Method, device and equipment for predicting electrical load of target area

Abstract

The invention provides an electrical load prediction method, device and equipment for a target area, and relates to the technical field of computer information, and the method comprises the steps: obtaining an electrical load sequence and influence factor data of the target area; performing abnormal value processing on the electric load sequence to obtain an electric load data set; performing correlation analysis on the influence factor data to obtain a factor influence value; splicing the electrical load data set and the factor influence value to obtain an electrical load characteristic value; and inputting the electrical load characteristic value into an electrical load prediction model to carry out electrical load prediction processing, and carrying out modular combination on output values of the electrical load prediction model to obtain an electrical load prediction result of the target area. According to the scheme, the electric load can be accurately predicted, and modular combination of load prediction values of different zero-carbon industrial parks is realized.

Description

Technical Field

[0001] The present invention relates to the field of computer information technology, and in particular to a method, device and equipment for predicting electric load in a target area. Background Art

[0002] As an important carrier of production and life, industrial parks concentrate a large number of industrial enterprises and equipment, and have huge demands for electricity, heat and other energy. With the adjustment of energy structure and the improvement of environmental protection requirements, how to effectively manage the energy system of industrial parks, optimize energy utilization efficiency and reduce carbon emissions has become an important issue that needs to be solved urgently.

[0003] The electricity load in a specific zero-carbon industrial park is affected by factors such as industrial structure, season and climate, electricity price policy, energy substitution, and the application of energy-saving technologies. The accuracy of the prediction results cannot be guaranteed using traditional load forecasting methods. Summary of the invention

[0004] The present invention provides a method, device and equipment for predicting electric load in a target area, so as to solve the problem of inaccurate prediction of electric load in a target area in the prior art.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A method for predicting electric load in a target area, comprising:

[0007] Obtain the electric load sequence and influencing factor data of the target area;

[0008] Performing outlier processing on the electric load sequence to obtain an electric load data set;

[0009] Performing correlation analysis on the influencing factor data to obtain factor influence values;

[0010] The electric load data set and the factor influence value are combined to obtain an electric load characteristic value;

[0011] The electric load characteristic value is input into the electric load prediction model to perform electric load prediction processing, and the output value of the electric load prediction model is modularly combined to obtain the electric load prediction result of the target area.

[0012] Optionally, performing outlier processing on the electric load sequence to obtain an electric load data set includes:

[0013] Calibrate the range of the electric load sequence to obtain first processed data;

[0014] marking the first processed data for abnormal data to obtain second processed data;

[0015] Outlier correction and missing value filling are performed on the second processed data to obtain an electric load data set.

[0016] Optionally, performing a correlation analysis on the influencing factor data to obtain the factor influence value includes:

[0017] Adding a first disturbance amount to the influencing factor data to obtain first disturbance data;

[0018] Adding a second disturbance amount to the influencing factor data to obtain second disturbance data;

[0019] A factor influence value is obtained according to the first disturbance data and the second disturbance data.

[0020] Optionally, obtaining a factor influence value according to the first disturbance data and the second disturbance data includes:

[0021] Obtaining an average impact value according to the first disturbance data and the second disturbance data;

[0022] The average influence value is normalized to obtain the factor influence value.

[0023] Optionally, the electric load data set and the factor influence value are concatenated to obtain an electric load characteristic value, including:

[0024] The electric load data set and the factor influence value are normalized to obtain an electric load characteristic value.

[0025] Optionally, the electric load characteristic value is input into an electric load prediction model to obtain an electric load prediction result, including:

[0026] Inputting the electric load characteristic value into the electric load prediction model to perform electric load prediction processing to obtain a first output value and a second output value;

[0027] The first output value and the second output value are combined to obtain an electric load prediction result.

[0028] Optionally, the target area electric load prediction method further includes:

[0029] According to the evaluation index of the electric load forecast result, the electric load forecast evaluation result is obtained.

[0030] The present invention also provides a device for predicting electric load in a target area, characterized by comprising:

[0031] An acquisition module is used to acquire the electric load sequence and influencing factor data of the target area;

[0032] The processing module is used to perform outlier processing on the electric load sequence to obtain an electric load data set; perform correlation analysis on the influencing factor data to obtain factor influence values; splice the electric load data set and the factor influence values ​​to obtain electric load characteristic values; input the electric load characteristic values ​​into the electric load prediction model to perform electric load prediction processing, and modularly combine the output values ​​of the electric load prediction model to obtain the electric load prediction result of the target area.

[0033] The present invention also provides a computing device, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed.

[0034] The present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0035] The above solution of the present invention includes at least the following beneficial effects:

[0036] The above scheme of the present invention obtains the electric load sequence and influencing factor data of the target area; performs outlier processing on the electric load sequence to obtain an electric load data set; performs correlation analysis on the influencing factor data to obtain factor influence values; splices the electric load data set and the factor influence values ​​to obtain electric load characteristic values; inputs the electric load characteristic values ​​into the electric load prediction model to perform electric load prediction processing, and modularly combines the output values ​​of the electric load prediction model to obtain the electric load prediction result of the target area. The scheme of the present invention can accurately predict the electric load and realize modular combination of load prediction values ​​for different zero-carbon industrial parks. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a step diagram of a method for predicting electric load in a target area provided by an embodiment of the present invention;

[0038] Figure 2 is a specific flow chart of the electric load prediction method for the target area provided by an embodiment of the present invention;

[0039] Figure 3 It is a box plot of the electric load data of Enterprise A provided by a specific embodiment of the present invention;

[0040] Figure 4 It is a box plot of the electric load data of Company B provided by a specific embodiment of the present invention;

[0041] Figure 5 A bar chart showing the degree of influence of influencing factors on Enterprise A provided by a specific embodiment of the present invention;

[0042] Figure 6is a bar chart showing the degree of influence of influencing factors on company B provided by a specific embodiment of the present invention;

[0043] Figure 7 It is a curve chart of the optimization result of the electric load data of Enterprise A provided by a specific embodiment of the present invention;

[0044] Figure 8 It is a curve chart of the optimization result of the electric load data of Enterprise B provided by a specific embodiment of the present invention;

[0045] Fig. 9 is a curve chart of electric load forecasting results of Enterprise A provided by a specific embodiment of the present invention;

[0046] Fig.10 is a curve chart of the electric load forecasting result of enterprise B provided by a specific embodiment of the present invention;

[0047] Fig.11 A modular combination result curve diagram of electric load prediction results provided by a specific embodiment of the present invention;

[0048] Fig.12 It is a module schematic diagram of the electric load prediction device for the target area provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0050] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting electric load in a target area, comprising:

[0051] Step 11, obtaining the electric load sequence and influencing factor data of the target area;

[0052] Step 12, performing outlier processing on the electric load sequence to obtain an electric load data set;

[0053] Step 13, performing correlation analysis on the influencing factor data to obtain factor influence values;

[0054] Step 14, concatenating the electric load data set and the factor influence value to obtain an electric load characteristic value;

[0055] Step 15, inputting the electric load characteristic value into the electric load prediction model to perform electric load prediction processing, and modularly combining the output values ​​of the electric load prediction model to obtain the electric load prediction result of the target area.

[0056] In this embodiment, there are different industrial modules and various industrial facilities such as polysilicon production, coal chemical industry, metallurgy, petrochemical industry, etc. in the zero-carbon industrial park. The power load of the industrial park is affected by various factors. Specifically, the influencing factor data include: time characteristics of date, hour, month, season, week and meteorological characteristics of dry bulb temperature, dew point temperature, relative humidity, atmospheric pressure, total radiation, direct radiation, scattered radiation, wind speed, etc. 13 factors. The power load data of different industrial facilities in the zero-carbon industrial park are arranged in time series to obtain the power load sequence of the target area. These data can reflect the changes in power load in different time periods.

[0057] A boxplot is used to check the abnormal data of the electric load sequence, and the outliers are corrected to fill the missing values. Secondly, the importance of 13 external factors affecting the temporal characteristics and meteorological characteristics of the electric load in the industrial park is evaluated, and the factor influence values ​​of the key factors are obtained. Data optimization processing of the industrial park electric load sequence can effectively extract the electric load signal with multi-scale characteristics and adaptively optimize the decomposition to reduce the impact of noise on the prediction, thereby improving the accuracy and robustness of the load prediction. The key external factors are spliced ​​with the processed industrial park electric load sequence, and the electric load prediction model is used for prediction, and the prediction results are evaluated using the fitting curve. Finally, the electric load prediction results in different zero-carbon industrial parks are obtained, and the modular combination of different zero-carbon industrial parks is realized.

[0058] The electric load prediction method for the target area of ​​this embodiment can accurately predict the electric load. The constructed model network can be directly applied to the electric load prediction and obtain accurate prediction results, thereby realizing the modular combination of load prediction values ​​for different zero-carbon industrial parks.

[0059] In an optional embodiment of the present invention, step 12 may include:

[0060] Step 121, calibrating the range of the electric load sequence to obtain first processed data;

[0061] Step 122, marking the first processed data as abnormal data to obtain second processed data;

[0062] Step 123, performing outlier correction and missing value filling on the second processed data to obtain an electric load data set.

[0063] In this embodiment, the median, lower quartile (Q1), upper quartile (Q3), and interquartile range (I QR ), upper and lower whiskers (Whiskers) and outliers (Outliers).

[0064] The median of the electric load sequence is determined according to the center line of the box plot; the lower quartile (Q1) is determined according to the lower edge of the box; the upper quartile (Q3) is determined according to the upper edge of the box; the interquartile range (I QR ); According to the box boundary (i.e., the upper quartile Q3 and the lower quartile Q1) extending to the maximum and minimum non-outlier value in the data set, the upper and lower whiskers are determined and set at [Q1-1.5×Q3+1.5×I QR ] range, and data outside this range are considered outliers; data points outside the upper and lower whiskers represent values ​​far away from the data center and are determined to be outliers.

[0065] The interquartile range method is used to set the range of the electric load series, I QR =Q3-Q1; By flexibly adjusting the quantile range to optimize the detection effect, the robustness is enhanced and the impact of extreme values ​​is reduced.

[0066] Detect abnormal values ​​within the set range of the electric load sequence: will be lower than Q1-1.5*I QR Or higher than Q3+1.5I QR The data are considered as outliers; and through the formula: Correct the outliers and obtain the electric load data set.

[0067] Among them, X missing is an outlier, X j is the electric load value, and K is the number of electric load samples in the set range of the electric load sequence.

[0068] In an optional embodiment of the present invention, step 13 may include:

[0069] Step 131, adding a first disturbance amount to the influencing factor data to obtain first disturbance data;

[0070] Step 132, adding a second disturbance amount to the influencing factor data to obtain second disturbance data;

[0071] Step 133: Obtain a factor influence value according to the first disturbance data and the second disturbance data.

[0072] In this embodiment, a disturbance is added or subtracted from the original sequence of each external factor affecting the electric load, and the order of magnitude of the disturbance is 1e-5 , generating a positive perturbation x pred_positive and negative disturbance x pred_negative , set the disturbance ratio to 0.1.

[0073] The electric load with positive and negative disturbances is predicted respectively, and the positive disturbance result y is generated. pred_positive and the negative perturbation result y pred_negative By comparing these two sets of prediction results, the average impact of external factors on load forecasting is obtained, and the calculation formula of the average impact value (MIV) is:

[0074]

[0075] Where, δ is the number of samples of electrical load;

[0076] The calculated factor influence values ​​are positive and negative. In order to evaluate the influence of external factors on the electric load, the absolute value of the factor influence value is normalized to between 0 and 1, and the result is:

[0077]

[0078] In the formula, X(t) is the factor influence value, Abs MIV is the absolute value of the average impact value, min MIV is the minimum value of the average impact value, max MIV It is the maximum value of the average impact value, where 1 indicates the greatest impact and 0 indicates the least impact.

[0079] This embodiment uses an average influence value analysis method to process the electric load data, which can provide better explanation and practicality by measuring the average influence of input variables on output results.

[0080] In an optional embodiment of the present invention, step 14 may include:

[0081] The electric load data set and the factor influence value are normalized to obtain an electric load characteristic value.

[0082] In this embodiment, the electric load data set is processed by variational mode decomposition (AVMD) to minimize the modal components and maximize the spectrum concentration as the optimization goal. The formula is:

[0083]

[0084]

[0085] Among them, u k (t) is the optimized electric load data set, K is the number of modes, u k For each modal component, ωk is the center frequency of the kth factor influence value.

[0086] When performing variational mode decomposition on the electric load data set, the parameters of variational mode decomposition (AVMD) are set, where alpha represents the bandwidth of the control mode. The larger its value, the narrower the bandwidth of the mode. The alpha search range is set to 500 to 10000 to ensure the accuracy of the decomposition result. A smaller bandwidth helps to extract subtle features in the signal, thereby improving the prediction ability of the model. K is the number of modes, which is set to an integer value from 2 to 10 to adapt to different data characteristics. Reasonable selection of K value can avoid overfitting or underfitting, thereby optimizing the decomposition effect of the signal. The convergence speed tau of the mode is set to 0 to 1 to ensure the efficient operation of the algorithm. tol is the convergence accuracy of the control algorithm, which is set to 1e-6. A smaller tolerance allows the algorithm to find a more accurate solution. DC represents whether to retain the DC component of the signal. When set to 1, the DC component of the signal is retained, making the superposition of IMFs closer to the original signal.

[0087] Then, the electric load data set after variational mode decomposition is globally dynamically optimized, and the Bayesian algorithm (TPE) is used to adjust the bandwidth, number of modes, convergence speed (alpha, K, tau) and other parameters, so that the multi-scale characteristics in the load signal can be accurately captured, the decomposition accuracy and noise resistance can be improved, and the model complexity can be simplified and the prediction stability can be improved. The formula is: K

[0088]

[0089] Among them, P i (f) is the power spectral density of the jth modal component, ε is the smoothing factor (valued at 10) to avoid numerical calculation errors. -10 ). H(alpha, K, tau) represents the spectrum entropy. The Bayesian algorithm is used to sample the above parameter space, and the maximum number of evaluations is set to 100 times, so that the spectrum entropy H(alpha, K, tau) is minimized.

[0090] This embodiment adopts a double-layer optimization method combining AVMD and TPE, and uses an adaptive algorithm to find the optimal modal parameters, which can reduce the calculation difficulty and improve the prediction accuracy.

[0091] In an optional embodiment of the present invention, step 15 may include:

[0092] Step 151, inputting the electric load characteristic value into the electric load prediction model to perform electric load prediction processing to obtain a first output value and a second output value;

[0093] Step 152: Combine the first output value and the second output value to obtain an electric load prediction result.

[0094] In this embodiment, the factor influence value X(t) in the above steps is compared with the optimized electric load data set u k (t) is spliced ​​as the electric load characteristic value Z(t) of the prediction model, and the electric load prediction model based on time-based bidirectional recurrent neural network is used to predict the electric load.

[0095] Specifically, the factor influence value X(t) is compared with the electric load data set u k (t) is spliced, and the formula is:

[0096] Z(t k )=[u k (txt)];

[0097] For Z(t k ) is normalized to the maximum and minimum to obtain the characteristic value of the electric load Z(t), the formula of which is:

[0098]

[0099] To build the electric load prediction model, we first build the neural network layer:

[0100] Input Gate i t :

[0101] i t =σ(W i ·[h t-1 ,z t ]+b i );

[0102] Among them, W i is the weight matrix of the input gate; h t-1 is the hidden state at the previous moment; b i is the bias vector of the input gate; σ is the activation function, which uses the sigmoid function and has an interval range of [0,1].

[0103] Candidate value

[0104]

[0105] Among them, W c is the weight matrix of candidate values; b c is the bias vector of the candidate value; tanh is the hyperbolic tangent activation function, which maps the output value to [-1,1].

[0106] Unit Status C t :

[0107]

[0108] Among them, f t is the output of the forget gate, which is used to control the unit state C at the previous moment t-1 The forgetting process; i t is the output of the input gate; is the candidate value at the current moment, and * represents element-by-element multiplication.

[0109] Output Gate O t :

[0110] O t =σ(W o ·[h t-1 ,z t ]+b o );

[0111] Among them, W o is the weight matrix of the output gate; b o is the bias vector of the output gate; σ is the activation function, which uses the sigmoid function.

[0112] Output value (hidden state) h t :

[0113] h t =O t *tanh(C t );

[0114] Among them, tanh(C t ) compresses the value of the cell state to [-1,1], O t To control the output ratio of the hidden state, * represents element-by-element multiplication.

[0115] The two neural network layers are combined to construct the electric load prediction model. The first neural network layer processes the forward information of the input sequence, and the second neural network layer processes the reverse information. Finally, the output of each neural network layer is merged into a single output. The data is divided into 80% training set and 20% test set. The formula is:

[0116] Forward neural network layer output

[0117]

[0118] Reverse Neural Network Layer Output

[0119]

[0120] Combine the outputs to get the predicted value of electric load h t :

[0121]

[0122] Finally, the predicted electric load values ​​of different industries in the industrial park are h t The combined results are superimposed to form a modular combination result, and the electric load forecast result of the target area is obtained. An 8760 curve chart is drawn based on the electric load forecast result of the target area.

[0123] In the above steps, the parameters of the electric load forecasting model are set as follows:

[0124] Neural network layer configuration:

[0125] Number of units: The number of units in each neural network layer is set to 100 to provide the model with sufficient learning capacity to capture complex patterns in time series data.

[0126] Return sequences: The “return_sequences” parameter of the first neural network layer is set to True to ensure that the layer returns the output at each time step to facilitate processing by subsequent layers.

[0127] Regularization:

[0128] To prevent the model from overfitting, a Dropout layer was added after each neural network layer, and the dropout rate was set to 0.2, that is, 20% of the neurons were randomly dropped during the training process.

[0129] Training process parameters:

[0130] Epochs: Set to 50 to control the number of epochs the model learns.

[0131] Batch Size: Set to 32, using 32 samples for each parameter update.

[0132] Optimizer and loss function:

[0133] The Adam optimizer is used, which performs well in dealing with complex problems and has an adaptive learning rate feature.

[0134] The mean squared error is used as the loss function, which is suitable for regression tasks.

[0135] Callback mechanism:

[0136] Early Stopping: Monitor the validation loss (val_loss). If there is no improvement in 5 consecutive epochs, stop training and restore to the optimal weights.

[0137] Learning rate decay (ReduceLROnPlateau): If the validation loss does not improve within 3 cycles, the learning rate is reduced to 20% of the original (factor = 0.2), and the minimum learning rate is set to 0.001.

[0138] Data preprocessing:

[0139] All input features are normalized using MinMaxScaler to make their values ​​range between 0 and 1 to accelerate model convergence.

[0140] In an optional embodiment of the present invention, the electric load prediction method of the target area further includes:

[0141] Step 16, obtaining an electric load forecast evaluation result according to the evaluation index of the electric load forecast result.

[0142] In this embodiment, an index evaluation is performed based on the electric load prediction result obtained in step 15, and the evaluation indexes include root mean square error (RMSE), mean absolute error (MAE), fitting coefficient (R 2 ):

[0143]

[0144] Among them, y i is the true value, is the predicted value, is the mean of the true value, n is the number of power load forecast results, R 2 The closer the value is to 1, the stronger the explanatory power of the model is and the more accurate the electric load prediction results are.

[0145] In a specific embodiment of the present invention, Figure 2 The figure shows a flow chart of electric load forecasting for a zero-carbon industrial park. The time characteristics and meteorological characteristics of the external factors of the zero-carbon industrial park and the electric load data are derived from the low-voltage power distribution area where the park is located. The park is equipped with a meteorological station information collection device.

[0146] Step S1, collect the hourly electricity load of enterprises A and B throughout the year and the time characteristics of date, hour, month, season, week and the meteorological characteristics of dry bulb temperature, dew point temperature, relative humidity, atmospheric pressure, total radiation, direct radiation, scattered radiation, wind speed and other external influencing factors.

[0147] Step S2: For the hourly load sequence of enterprises A and B throughout the year, Q1 is set to 10% and Q2 is set to 90%. By using the 80% range set by IQR, the abnormal load data is identified and marked using a box plot, such as Figure 3 and Figure 4 As shown in the figure, the K-nearest neighbor algorithm is used to correct the outliers and fill the missing values, and the new hourly electricity load series of enterprises A and B throughout the year are obtained;

[0148] Step S3: Use the mean impact value (MIV) method to evaluate the impact of 13 external factors of companies A and B on the power load of the industrial park, such as Figure 5 and Figure 6 As shown; the order of the impact of external factors on Company A is date, total radiation, month, scattered radiation, atmospheric pressure, season, direct radiation, wind speed, hour, week, dry bulb temperature, relative humidity, and dew point temperature; the order of the impact of external factors on Company B is dry bulb temperature, dew point temperature, relative humidity, atmospheric pressure, wind speed, total radiation, season, scattered radiation, date, direct radiation, month, hour, and week.

[0149] Step S4: Using a two-layer optimization method combining variational mode decomposition (AVMD) and Bayesian algorithm (TPE) to decompose the power load data of companies A and B into multiple mode functions, such as Figure 7 and Figure 8 shown.

[0150] Step S5: The external factors that have a greater impact on companies A and B are combined with the power load signals processed by AVMD as training data for the power load prediction model to predict the power load for 8760 hours in the next year. Fig. 9 and Fig.10 As shown. The RMSE of the predicted value and the true value of Company A is 1.46%, MAE is 1.07%, and R2 is 0.99; the RMSE of the predicted value and the true value of Company B is 0.577%, MAE is 0.44%, and R2 is 0.99;

[0151] Step S6: Modular combination of industrial enterprises A and B Fig.11 As shown, the load forecast of this park is obtained.

[0152] The electric load prediction method for the target area described in the above embodiment of the present invention, the electric load of the industrial park is affected by multivariate nonlinear factors (such as season, temperature, economic activities, etc.), and by constructing a hierarchical electric load prediction model, it is possible to learn hidden complex patterns and handle complex nonlinear relationships, thereby improving the prediction accuracy; the electric load in the industrial park is highly volatile, and this electric load prediction method can adapt to data changes, has strong robustness and generalization ability, and adapts to dynamically fluctuating power demand; the amount of electric load data in the industrial park is large, and the electric load prediction model performs well in a big data environment. By training a large amount of historical data, the model can more accurately predict future electric loads. It provides a basis for the dispatching of the power system and energy management of the industrial park, which is conducive to the realization of a zero-carbon industrial park.

[0153] like Fig.12 As shown, an embodiment of the present invention further provides an electric load prediction device 120 for a target area, comprising:

[0154] An acquisition module 121 is used to acquire the electric load sequence and influencing factor data of the target area;

[0155] The processing module 122 is used to perform outlier processing on the electric load sequence to obtain an electric load data set; perform correlation analysis on the influencing factor data to obtain factor influence values; concatenate the electric load data set and the factor influence values ​​to obtain electric load characteristic values; input the electric load characteristic values ​​into the electric load prediction model to perform electric load prediction processing, and modularly combine the output values ​​of the electric load prediction model to obtain the electric load prediction results of the target area.

[0156] Optionally, performing missing value processing on the electric load data to obtain an electric load data set includes:

[0157] Calibrate the range of the electric load sequence to obtain first processed data;

[0158] marking the first processed data for abnormal data to obtain second processed data;

[0159] Outlier correction and missing value filling are performed on the second processed data to obtain an electric load data set.

[0160] Optionally, performing a correlation analysis on the influencing factor data to obtain the factor influence value includes:

[0161] Adding a first disturbance amount to the influencing factor data to obtain first disturbance data;

[0162] Adding a second disturbance amount to the influencing factor data to obtain second disturbance data;

[0163] A factor influence value is obtained according to the first disturbance data and the second disturbance data.

[0164] Optionally, obtaining a factor influence value according to the first disturbance data and the second disturbance data includes:

[0165] Obtaining an average impact value according to the first disturbance data and the second disturbance data;

[0166] The average influence value is normalized to obtain the factor influence value.

[0167] Optionally, the electric load data set and the factor influence value are concatenated to obtain an electric load characteristic value, including:

[0168] The electric load data set and the factor influence value are normalized to obtain an electric load characteristic value.

[0169] Optionally, the electric load characteristic value is input into an electric load prediction model to obtain an electric load prediction result, including:

[0170] Inputting the electric load characteristic value into the electric load prediction model to perform electric load prediction processing to obtain a first output value and a second output value;

[0171] The first output value and the second output value are combined to obtain an electric load prediction result.

[0172] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0173] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the method described in the above embodiment is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0174] In an embodiment of the present invention, a computer-readable storage medium is further provided, which stores instructions, and when the instructions are executed on a computer, the computer executes the method described in the above embodiment. All implementations in the above method embodiment are applicable to this embodiment, and can also achieve the same technical effect.

[0175] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0176] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0177] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0178] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0180] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0181] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0182] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0183] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for predicting electric load in a target area, characterized in that: include: Obtain the electric load sequence and influencing factor data of the target area; Performing outlier processing on the electric load sequence to obtain an electric load data set; Performing correlation analysis on the influencing factor data to obtain factor influence values; The electric load data set and the factor influence value are combined to obtain an electric load characteristic value; The electric load characteristic value is input into the electric load prediction model to perform electric load prediction processing, and the output value of the electric load prediction model is modularly combined to obtain the electric load prediction result of the target area.

2. The method for predicting electric load in a target area according to claim 1, characterized in that: The electric load sequence is processed for outliers to obtain an electric load data set, including: Calibrate the range of the electric load sequence to obtain first processed data; marking the first processed data for abnormal data to obtain second processed data; Outlier correction and missing value filling are performed on the second processed data to obtain an electric load data set.

3. The method for predicting electric load in a target area according to claim 1, characterized in that: Performing correlation analysis on the influencing factor data to obtain factor influence values ​​includes: Adding a first disturbance amount to the influencing factor data to obtain first disturbance data; Adding a second disturbance amount to the influencing factor data to obtain second disturbance data; A factor influence value is obtained according to the first disturbance data and the second disturbance data.

4. The method for predicting electric load in a target area according to claim 3, characterized in that: Obtaining a factor influence value according to the first disturbance data and the second disturbance data includes: Obtaining an average impact value according to the first disturbance data and the second disturbance data; The average influence value is normalized to obtain the factor influence value.

5. The method for predicting electric load in a target area according to claim 1, characterized in that: The electric load data set and the factor influence value are spliced ​​to obtain the electric load characteristic value, including: The electric load data set and the factor influence value are normalized to obtain an electric load characteristic value.

6. The method for predicting electric load in a target area according to claim 5, characterized in that: The electric load characteristic value is input into the electric load prediction model, and the output value is modularly combined to obtain the electric load prediction result of the target area, including: The electric load characteristic value is input into the electric load prediction model to perform electric load prediction processing to obtain a first output value and a second output value; the first output value and the second output value are combined to obtain an electric load prediction result.

7. The method for predicting electric load in a target area according to claim 1, characterized in that: Also includes: According to the evaluation index of the electric load forecast result, the electric load forecast evaluation result is obtained.

8. An electric load prediction device for a target area, characterized in that: include: An acquisition module is used to acquire the electric load sequence and influencing factor data of the target area; A processing module is used to perform outlier processing on the electric load sequence to obtain an electric load data set; perform correlation analysis on the influencing factor data to obtain factor influence values; The electric load data set and the factor influence value are combined to obtain an electric load characteristic value; The electric load characteristic value is input into the electric load prediction model to perform electric load prediction processing, and the output value of the electric load prediction model is modularly combined to obtain the electric load prediction result of the target area.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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