSZmin+WDCz)/5), the third temperature interval threshold is (2× (WDSZmin+WDCz)/5, 3× (WDSZmin+WDCz)/5, the fourth temperature interval threshold is (3×WDSZmin+WDCz/5,4× (WDSZmin+WDCz)/5), and the fifth temperature interval threshold is (4× (WDSZmin+WDCz)/5, WDSZmax ].
4. The method for predicting the short-term power load of a power grid based on environmental feedback according to claim 1, wherein in the step S3, the power load calculation module performs the following specific steps:
step S31: the electric load calculation module receives the average value of the power utilization level of each interval in the electric power operation parameters; the method comprises the steps of acquiring a level average value for a first interval a1, a level average value for a first interval a2, a level average value … … for a first interval a3, and acquiring a power consumption change value of a first temperature interval in each year;
step S32: the method comprises the steps of acquiring a value in a level average value for a second interval a1, a level average value for a second interval a2, a level average value … … and a level average value for a second interval an, and acquiring a power consumption change value of a second temperature interval in each year;
step S33: the method comprises the steps of acquiring a value in a level average value for a third interval a1, a level average value for a third interval a2, a level average value … … and a level average value for a third interval an, and acquiring a power consumption change value of a third temperature interval in each year;
step S34: the method comprises the steps of acquiring a value in a level average value for a fourth interval a1, a level average value for a fourth interval a2, a level average value … … and a level average value for a fourth interval an, and acquiring a power consumption change value of a fourth temperature interval in each year;
step S35: the method comprises the steps that a level average value is used for a fifth interval a1, a level average value is used for a fifth interval a2, a value in the level average value is used for a fifth interval a3, the level average value … … and the value in the level average value is used for a fifth interval an, and a power consumption change value of a fifth temperature interval in each year is obtained;
step S36: and defining the acquired electric quantity change value as electric power operation reference data, and transmitting the electric power operation reference data to a load prediction module.
5. The method for predicting the short-term power load of the power grid based on the environmental feedback according to claim 2, wherein in the step S15, when the power consumption in the different temperature intervals in the second year and the third year … … n is obtained, the specific steps are as follows:
obtaining a level average value for a first interval a2, a level average value for a second interval a2, a level average value for a third interval a2, a level average value for a fourth interval a2 and a level average value for a fifth interval a2 in the second year;
……
in the nth year, the level average value for a first interval an, the level average value for a second interval an, the level average value for a third interval an, the level average value for a fourth interval an and the level average value for a fifth interval an are obtained; and defining the acquired average value of the power level of each interval as a power operation parameter.
6. The method for predicting short-term power load of a power grid based on environmental feedback of claim 4, wherein the load prediction module performs power load prediction as follows:
the load prediction module obtains a temperature change value of a first section, a temperature change value of a second section, a temperature change value of a third section, a temperature change value of a fourth section and a temperature change value of a fifth section in the power operation reference data; the power load of the next year is predicted based on the temperature change value.
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