CN117273802B - Medium-short term electricity sales quantity prediction method based on deep neural network - Google Patents
Medium-short term electricity sales quantity prediction method based on deep neural network Download PDFInfo
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Abstract
The invention discloses a method for predicting middle-short-term electricity sales based on a deep neural network, which relates to the technical field of electricity sales data processing and comprises the following steps: acquiring an original sample set of each area; performing data cleaning on the original sample set; preprocessing the cleaned data to obtain a preprocessed sample set; the management center carries out prediction optimization coefficient YH analysis on the pretreatment sample sets of each platform region to obtain a prediction optimization sequence of the pretreatment sample sets, and data processing efficiency is improved; after receiving the pretreatment sample set, the prediction terminal processes the time sequence in a sequence difference mode, and an LSTM neural network model is established; initializing the weight of a neural network and the parameters of a particle swarm optimization algorithm by adopting an Nguyen-Widry method; performing model training on the neural network model, and performing model evaluation through a loss function to obtain an optimal medium-short-term electricity selling data prediction model with minimum overall error of a training sample; and the data prediction accuracy is improved.
Description
Technical Field
The invention relates to the technical field of electricity selling data processing, in particular to a medium-short-term electricity selling quantity prediction method based on a deep neural network.
Background
With the development of the electric power market and the improvement of the demands of users, the safe and economic operation of the power grid becomes important. Accurate short-term prediction is carried out on the sales power quantity of the transformer area, so that the safe operation of the power grid can be effectively ensured, the power generation cost is reduced, the user requirements are met, and the social and economic benefits are improved. Because the sales power of the station area has obvious periodic characteristics, and meanwhile, the influence factors are complex, such as the electricity consumption behavior of a user, the load change, the seasonal change, the holiday and the like, the selection of an advanced and accurate medium-short-term sales power prediction method is necessary.
In the electricity sales prediction process, two kinds of neural network models, namely a cyclic neural network (Recurrent Neural Network, RNN) or a Long short-term memory (LSTM), are commonly used. With sufficient time, the RNN/LSTM can meet the requirements of most power segments. However, in the network structure of RNN/LSTM, the current layer input is the output of the previous layer, and all RNN/LSTM are suitable for processing the time series problem, but due to the front-back serial structure, the training speed of the RNN/LSTM model is limited, the parallelization processing cannot be performed, and the modeling efficiency is seriously affected under the conditions of large data volume and short construction period; the support vector method based on the statistical theory has the advantages that the determination of the self-selected parameters and functions is dependent on manual experience, and the prediction effect is also affected. Based on the defects, the invention provides a medium-short-term electricity sales quantity prediction method based on a deep neural network.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a medium-short-term electricity sales quantity prediction method based on a deep neural network.
To achieve the above objective, an embodiment of the first aspect of the present invention provides a method for predicting a medium-short-term sales power based on a deep neural network, including the following steps:
step one: acquiring an original sample set of each area; performing data cleaning on the original sample set; preprocessing the cleaned data to obtain a preprocessed sample set and caching the preprocessed sample set to a management center; the original sample set comprises date data, climate text data and sales volume history data;
step two: the management center analyzes the prediction optimization coefficients YH of the pretreatment sample sets of each platform region, and sorts the pretreatment sample sets according to the sizes of the prediction optimization coefficients YH to obtain a prediction optimization sequence of the pretreatment sample sets;
then, the pretreatment sample sets of each platform area are sequentially sent to a prediction terminal for electricity selling data prediction model training according to the prediction optimization sequence;
step three: after receiving the pretreatment sample set, the prediction terminal splits the pretreatment sample set into a mutually exclusive training set, a mutually exclusive verification set and a mutually exclusive testing set; processing the time sequence in a sequence difference mode, and establishing an LSTM neural network model; the method comprises the steps of designating the number of LSTM input nodes of a long-short-period memory neural network according to the number of input variables, and setting the number of suitable hidden layer nodes and the number of output nodes representing the predicted value of the sales quantity;
step four: initializing the weight of a neural network and the parameters of a particle swarm optimization algorithm; initializing a neural network weight by adopting an Nguyen-widry method; performing model training on the neural network model to obtain a trained medium-short-term electricity selling data prediction model;
step five: after the middle-short-term electricity selling data prediction model of the optimal parameters is obtained, the electricity selling quantity data of the time zone before the time zone to be predicted and the climate text data of the time zone to be predicted are input as models, and the electricity selling quantity prediction value of the time zone to be predicted is obtained.
Further, the management center carries out prediction optimization coefficient YH analysis on the pretreatment sample set of each platform region, and the specific analysis steps are as follows:
obtaining a platform region corresponding to a pretreatment sample set, and obtaining a power supply region of the platform region; counting the length of a power supply line in a power supply area to be L1, the number of power supplies to be HL and the average power consumption of users to be DL;
calculating a region association coefficient GD of the station region by using a formula GD=L1×g2+HL×g3+DL×g4, wherein g2, g3 and g4 are preset coefficient factors;
collecting vending error data of each sampling interval of the platform region in a preset time period; the supply and sales error data are difference data of the supply power quantity and the sales power quantity; the difference value data is positive;
marking the vending error data of each sampling interval as Wi; comparing the vending error data Wi with a preset error threshold value; if Wi is larger than a preset error threshold, the electric energy loss of the corresponding area is larger, the power generation cost is increased, and extra loss is caused to the operation of the power grid;
counting the times of the sale error data Wi > the preset error threshold value as Zb, and when Wi > the preset error threshold value, obtaining the difference value of Wi and the preset error threshold value and summing to obtain a super error total value LZ; calculating a power supply loss coefficient GS of the station area by using a formula GS=Zb×g1+LZ×g5, wherein g1 and g5 are preset coefficient factors;
normalizing the area association coefficient and the power supply loss coefficient and taking the values of the area association coefficient and the power supply loss coefficient, and calculating by using a formula YH= ƒ × (GDXb1+GS Xb 2) to obtain a prediction optimization coefficient YH of the pretreatment sample set, wherein b1 and b2 are preset coefficient factors; ƒ is a preset equalization coefficient.
Further, performing data cleaning on the original sample set, including: filling or discarding the null value; performing de-duplication processing on the repeated data; cleaning climate text data with incorrect ranges; and carrying out data verification on the climate text data.
Further, preprocessing the cleaned data, including:
carrying out unit conversion processing on the electricity selling history data, complementing sampling time points to ensure continuity of the electricity selling history data, and filling missing data of the sampling points by using an average interpolation method to obtain a time sequence of electricity selling quantity of a platform area; if the sampling time point and the data are missing in a large area, filling is carried out by using the data of other years in the same period.
Further, the time sequence is processed by adopting a sequence difference mode, and the specific steps comprise:
according to the month and quarter data of the time sequence, obtaining a moving average value of the sales power of the platform area for 12 months or 4 quarters, and obtaining long-term moving average value trend data Q;
calculating Y/q=s×c×i according to the multiplication model; wherein Y represents year, S represents seasonal component, C represents periodic component, and I represents irregular component;
calculating Y/Q according to the same month or the same quarter of each year again and taking an average value to obtain an arithmetic average Pi of the same month or the same quarter of each year;
calculating to obtain a seasonal ratio Si of each month or quarter by taking an arithmetic mean Pi of the same month or quarter of each year as a molecule and taking the sum of the arithmetic mean of all months or quarters as a denominator, wherein Si= (N×Pi)/(P1+P2+ … +PN); the seasonal ratio Si is a correction coefficient of the seasonal factor to the long-term trend of the sales power; p1+p2+ … +pn is the sum of the arithmetic averages of all months or quarters; n is the number of samples;
calculating to obtain the product of Tt with seasonal factors removed and seasonal Si corresponding to the t period, namely the predicted value of the sales amount corresponding to the t period; wherein Qt is moving average trend data for period t.
Further, the neural network weight comprises at least one of an input value, a hidden value, a model layer number, a neuron discarding rate, an activation function, a characteristic weight initial value and a bias of the model.
Further, performing model training on the neural network model to obtain a trained medium-short-term electricity selling data prediction model, which specifically comprises the following steps:
and inputting the training set, the verification set and the test set as historical characteristic values into the LSTM neural network model to perform model training, and performing model evaluation through a loss function to obtain an optimal medium-short term electricity selling data prediction model which minimizes the overall error of the training sample.
Further, performing model evaluation through a loss function comprises:
adopting a root mean square error equation as a loss function, adopting an R2_score determining coefficient as a model fitting degree evaluating method, and evaluating the loss condition between a model predicted value and a true value;
and determining a trained medium-short-term electricity selling data prediction model according to the loss evaluation result.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention obtains the original sample set of each area; data cleaning is carried out on the original sample set; preprocessing the cleaned data to obtain a preprocessed sample set and caching the preprocessed sample set to a management center; the management center analyzes the prediction optimization coefficient YH of the pretreatment sample set of each station area, and calculates the prediction optimization coefficient YH of the pretreatment sample set by combining the length of a power supply line in a power supply area, the number of power supplies, the average power consumption of users and the sales error data of each sampling interval; sequencing the pretreatment sample set according to the magnitude of the prediction optimization coefficient YH to obtain a prediction optimization sequence of the pretreatment sample set; the data processing efficiency is effectively improved;
2. after receiving the pretreatment sample set, the prediction terminal splits the pretreatment sample set into three mutually exclusive data sets; processing the time sequence in a sequence difference mode, and establishing an LSTM neural network model; initializing the weight of a neural network and the parameters of a particle swarm optimization algorithm by adopting an Nguyen-Widry method; inputting the training set, the verification set and the test set as historical characteristic values into an LSTM neural network model for model training, and carrying out model evaluation through a loss function to obtain an optimal medium-short-term electricity selling data prediction model which minimizes the overall error of a training sample; and the data prediction accuracy is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic block diagram of a method for predicting medium-short-term sales power based on a deep neural network.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a method for predicting middle-short-term electricity sales based on a deep neural network includes the following steps:
step one: acquiring an original sample set of each area; data cleaning is carried out on the original sample set; preprocessing the cleaned data to obtain a preprocessed sample set and caching the preprocessed sample set to a management center; the original sample set comprises date data, climate text data and sales volume history data;
the data cleaning of the original sample set comprises the following steps:
because the historical data of the sales power of the sample area possibly has a small amount of data missing, and the missing of the sampling data can influence VMD decomposition, the invention adopts an average interpolation method to complement the missing data;
preprocessing the cleaned data, including:
carrying out unit conversion processing on the electricity selling history data, complementing sampling time points to ensure continuity of the electricity selling history data, and filling missing data of the sampling points by using an average interpolation method to obtain a time sequence of electricity selling quantity of a platform area; if the sampling time point and the data are missing in a large area, filling by using the data of other years in the same period;
step two: the management center analyzes the prediction optimization coefficients YH of the pretreatment sample sets of each platform region, and sorts the pretreatment sample sets according to the sizes of the prediction optimization coefficients YH to obtain a prediction optimization sequence of the pretreatment sample sets; the specific analysis steps are as follows:
obtaining a platform region corresponding to the pretreatment sample set, and obtaining a power supply region of the platform region; counting the length of a power supply line in a power supply area to be L1, the number of power supplies to be HL and the average power consumption of users to be DL;
calculating a region association coefficient GD of the station region by using a formula GD=L1×g2+HL×g3+DL×g4, wherein g2, g3 and g4 are preset coefficient factors;
collecting sales error data of each sampling interval of the station area in a preset time period; the supply and sales error data are difference data of the supply power quantity and the sales power quantity; taking the positive number of the difference value data;
marking the vending error data of each sampling interval as Wi; comparing the vending error data Wi with a preset error threshold value; the larger the vending error data Wi is, the larger the electric energy loss of the corresponding area is, and the power generation cost is increased; extra loss is caused to the operation of the power grid;
counting the times of the sale error data Wi > the preset error threshold value as Zb, and when Wi > the preset error threshold value, obtaining the difference value of Wi and the preset error threshold value and summing to obtain a super error total value LZ;
calculating a power supply loss coefficient GS of the platform region by using a formula GS=Zb×g1+LZ×g5, wherein g1 and g5 are preset coefficient factors;
normalizing the area association coefficient and the power supply loss coefficient, taking the values of the area association coefficient and the power supply loss coefficient, and calculating by using a formula YH= ƒ × (GDXb1+GS Xb2) to obtain a prediction optimization coefficient YH of the pretreatment sample set, wherein b1 and b2 are preset coefficient factors; ƒ is a preset equalization coefficient;
the management center sequentially sends the pretreatment sample set of each station area to the prediction terminal for electricity selling data prediction model training according to the prediction optimization sequence;
step three: after receiving the pretreatment sample set, the prediction terminal splits the pretreatment sample set into three mutually exclusive data sets; the data set is a training set, a verification set and a test set; processing a time sequence in a sequence difference mode, and establishing an LSTM neural network model, wherein the number of LSTM input nodes of the long-term memory neural network is designated according to the number of input variables, and the number of suitable hidden layer nodes and the number of output nodes representing the sales quantity predicted value are set;
the method for processing the time sequence by adopting the sequence difference mode comprises the following specific steps:
according to the month and quarter data of the time sequence, obtaining a moving average value of the sales power of the platform area for 12 months or 4 quarters, and obtaining long-term moving average value trend data Q;
calculating Y/q=s×c×i according to the multiplication model; wherein Y represents year, S represents seasonal component, C represents periodic component, and I represents irregular component;
calculating Y/Q according to the same month or the same quarter of each year again and taking an average value to obtain an arithmetic average Pi of the same month or the same quarter of each year;
calculating to obtain a seasonal ratio Si of each month or quarter by taking an arithmetic mean Pi of the same month or quarter of each year as a molecule and taking the sum of the arithmetic mean of all months or quarters as a denominator, wherein Si= (N×Pi)/(P1+P2+ … +PN); the seasonal ratio Si is a correction coefficient of the seasonal factor to the long-term trend of the sales power; p1+p2+ … +pn is the sum of the arithmetic averages of all months or quarters; n is the number of samples;
calculating to obtain the product of Tt with seasonal factors removed and seasonal Si corresponding to the t period, namely the predicted value of the sales amount corresponding to the t period; wherein Qt is moving average trend data for period t;
step four: initializing the weight of a neural network and the parameters of a particle swarm optimization algorithm; performing model training on the neural network model to obtain a trained medium-short-term electricity selling data prediction model;
initializing a neural network weight by adopting an Nguyen-widry method; the neural network weight comprises at least one of an input value, a hidden value, a model layer number, a neuron discarding rate, an activation function, a characteristic weight initial value and a bias of a model;
the method comprises the steps of training a neural network model to obtain a trained medium-short-term electricity selling data prediction model, and specifically comprises the following steps:
inputting the training set, the verification set and the test set as historical characteristic values into an LSTM neural network model for model training, and carrying out model evaluation through a loss function to obtain an optimal medium-short-term electricity selling data prediction model which minimizes the overall error of a training sample;
the technical scheme is that: model evaluation by loss function, comprising:
adopting a root mean square error equation as a loss function, adopting an R2_score determining coefficient as a model fitting degree evaluating method, and evaluating the loss condition between a model predicted value and a true value;
determining a trained medium-short-term electricity selling data prediction model according to the loss evaluation result;
step five: after the middle-short-term electricity selling data prediction model of the optimal parameters is obtained, the electricity selling quantity data of the time zone before the time zone to be predicted and the climate text data of the time zone to be predicted are input as models, and the electricity selling quantity prediction value of the time zone to be predicted is obtained.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
a method for predicting middle-short-term sales power based on a deep neural network comprises the steps of obtaining an original sample set of each area when in operation; data cleaning is carried out on the original sample set; preprocessing the cleaned data to obtain a preprocessed sample set and caching the preprocessed sample set to a management center; the management center analyzes the prediction optimization coefficient YH of the pretreatment sample set of each station area, and calculates the prediction optimization coefficient YH of the pretreatment sample set by combining the length of a power supply line in a power supply area, the number of power supplies, the average power consumption of users and the sales error data of each sampling interval; sequencing the pretreatment sample set according to the magnitude of the prediction optimization coefficient YH to obtain a prediction optimization sequence of the pretreatment sample set; the management center sequentially sends the pretreatment sample set of each station area to the prediction terminal for electricity selling data prediction model training according to the prediction optimization sequence; the data processing efficiency is improved;
after receiving the pretreatment sample set, the prediction terminal splits the pretreatment sample set into three mutually exclusive data sets; processing the time sequence in a sequence difference mode, and establishing an LSTM neural network model; initializing the weight of a neural network and the parameters of a particle swarm optimization algorithm by adopting an Nguyen-Widry method; inputting the training set, the verification set and the test set as historical characteristic values into an LSTM neural network model for model training, and carrying out model evaluation through a loss function to obtain an optimal medium-short-term electricity selling data prediction model which minimizes the overall error of a training sample; and the data prediction accuracy is improved.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The medium-short-term electricity sales quantity prediction method based on the deep neural network is characterized by comprising the following steps of:
step one: acquiring an original sample set of each area; performing data cleaning on the original sample set; preprocessing the cleaned data to obtain a preprocessed sample set and caching the preprocessed sample set to a management center; the original sample set comprises date data, climate text data and sales volume history data;
step two: the management center carries out prediction optimization coefficient YH analysis on the pretreatment sample set of each platform region, and the specific analysis steps are as follows:
obtaining a platform region corresponding to a pretreatment sample set, and obtaining a power supply region of the platform region; counting the length of a power supply line in a power supply area to be L1, the number of power supplies to be HL and the average power consumption of users to be DL;
calculating a region association coefficient GD of the station region by using a formula GD=L1×g2+HL×g3+DL×g4, wherein g2, g3 and g4 are preset coefficient factors;
collecting vending error data of each sampling interval of the platform region in a preset time period; the supply and sales error data are difference data of the supply power quantity and the sales power quantity; the difference value data is positive;
marking the vending error data of each sampling interval as Wi; comparing the vending error data Wi with a preset error threshold value; if Wi is larger than a preset error threshold, the power consumption of the corresponding area is large, the power generation cost is increased, and extra loss is caused to the operation of the power grid;
counting the times of the sale error data Wi > the preset error threshold value as Zb, and when Wi > the preset error threshold value, obtaining the difference value of Wi and the preset error threshold value and summing to obtain a super error total value LZ; calculating a power supply loss coefficient GS of the station area by using a formula GS=Zb×g1+LZ×g5, wherein g1 and g5 are preset coefficient factors;
normalizing the area association coefficient and the power supply loss coefficient and taking the values of the area association coefficient and the power supply loss coefficient, and calculating by using a formula YH= ƒ × (GDXb1+GS Xb 2) to obtain a prediction optimization coefficient YH of the pretreatment sample set, wherein b1 and b2 are preset coefficient factors; ƒ is a preset equalization coefficient;
sequencing the pretreatment sample set according to the magnitude of the prediction optimization coefficient YH to obtain a prediction optimization sequence of the pretreatment sample set; then, the pretreatment sample sets of each platform area are sequentially sent to a prediction terminal for electricity selling data prediction model training according to the prediction optimization sequence;
step three: after receiving the pretreatment sample set, the prediction terminal splits the pretreatment sample set into a mutually exclusive training set, a mutually exclusive verification set and a mutually exclusive testing set; processing the time sequence in a sequence difference mode, and establishing an LSTM neural network model; the method comprises the steps of designating the number of LSTM input nodes of a long-short-period memory neural network according to the number of input variables, and setting the number of suitable hidden layer nodes and the number of output nodes representing the predicted value of the sales quantity;
step four: initializing the weight of a neural network and the parameters of a particle swarm optimization algorithm; initializing a neural network weight by adopting an Nguyen-widry method; performing model training on the neural network model to obtain a trained medium-short-term electricity selling data prediction model;
step five: after the middle-short-term electricity selling data prediction model of the optimal parameters is obtained, the electricity selling quantity data of the time zone before the time zone to be predicted and the climate text data of the time zone to be predicted are input as models, and the electricity selling quantity prediction value of the time zone to be predicted is obtained.
2. The method for predicting the medium-short term electricity sales amount based on the deep neural network according to claim 1, wherein the data cleaning of the original sample set comprises the following steps: filling or discarding the null value; performing de-duplication processing on the repeated data; cleaning climate text data with incorrect ranges; and carrying out data verification on the climate text data.
3. The method for predicting the medium-short term electricity sales amount based on the deep neural network according to claim 2, wherein preprocessing the cleaned data comprises the following steps:
carrying out unit conversion processing on the electricity selling history data, complementing sampling time points to ensure continuity of the electricity selling history data, and filling missing data of the sampling points by using an average interpolation method to obtain a time sequence of electricity selling quantity of a platform area; if the sampling time point and the data are missing in a large area, filling is carried out by using the data of other years in the same period.
4. The method for predicting the medium-short-term electricity sales amount based on the deep neural network according to claim 1, wherein the method for processing the time sequence by adopting a sequence difference mode comprises the following specific steps:
according to the month and quarter data of the time sequence, obtaining a moving average value of the sales power of the platform area for 12 months or 4 quarters, and obtaining long-term moving average value trend data Q;
calculating Y/q=s×c×i according to the multiplication model; wherein Y represents year, S represents seasonal component, C represents periodic component, and I represents irregular component;
calculating Y/Q according to the same month or the same quarter of each year again and taking an average value to obtain an arithmetic average Pi of the same month or the same quarter of each year;
calculating to obtain a seasonal ratio Si of each month or quarter by taking an arithmetic mean Pi of the same month or quarter of each year as a molecule and taking the sum of the arithmetic mean of all months or quarters as a denominator, wherein Si= (N×Pi)/(P1+P2+ … +PN); the seasonal ratio Si is a correction coefficient of the seasonal factor to the long-term trend of the sales power; p1+p2+ … +pn is the sum of the arithmetic averages of all months or quarters; n is the number of samples;
calculating to obtain the product of Qt with seasonal factors removed and seasonal Si corresponding to the t period, namely the predicted value of the sales power quantity corresponding to the t period; wherein Qt is moving average trend data for period t.
5. The method for predicting the middle-short term electricity sales based on the deep neural network according to claim 1, wherein the neural network weight comprises at least one of an input value, a hidden value, a model layer number, a neuron discarding rate, an activation function, a characteristic weight initial value and a bias of a model.
6. The method for predicting the medium-short-term electricity sales amount based on the deep neural network according to claim 1, wherein the neural network model is subjected to model training to obtain a trained medium-short-term electricity sales data prediction model, and the method specifically comprises the following steps:
and inputting the training set, the verification set and the test set as historical characteristic values into the LSTM neural network model to perform model training, and performing model evaluation through a loss function to obtain an optimal medium-short term electricity selling data prediction model which minimizes the overall error of the training sample.
7. The method for predicting medium-short term sales power based on deep neural network according to claim 6, wherein the model evaluation by the loss function comprises:
adopting a root mean square error equation as a loss function, adopting an R2_score determining coefficient as a model fitting degree evaluating method, and evaluating the loss condition between a model predicted value and a true value;
and determining a trained medium-short-term electricity selling data prediction model according to the loss evaluation result.
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