CN116796639A - Short-term power load prediction method, device and equipment - Google Patents
Short-term power load prediction method, device and equipment Download PDFInfo
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Abstract
Description
技术领域Technical field
本发明一般涉及负荷预测领域,并且更具体地,涉及短期电力负荷预测方法、装置及设备。The present invention generally relates to the field of load forecasting, and more specifically, to short-term power load forecasting methods, devices and equipment.
背景技术Background technique
随着电力市场改革,工业企业尤其是具备机械加工、冶金等智能制造企业来说,用能成本对于强化竞争优势具有现实意义。电力市场多元化使得工业企业可以灵活的在市场中获得具备竞争优势电力电能。而负荷短期预测则是实现用能成本降低的基础条件。With the reform of the electricity market, energy costs are of practical significance for industrial enterprises, especially intelligent manufacturing enterprises such as mechanical processing and metallurgy, to strengthen their competitive advantages. The diversification of the power market allows industrial enterprises to flexibly obtain competitive advantages in the market. Short-term load forecasting is the basic condition for reducing energy costs.
需求预测近年来一直是众多学者的研究热点。传统方法大都采用概率模型,如自回归方法(ARMA)、多元线性回归及灰度模型等。但传统模型方法大都存在这计算方面的限制。这些限制无法处理电力需求数据的非线性及数据缺失等特性。现代预测技术大都采用混合机器学习方法。Demand forecasting has been a research hotspot for many scholars in recent years. Most traditional methods use probabilistic models, such as autoregressive method (ARMA), multiple linear regression and grayscale models. However, most traditional model methods have this computational limitation. These limitations cannot handle the nonlinear and missing data characteristics of power demand data. Modern forecasting techniques mostly use hybrid machine learning methods.
混合机器学习方法的优势在于实现预测功能的同时具有良好的预测精度。目前研究包含模糊-神经网络法、支持向量机-优化法、极限学习机-优化法及BP神经网络等众多组合方法。在工业负荷短期预测方面计算时间较长,精度较低,鲁棒性较差等问题比较凸显。The advantage of the hybrid machine learning method is that it has good prediction accuracy while achieving the prediction function. Current research includes many combination methods such as fuzzy-neural network method, support vector machine-optimization method, extreme learning machine-optimization method, and BP neural network. In terms of short-term industrial load forecasting, problems such as long calculation time, low accuracy, and poor robustness are more prominent.
发明内容Contents of the invention
根据本发明的实施例,提供了一种短期电力负荷预测方案。本方案解决了处理负荷数据中的高频波动性和非线性难题,提升了预测精度,适用于厂级以上的负荷需求预测,具备更佳的通用性和适用性。According to embodiments of the present invention, a short-term power load prediction scheme is provided. This solution solves the high-frequency fluctuation and nonlinear problems in processing load data, improves the prediction accuracy, is suitable for load demand prediction at the factory level and above, and has better versatility and applicability.
在本发明的第一方面,提供了一种短期电力负荷预测方法。该方法包括:In a first aspect of the invention, a short-term power load forecasting method is provided. The method includes:
获取电力需求时间序列数据,将所述电力需求时间序列数据分解为高通系数和低通系数;Obtain power demand time series data, and decompose the power demand time series data into high-pass coefficients and low-pass coefficients;
将所述高通系数和低通系数分别与小波函数进行卷积,得到不同频带的小波系数,并按序排列,得到输入特征向量;Convolve the high-pass coefficients and low-pass coefficients with wavelet functions respectively to obtain wavelet coefficients in different frequency bands, and arrange them in order to obtain the input feature vector;
将所述输入特征向量通过差分进化算法对小波系数进行优化,构建基于差分进化优化算法的径向基函数神经网络模型;Optimize the wavelet coefficients of the input feature vector through a differential evolution algorithm, and construct a radial basis function neural network model based on the differential evolution optimization algorithm;
计算所述基于差分进化优化算法的径向基函数神经网络模型的高斯函数中心、高斯函数宽度以及隐藏单元和输出单元的权重,对所述基于差分进化优化算法的径向基函数神经网络模型进行参数调整;Calculate the Gaussian function center, Gaussian function width and the weight of the hidden unit and the output unit of the radial basis function neural network model based on the differential evolution optimization algorithm, and perform the radial basis function neural network model based on the differential evolution optimization algorithm. Parameter adjustment;
利用参数调整后的基于差分进化优化算法的径向基函数神经网络模型对未来时间点的负荷进行预测。The parameter-adjusted radial basis function neural network model based on differential evolution optimization algorithm is used to predict the load at future time points.
进一步地,所述将所述电力需求时间序列数据分解为高通系数和低通系数,包括:Further, the decomposition of the power demand time series data into high-pass coefficients and low-pass coefficients includes:
将所述电力需求时间序列数据的原始时间序列进行N次滤波,每一次滤波后得到一个近似系数和细节系数,一共得到N个近似系数和N个细节系数;所述N个近似系数作为高通系数;所述N个细节系数作为低通系数。The original time series of the power demand time series data is filtered N times, and an approximate coefficient and a detail coefficient are obtained after each filtering. A total of N approximate coefficients and N detail coefficients are obtained; the N approximate coefficients are used as high-pass coefficients ;The N detail coefficients are used as low-pass coefficients.
进一步地,所述将所述输入特征向量通过差分进化算法对小波系数进行优化,得到基于差分进化优化算法的径向基函数神经网络模型,包括:Further, the wavelet coefficients of the input feature vector are optimized through a differential evolution algorithm to obtain a radial basis function neural network model based on the differential evolution optimization algorithm, including:
初始化种群;Initialize the population;
在所述种群中随机选取若干不同个体,对所选取个体的向量差进行缩放,与待变异个体进行向量合成,得到变异向量;Randomly select several different individuals from the population, scale the vector differences of the selected individuals, and perform vector synthesis with the individuals to be mutated to obtain a mutation vector;
将基准向量与所述变异向量进行交叉操作;Perform a crossover operation on the reference vector and the mutation vector;
利用差分进化算法对所述种群中的最优个体进行选择,得到子代个体;Use a differential evolution algorithm to select the optimal individual in the population to obtain offspring individuals;
对所述子代个体进行适应性评估,根据适应性筛选得到精英个体;Conduct an adaptability assessment on the offspring individuals, and obtain elite individuals based on adaptability screening;
将达到迭代条件时的精英个体作为基于差分进化优化算法的径向基函数神经网络模型。The elite individuals when reaching the iteration conditions are used as the radial basis function neural network model based on the differential evolution optimization algorithm.
进一步地,所述计算所述基于差分进化优化算法的径向基函数神经网络模型的高斯函数中心、高斯函数宽度以及隐藏单元和输出单元的权重,包括:Further, the calculation of the Gaussian function center, Gaussian function width, and weights of hidden units and output units of the radial basis function neural network model based on the differential evolution optimization algorithm includes:
通过K-means聚类算法,将所述不同频带的小波系数进行聚类,并将聚类中心作为高斯函数的中心;Using the K-means clustering algorithm, the wavelet coefficients of the different frequency bands are clustered, and the cluster center is used as the center of the Gaussian function;
使用均方误差作为损失函数,通过反向传播算法计算得到隐藏单元和输出单元的权重;Using the mean square error as the loss function, the weights of the hidden units and output units are calculated through the backpropagation algorithm;
通过交叉验证选择最优的宽度作为高斯函数的宽度。The optimal width is selected as the width of the Gaussian function through cross-validation.
进一步地,所述利用参数调整后的基于差分进化优化算法的径向基函数神经网络模型对未来时间点的负荷进行预测,包括:Further, the use of the parameter-adjusted radial basis function neural network model based on the differential evolution optimization algorithm to predict the load at future time points includes:
将不同频带的小波系数输入参数调整后的基于差分进化优化算法的径向基函数神经网络模型,输出未来连续时间序列的负荷值。The wavelet coefficients of different frequency bands are input into the radial basis function neural network model based on the differential evolution optimization algorithm after parameter adjustment, and the load value of the future continuous time series is output.
在本发明的第二方面,提供了一种短期电力负荷预测装置。该装置包括:In a second aspect of the present invention, a short-term power load prediction device is provided. The device includes:
分解模块,用于获取电力需求时间序列数据,将所述电力需求时间序列数据分解为高通系数和低通系数;A decomposition module, used to obtain power demand time series data and decompose the power demand time series data into high-pass coefficients and low-pass coefficients;
卷积模块,用于将所述高通系数和低通系数分别与小波函数进行卷积,得到不同频带的小波系数,并按序排列,得到输入特征向量;The convolution module is used to convolve the high-pass coefficient and the low-pass coefficient with the wavelet function respectively to obtain the wavelet coefficients of different frequency bands, and arrange them in order to obtain the input feature vector;
优化模块,用于将所述输入特征向量通过差分进化算法对小波系数进行优化,得到基于差分进化优化算法的径向基函数神经网络模型;An optimization module, used to optimize the wavelet coefficients of the input feature vector through a differential evolution algorithm to obtain a radial basis function neural network model based on the differential evolution optimization algorithm;
计算模块,用于计算所述基于差分进化优化算法的径向基函数神经网络模型的高斯函数中心、高斯函数宽度以及隐藏单元和输出单元的权重;A calculation module used to calculate the Gaussian function center, Gaussian function width, and the weight of the hidden unit and the output unit of the radial basis function neural network model based on the differential evolution optimization algorithm;
预测模块,用于利用所述基于差分进化优化算法的径向基函数神经网络模型对未来时间点的负荷进行预测。A prediction module is used to predict the load at future time points using the radial basis function neural network model based on the differential evolution optimization algorithm.
进一步地,所述将所述电力需求时间序列数据分解为高通系数和低通系数,包括:Further, the decomposition of the power demand time series data into high-pass coefficients and low-pass coefficients includes:
将所述电力需求时间序列数据的原始时间序列进行N次滤波,每一次滤波后得到一个近似系数和细节系数,一共得到N个近似系数和N个细节系数;所述N个近似系数作为高通系数;所述N个细节系数作为低通系数。The original time series of the power demand time series data is filtered N times, and an approximate coefficient and a detail coefficient are obtained after each filtering. A total of N approximate coefficients and N detail coefficients are obtained; the N approximate coefficients are used as high-pass coefficients ;The N detail coefficients are used as low-pass coefficients.
进一步地,所述将所述输入特征向量通过差分进化算法对小波系数进行优化,得到基于差分进化优化算法的径向基函数神经网络模型,包括:Further, the wavelet coefficients of the input feature vector are optimized through a differential evolution algorithm to obtain a radial basis function neural network model based on the differential evolution optimization algorithm, including:
初始化种群;Initialize the population;
在所述种群中随机选取若干不同个体,对所选取个体的向量差进行缩放,与待变异个体进行向量合成,得到变异向量;Randomly select several different individuals from the population, scale the vector differences of the selected individuals, and perform vector synthesis with the individuals to be mutated to obtain a mutation vector;
将基准向量与所述变异向量进行交叉操作;Perform a crossover operation on the reference vector and the mutation vector;
利用差分进化算法对所述种群中的最优个体进行选择,得到子代个体;Use a differential evolution algorithm to select the optimal individual in the population to obtain offspring individuals;
对所述子代个体进行适应性评估,根据适应性筛选得到精英个体;Conduct an adaptability assessment on the offspring individuals, and obtain elite individuals based on adaptability screening;
将达到迭代条件时的精英个体作为基于差分进化优化算法的径向基函数神经网络模型。The elite individuals when reaching the iteration conditions are used as the radial basis function neural network model based on the differential evolution optimization algorithm.
进一步地,所述计算所述基于差分进化优化算法的径向基函数神经网络模型的高斯函数中心、高斯函数宽度以及隐藏单元和输出单元的权重,包括:Further, the calculation of the Gaussian function center, Gaussian function width, and weights of hidden units and output units of the radial basis function neural network model based on the differential evolution optimization algorithm includes:
通过K-means聚类算法,将所述不同频带的小波系数进行聚类,并将聚类中心作为高斯函数的中心;Using the K-means clustering algorithm, the wavelet coefficients of the different frequency bands are clustered, and the cluster center is used as the center of the Gaussian function;
使用均方误差作为损失函数,通过反向传播算法计算得到隐藏单元和输出单元的权重;Using the mean square error as the loss function, the weights of the hidden units and output units are calculated through the backpropagation algorithm;
通过交叉验证选择最优的宽度作为高斯函数的宽度。The optimal width is selected as the width of the Gaussian function through cross-validation.
进一步地,所述利用参数调整后的基于差分进化优化算法的径向基函数神经网络模型对未来时间点的负荷进行预测,包括:Further, the use of the parameter-adjusted radial basis function neural network model based on the differential evolution optimization algorithm to predict the load at future time points includes:
将不同频带的小波系数输入参数调整后的基于差分进化优化算法的径向基函数神经网络模型,输出未来连续时间序列的负荷值。The wavelet coefficients of different frequency bands are input into the radial basis function neural network model based on the differential evolution optimization algorithm after parameter adjustment, and the load value of the future continuous time series is output.
在本发明的第三方面,提供了一种电子设备。该电子设备至少一个处理器;以及与所述至少一个处理器通信连接的存储器;所述存储器存储有可被所述至少一个处理器执行的指令,所述指令被所述至少一个处理器执行,以使所述至少一个处理器能够执行本发明第一方面的方法。In a third aspect of the invention, an electronic device is provided. The electronic device has at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, To enable the at least one processor to execute the method of the first aspect of the invention.
在本发明的第四方面,提供了一种存储有计算机指令的非瞬时计算机可读存储介质,所述计算机指令用于使所述计算机执行本发明第一方面的方法。In a fourth aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method of the first aspect of the present invention.
应当理解,发明内容部分中所描述的内容并非旨在限定本发明的实施例的关键或重要特征,亦非用于限制本发明的范围。本发明的其它特征将通过以下的描述变得容易理解。It should be understood that the content described in this summary is not intended to identify key or important features of the embodiments of the invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description.
附图说明Description of the drawings
结合附图并参考以下详细说明,本发明各实施例的上述和其他特征、优点及方面将变得更加明显。在附图中,相同或相似的附图标记表示相同或相似的元素,其中:The above and other features, advantages and aspects of various embodiments of the invention will become more apparent with reference to the following detailed description taken in conjunction with the accompanying drawings. In the drawings, the same or similar reference numbers represent the same or similar elements, where:
图1示出了根据本发明的实施例的短期电力负荷预测方法的流程图;Figure 1 shows a flow chart of a short-term power load forecasting method according to an embodiment of the present invention;
图2示出了根据本发明的实施例的通过差分进化算法对小波系数进行优化的优化过程示意图;Figure 2 shows a schematic diagram of the optimization process of optimizing wavelet coefficients through a differential evolution algorithm according to an embodiment of the present invention;
图3示出了根据本发明的实施例的短期电力负荷预测装置的方框图;Figure 3 shows a block diagram of a short-term power load prediction device according to an embodiment of the present invention;
图4示出了能够实施本发明的实施例的示例性电子设备的方框图;4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the invention;
其中,400为电子设备、401为计算单元、402为ROM、403为RAM、404为总线、405为I/O接口、406为输入单元、407为输出单元、408为存储单元、409为通信单元。Among them, 400 is electronic equipment, 401 is computing unit, 402 is ROM, 403 is RAM, 404 is bus, 405 is I/O interface, 406 is input unit, 407 is output unit, 408 is storage unit, and 409 is communication unit. .
具体实施方式Detailed ways
为使本发明实施例的目的、技术方案和优点更加清楚,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的全部其他实施例,都属于本发明保护的范围。In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.
另外,本文中术语“和/或”,仅仅是一种描述关联对象的关联关系,表示可以存在三种关系,例如,A和/或B,可以表示:单独存在A,同时存在A和B,单独存在B这三种情况。另外,本文中字符“/”,一般表示前后关联对象是一种“或”的关系。In addition, the term "and/or" in this article is only an association relationship that describes related objects, indicating that there can be three relationships. For example, A and/or B can mean: A alone exists, and A and B exist simultaneously. There are three cases of B alone. In addition, the character "/" in this article generally indicates that the related objects are an "or" relationship.
图1示出了本发明实施例的短期电力负荷预测方法的流程图。DE-RBFNN模型表示基于差分进化优化算法的径向基函数神经网络模型。Figure 1 shows a flow chart of a short-term power load prediction method according to an embodiment of the present invention. The DE-RBFNN model represents the radial basis function neural network model based on the differential evolution optimization algorithm.
该方法包括:The method includes:
S101、获取电力需求时间序列数据,将所述电力需求时间序列数据分解为高通系数和低通系数。S101. Obtain power demand time series data, and decompose the power demand time series data into high-pass coefficients and low-pass coefficients.
在本实施例中,电力系统获取的用户历史数据为用户负荷数据,可以为15min时间间隔,单位为KW;或时间间隔为1hour时间间隔,单位为MW。In this embodiment, the user historical data obtained by the power system is user load data, which can be a 15-minute time interval, the unit is KW; or the time interval is a 1-hour time interval, the unit is MW.
在本实施例中,将所述电力需求时间序列数据的原始时间序列进行N次滤波,每一次滤波后得到一个近似系数和细节系数,一共得到N个近似系数和N个细节系数;所述N个近似系数作为高通系数;所述N个细节系数作为低通系数。In this embodiment, the original time series of the power demand time series data is filtered N times, and an approximate coefficient and a detail coefficient are obtained after each filtering, and a total of N approximate coefficients and N detail coefficients are obtained; the N The approximation coefficients are used as high-pass coefficients; the N detail coefficients are used as low-pass coefficients.
具体地,用DWT(Discrete Wavelet Transformation)离散小波变换作为预处理技术,将电力需求时间序列数据分解为频域和时域。减少数据中存在的噪声,并使电力数据更加平稳。使用离散小波变换,将信号分解为一组近似高通系数和低通系数。将电力数据分解为三级以上信息。例如分解过程是四级,N=4,4个近似系数为(A1,A2,A3,A4)和4个细节系数(D1,D2,D3,D4)的数量分别为四个。Specifically, DWT (Discrete Wavelet Transformation) discrete wavelet transform is used as a preprocessing technology to decompose the power demand time series data into the frequency domain and time domain. Reduce the noise present in the data and make the power data smoother. Using the discrete wavelet transform, the signal is decomposed into a set of approximate high-pass and low-pass coefficients. Decompose power data into three or more levels of information. For example, the decomposition process is four levels, N=4, the number of 4 approximate coefficients is (A 1 , A 2 , A 3 , A 4 ) and 4 detail coefficients (D 1 , D 2 , D 3 , D 4 ) respectively. for four.
在上述实施例中,将原始时间序列通过滤波器进行一次滤波,得到第一级近似系数A1和第一级细节系数D1。In the above embodiment, the original time series is filtered once through the filter to obtain the first-level approximation coefficient A 1 and the first-level detail coefficient D 1 .
然后,将第一级近似系数A1和第一级细节系数D1再通过滤波器进行二次滤波,得到第二级近似系数A2和第二级细节系数D2。Then, the first-level approximation coefficient A 1 and the first-level detail coefficient D 1 are filtered twice through the filter to obtain the second-level approximation coefficient A 2 and the second-level detail coefficient D 2 .
再然后,将第二级近似系数A2和第二级细节系数D2再通过滤波器进行三次滤波,得到第三级近似系数A3和第三级细节系数D3。Then, the second-level approximation coefficient A 2 and the second-level detail coefficient D 2 are filtered three times through the filter to obtain the third-level approximation coefficient A 3 and the third-level detail coefficient D 3 .
最后,将第三级近似系数A3和第三级细节系数D3再通过滤波器进行四次滤波,得到第四级近似系数A4和第三级细节系数D4。Finally, the third-level approximation coefficient A 3 and the third-level detail coefficient D 3 are filtered four times through the filter to obtain the fourth-level approximation coefficient A 4 and the third-level detail coefficient D 4 .
作为本发明的一种实施例,从上述4个近似系数为(A1,A2,A3,A4)和4个细节系数(D1,D2,D3,D4)中,选择最后一级近似系数,即第四级近似系数A4和所有细节系数。重建时间序列x(t):As an embodiment of the present invention, from the above four approximate coefficients (A 1 , A 2 , A 3 , A 4 ) and the four detail coefficients (D 1 , D 2 , D 3 , D 4 ), select The last level of approximation coefficients is the fourth level approximation coefficient A 4 and all detail coefficients. Reconstruct the time series x(t):
在上述实施例中,用DWT作为预处理技术,将电力需求时间序列数据分解为频域和时域。减少数据中存在的噪声,并使电力数据更加平稳。In the above embodiment, DWT is used as a preprocessing technique to decompose the power demand time series data into frequency domain and time domain. Reduce the noise present in the data and make the power data smoother.
S102、将所述高通系数和低通系数分别与小波函数进行卷积,得到不同频带的小波系数,并按序排列,得到输入特征向量。S102. Convolve the high-pass coefficients and low-pass coefficients with wavelet functions respectively to obtain wavelet coefficients in different frequency bands, and arrange them in order to obtain input feature vectors.
小波分解是一种将信号分解为不同尺度的频带的技术。DWT通过将信号与小波函数进行卷积,得到不同频带的小波系数。将不同尺度的小波系数特征按照一定的顺序排列,构成输入特征向量。Wavelet decomposition is a technique that decomposes a signal into frequency bands of different scales. DWT obtains wavelet coefficients in different frequency bands by convolving the signal with the wavelet function. The wavelet coefficient features of different scales are arranged in a certain order to form an input feature vector.
S103、将所述输入特征向量通过差分进化算法对小波系数进行优化,得到DE-RBFNN模型。S103. Use the differential evolution algorithm to optimize the wavelet coefficients of the input feature vector to obtain the DE-RBFNN model.
DE(Differential Evolution Algorithm)是差分进化算法。RBFNN(Radial basisfunction neural network)是径向基函数神经网络。DE (Differential Evolution Algorithm) is a differential evolution algorithm. RBFNN (Radial basis function neural network) is a radial basis function neural network.
在本实施例中,如图2所示,通过差分进化算法对小波系数进行优化的优化过程包括:In this embodiment, as shown in Figure 2, the optimization process of optimizing wavelet coefficients through differential evolution algorithm includes:
S201、初始化种群pk。S201. Initialize the population p k .
具体地,种群由一组个体组成每个个体/>代表一个RBF神经网络的参数集合。按照公式/>随机选取初始值,式中:/>表示第0代第i个个体的第j个值;U(0,1)表示区间(0,1)内均匀分布随机数。Specifically, a population consists of a group of individuals Each individual/> Represents a parameter set of an RBF neural network. According to the formula/> Randomly select the initial value, where:/> Represents the j-th value of the i-th individual in the 0th generation; U(0,1) represents a uniformly distributed random number in the interval (0,1).
S202、在所述种群中随机选取若干不同个体,对所选取个体的向量差进行缩放,与待变异个体进行向量合成,得到变异向量。S202. Randomly select several different individuals from the population, scale the vector differences of the selected individuals, and perform vector synthesis with the individuals to be mutated to obtain a mutation vector.
具体地,通过在种群中随机选取2个不同个体,对其向量差进行缩放后,与待变异个体进行向量合成,完成突变式中:xj1,xj2,xj3为从当前种群随机挑选的3个不同个体;F为缩放因子,决定搜索的步长和速度,取值范围为[0,1]。Specifically, the mutation is completed by randomly selecting two different individuals in the population, scaling their vector differences, and then performing vector synthesis with the individual to be mutated. In the formula: x j1 , x j2 , x j3 are three different individuals randomly selected from the current population; F is the scaling factor, which determines the step length and speed of the search, and the value range is [0,1].
S203、将基准向量与所述变异向量进行交叉操作。S203. Perform a crossover operation on the reference vector and the mutation vector.
具体地,将基准向量与变异向量进行交叉操作,二项式交叉算子计算为式中:randi为区间[0,1]内均匀分布的随机数;in为区间[1,n]内均匀分布随机数;CR为交叉概率,决定着变异前后遗传信息所占权重,取值范围为[0,1]。Specifically, the baseline vector and the mutation vector are crossed, and the binomial crossover operator is calculated as In the formula: rand i is a uniformly distributed random number in the interval [0,1]; i n is a uniformly distributed random number in the interval [1,n]; CR is the crossover probability, which determines the weight of genetic information before and after mutation, taking The value range is [0,1].
S204、利用差分进化算法对所述种群中的最优个体进行选择,得到子代个体。S204. Use a differential evolution algorithm to select the optimal individual in the population to obtain offspring individuals.
差分进化算法即DE算法采用贪婪选择机制,可以保证种群始终向着全局最优的方向进化DE算法具有控制参数少、收敛速度快、求解非线性问题可靠性高等优点,已广泛应用于求解各类问题。然而,DE算法控制参数选择压力大,算法性能对控制参数的依赖性高,种群个体容易陷入早熟收敛、局部最优、搜索停滞等问题,在应用求解过程中具有一定的局限性。The differential evolution algorithm, or DE algorithm, uses a greedy selection mechanism to ensure that the population always evolves towards the global optimum. The DE algorithm has the advantages of fewer control parameters, fast convergence speed, and high reliability in solving nonlinear problems, and has been widely used to solve various problems. However, the DE algorithm has great pressure to select control parameters, and the algorithm performance is highly dependent on the control parameters. Population individuals are prone to problems such as premature convergence, local optimality, and search stagnation, which have certain limitations in the application solution process.
S205、对所述子代个体进行适应性评估,根据适应性筛选得到精英个体。S205. Conduct an adaptability assessment on the offspring individuals, and obtain elite individuals based on adaptability screening.
具体地,对于每个个体,计算其适应度函数值。适应度函数可以是训练误差的衡量指标,例如均方根误差(RMSE)或交叉熵损失。通过在训练集上评估个体的预测性能来计算适应度。对于新生成的个体计算其适应度函数值。将新生成的个体/>与父代个体/>进行比较,保留适应度较好的个体作为精英个体。Specifically, for each individual, its fitness function value is calculated. The fitness function can be a measure of training error, such as root mean square error (RMSE) or cross-entropy loss. Fitness is calculated by evaluating the individual's predictive performance on the training set. For newly generated individuals Calculate its fitness function value. Will the newly generated individual/> With the parent individual/> Compare and retain individuals with better fitness as elite individuals.
S206、将达到迭代条件时的精英个体作为DE-RBFNN模型。S206. Use the elite individuals when the iteration conditions are met as the DE-RBFNN model.
具体地,检查终止条件是否满足。终止条件可以是达到最大迭代次数或适应度函数值达到预先定义的阈值。最终选择适应度最好的个体作为最终的DE-RBFNN模型。Specifically, check whether the termination condition is met. The termination condition can be that the maximum number of iterations is reached or the fitness function value reaches a predefined threshold. Finally, the individual with the best fitness is selected as the final DE-RBFNN model.
S104、计算所述DE-RBFNN模型的高斯函数中心、高斯函数宽度以及隐藏单元和输出单元的权重,对所述DE-RBFNN模型进行参数调整。S104. Calculate the Gaussian function center, Gaussian function width, and weights of hidden units and output units of the DE-RBFNN model, and adjust parameters of the DE-RBFNN model.
在本实施例中,通过K-means聚类算法,将训练集中的小波系数进行聚类,并将聚类中心作为高斯函数的中心,使用均方误差(MSE)作为损失函数,并通过反向传播算法更新权重,通过交叉验证或试错法来选择最优的宽度。可以尝试不同的宽度取值,对每个取值进行模型训练和验证,然后选择在验证集上表现最好的宽度值。计算所有比较模型的相对偏差值,通过计算MAD可以更直接地确认这一点,其中所提出的模型返回训练集和测试集的最佳值。In this embodiment, the K-means clustering algorithm is used to cluster the wavelet coefficients in the training set, and the cluster center is used as the center of the Gaussian function. The mean square error (MSE) is used as the loss function, and through the reverse The propagation algorithm updates the weights and selects the optimal width through cross-validation or trial and error. You can try different width values, train and validate the model for each value, and then choose the width value that performs best on the validation set. The relative deviation values of all compared models are calculated, which can be confirmed more directly by calculating the MAD, where the proposed model returns the best value for the training set and the test set.
高斯函数中心:高斯函数中心表示每个隐藏单元(径向基函数)在输入空间中的位置。每个隐藏单元都有一个对应的高斯函数中心。这些中心点决定了网络在输入空间中对不同特征和样本的响应程度,从而影响网络的表示能力和模型的拟合能力。Gaussian function center: Gaussian function center represents the position of each hidden unit (radial basis function) in the input space. Each hidden unit has a corresponding Gaussian function center. These center points determine the degree to which the network responds to different features and samples in the input space, thereby affecting the representation ability of the network and the fitting ability of the model.
高斯函数宽度:高斯函数宽度决定了径向基函数在输入空间中的覆盖范围。它控制了径向基函数的激活程度和幅度衰减速度。较小的宽度会导致较尖锐的激活函数曲线,对输入空间的局部变化敏感;较大的宽度会导致较平缓的激活函数曲线,对输入空间的整体特征更敏感。高斯函数宽度的选择通常需要在训练过程中进行调优,以使得网络可以更好地拟合数据,并且具有合适的泛化能力。Gaussian function width: Gaussian function width determines the coverage of the radial basis function in the input space. It controls the activation degree and amplitude decay rate of the radial basis function. A smaller width will lead to a sharper activation function curve, which is sensitive to local changes in the input space; a larger width will lead to a flatter activation function curve, which is more sensitive to the overall characteristics of the input space. The choice of Gaussian function width usually needs to be tuned during the training process so that the network can better fit the data and have appropriate generalization capabilities.
隐藏单元和输出单元的权重:隐藏单元和输出单元的权重用于将输入信号传递和转换到下一层。隐藏单元的权重确定了径向基函数对输入数据的加权响应,输出单元的权重确定了输出值的计算。这些权重在训练过程中通过优化算法(如差分进化)进行调整,以最小化网络的预测误差或损失函数。通过调整权重,网络可以学习到数据的特征和模式,以便进行准确的预测和分类。Weights of Hidden Units and Output Units: The weights of hidden units and output units are used to pass and transform the input signal to the next layer. The weights of the hidden units determine the weighted response of the radial basis function to the input data, and the weights of the output units determine the calculation of the output value. These weights are adjusted during training by optimization algorithms such as differential evolution to minimize the network's prediction error or loss function. By adjusting the weights, the network can learn the characteristics and patterns of the data in order to make accurate predictions and classifications.
在本实施例中,高斯函数中心和宽度决定了RBFNN中径向基函数的位置和激活范围,而隐藏单元和输出单元的权重则决定了网络的连接和信号传递。这些参数的选择和调整对于RBFNN的建模能力和性能具有重要影响。In this embodiment, the Gaussian function center and width determine the position and activation range of the radial basis function in RBFNN, while the weights of the hidden units and output units determine the network connection and signal transmission. The selection and adjustment of these parameters have an important impact on the modeling ability and performance of RBFNN.
S105、利用参数调整后的DE-RBFNN模型对未来时间点的负荷进行预测。即在上述高斯函数中心、高斯函数宽度以及隐藏单元和输出单元的权重等参数调整后,将不同频带的小波系数输入参数调整后的DE-RBFNN模型,输出未来连续时间序列的负荷值,实现对未来时间点的负荷进行预测。S105. Use the parameter-adjusted DE-RBFNN model to predict the load at future time points. That is, after the above-mentioned parameters such as the center of the Gaussian function, the width of the Gaussian function, and the weights of the hidden units and output units are adjusted, the wavelet coefficients of different frequency bands are input into the parameter-adjusted DE-RBFNN model, and the load values of the future continuous time series are output to realize the control. Load forecast at future time points.
根据本发明的实施例,提出了短期负荷预测方法,该方法有效处理负荷数据中的高频波动性和非线性难题,提升了预测精度。该方法适用于厂级以上的负荷需求预测,具备更佳的通用性和适用性。According to embodiments of the present invention, a short-term load prediction method is proposed, which effectively handles high-frequency fluctuations and nonlinear problems in load data and improves prediction accuracy. This method is suitable for load demand forecasting at the factory level and above, and has better versatility and applicability.
需要说明的是,对于前述的各方法实施例,为了简单描述,故将其都表述为一系列的动作组合,但是本领域技术人员应该知悉,本发明并不受所描述的动作顺序的限制,因为依据本发明,某些步骤可以采用其他顺序或者同时进行。其次,本领域技术人员也应该知悉,说明书中所描述的实施例均属于可选实施例,所涉及的动作和模块并不一定是本发明所必须的。It should be noted that for the sake of simple description, the foregoing method embodiments are expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence. Because in accordance with the present invention, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily necessary for the present invention.
以上是关于方法实施例的介绍,以下通过装置实施例,对本发明所述方案进行进一步说明。The above is an introduction to the method embodiments. The solution of the present invention will be further described below through device embodiments.
如图3所示,装置300包括:As shown in Figure 3, device 300 includes:
分解模块310,用于获取电力需求时间序列数据,将所述电力需求时间序列数据分解为高通系数和低通系数;Decomposition module 310 is used to obtain power demand time series data and decompose the power demand time series data into high-pass coefficients and low-pass coefficients;
卷积模块320,用于将所述高通系数和低通系数分别与小波函数进行卷积,得到不同频带的小波系数,并按序排列,得到输入特征向量;The convolution module 320 is used to convolve the high-pass coefficient and the low-pass coefficient with the wavelet function respectively to obtain the wavelet coefficients of different frequency bands, and arrange them in order to obtain the input feature vector;
优化模块330,用于将所述输入特征向量通过差分进化算法对小波系数进行优化,得到DE-RBFNN模型;The optimization module 330 is used to optimize the wavelet coefficients of the input feature vector through a differential evolution algorithm to obtain the DE-RBFNN model;
计算模块340,用于计算所述DE-RBFNN模型的高斯函数中心、高斯函数宽度以及隐藏单元和输出单元的权重,对所述DE-RBFNN模型进行参数调整;The calculation module 340 is used to calculate the Gaussian function center, Gaussian function width, and the weight of the hidden unit and the output unit of the DE-RBFNN model, and adjust parameters of the DE-RBFNN model;
预测模块350,用于利用调整后的DE-RBFNN模型对未来时间点的负荷进行预测。The prediction module 350 is used to predict the load at future time points using the adjusted DE-RBFNN model.
在本实施例中,所述分解模块310,将所述电力需求时间序列数据的原始时间序列进行N次滤波,每一次滤波后得到一个近似系数和细节系数,一共得到N个近似系数和N个细节系数;所述N个近似系数作为高通系数;所述N个细节系数作为低通系数。In this embodiment, the decomposition module 310 filters the original time series of the power demand time series data N times, and obtains an approximate coefficient and a detail coefficient after each filtering. A total of N approximate coefficients and N Detail coefficients; the N approximate coefficients serve as high-pass coefficients; the N detail coefficients serve as low-pass coefficients.
在本实施例中,所述优化模块330,具体用于:In this embodiment, the optimization module 330 is specifically used to:
初始化种群;Initialize the population;
在所述种群中随机选取若干不同个体,对所选取个体的向量差进行缩放,与待变异个体进行向量合成,得到变异向量;Randomly select several different individuals from the population, scale the vector differences of the selected individuals, and perform vector synthesis with the individuals to be mutated to obtain a mutation vector;
将基准向量与所述变异向量进行交叉操作;Perform a crossover operation on the reference vector and the mutation vector;
利用差分进化算法对所述种群中的最优个体进行选择,得到子代个体;Use a differential evolution algorithm to select the optimal individual in the population to obtain offspring individuals;
对所述子代个体进行适应性评估,根据适应性筛选得到精英个体;Conduct an adaptability assessment on the offspring individuals, and obtain elite individuals based on adaptability screening;
将达到迭代条件时的精英个体作为DE-RBFNN模型。The elite individuals when reaching the iteration conditions are used as the DE-RBFNN model.
在本实施例中,计算模块340,具体用于:In this embodiment, the calculation module 340 is specifically used for:
通过K-means聚类算法,将所述不同频带的小波系数进行聚类,并将聚类中心作为高斯函数的中心;使用均方误差作为损失函数,通过反向传播算法计算得到隐藏单元和输出单元的权重;通过交叉验证选择最优的宽度作为高斯函数的宽度。The K-means clustering algorithm is used to cluster the wavelet coefficients of different frequency bands, and the cluster center is used as the center of the Gaussian function; the mean square error is used as the loss function, and the hidden unit and output are calculated through the back propagation algorithm. The weight of the unit; the optimal width is selected as the width of the Gaussian function through cross-validation.
所属领域的技术人员可以清楚地了解到,为描述的方便和简洁,所述描述的模块的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiment, and will not be described again here.
本发明的技术方案中,所涉及的用户个人信息的获取,存储和应用等,均符合相关法律法规的规定,且不违背公序良俗。In the technical solution of the present invention, the acquisition, storage and application of the user's personal information are all in compliance with relevant laws and regulations and do not violate public order and good customs.
根据本发明的实施例,本发明还提供了一种电子设备和一种可读存储介质。According to embodiments of the present invention, the present invention also provides an electronic device and a readable storage medium.
图4示出了可以用来实施本发明的实施例的电子设备400的示意性框图。电子设备旨在表示各种形式的数字计算机,诸如,膝上型计算机、台式计算机、工作台、个人数字助理、服务器、刀片式服务器、大型计算机、和其它适合的计算机。电子设备还可以表示各种形式的移动装置,诸如,个人数字处理、蜂窝电话、智能电话、可穿戴设备和其它类似的计算装置。本文所示的部件、它们的连接和关系、以及它们的功能仅仅作为示例,并且不意在限制本文中描述的和/或者要求的本发明的实现。Figure 4 shows a schematic block diagram of an electronic device 400 that may be used to implement embodiments of the invention. Electronic devices are intended to refer to various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are examples only and are not intended to limit the implementation of the invention described and/or claimed herein.
设备400包括计算单元401,其可以根据存储在只读存储器(ROM)402中的计算机程序或者从存储单元408加载到随机访问存储器(RAM)403中的计算机程序,来执行各种适当的动作和处理。在RAM 403中,还可存储设备400操作所需的各种程序和数据。计算单元401、ROM 402以及RAM 403通过总线404彼此相连。输入/输出(I/O)接口405也连接至总线404。The device 400 includes a computing unit 401 that can perform various appropriate actions according to a computer program stored in a read-only memory (ROM) 402 or loaded from a storage unit 408 into a random access memory (RAM) 403 and deal with. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. Computing unit 401, ROM 402 and RAM 403 are connected to each other via bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
设备400中的多个部件连接至I/O接口405,包括:输入单元406,例如键盘、鼠标等;输出单元407,例如各种类型的显示器、扬声器等;存储单元408,例如磁盘、光盘等;以及通信单元409,例如网卡、调制解调器、无线通信收发机等。通信单元409允许设备400通过诸如因特网的计算机网络和/或各种电信网络与其他设备交换信息/数据。Multiple components in the device 400 are connected to the I/O interface 405, including: input unit 406, such as a keyboard, mouse, etc.; output unit 407, such as various types of displays, speakers, etc.; storage unit 408, such as a magnetic disk, optical disk, etc. ; and communication unit 409, such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices through computer networks such as the Internet and/or various telecommunications networks.
计算单元401可以是各种具有处理和计算能力的通用和/或专用处理组件。计算单元401的一些示例包括但不限于中央处理单元(CPU)、图形处理单元(GPU)、各种专用的人工智能(AI)计算芯片、各种运行机器学习模型算法的计算单元、数字信号处理器(DSP)、以及任何适当的处理器、控制器、微控制器等。计算单元401执行上文所描述的各个方法和处理,例如方法S101~S105。例如,在一些实施例中,方法S101~S105可被实现为计算机软件程序,其被有形地包含于机器可读介质,例如存储单元408。在一些实施例中,计算机程序的部分或者全部可以经由ROM 402和/或通信单元409而被载入和/或安装到设备400上。当计算机程序加载到RAM 403并由计算单元401执行时,可以执行上文描述的方法S101~S105的一个或多个步骤。备选地,在其他实施例中,计算单元401可以通过其他任何适当的方式(例如,借助于固件)而被配置为执行方法S101~S105。Computing unit 401 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 executes various methods and processes described above, such as methods S101 to S105. For example, in some embodiments, methods S101 to S105 may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 400 via ROM 402 and/or communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the methods S101 to S105 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform methods S101 to S105 in any other suitable manner (eg, by means of firmware).
本文中以上描述的系统和技术的各种实施方式可以在数字电子电路系统、集成电路系统、场可编程门阵列(FPGA)、专用集成电路(ASIC)、专用标准产品(ASSP)、芯片上系统的系统(SOC)、负载可编程逻辑设备(CPLD)、计算机硬件、固件、软件、和/或它们的组合中实现。这些各种实施方式可以包括:实施在一个或者多个计算机程序中,该一个或者多个计算机程序可在包括至少一个可编程处理器的可编程系统上执行和/或解释,该可编程处理器可以是专用或者通用可编程处理器,可以从存储系统、至少一个输入装置、和至少一个输出装置接收数据和指令,并且将数据和指令传输至该存储系统、该至少一个输入装置、和该至少一个输出装置。Various implementations of the systems and techniques described above may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip implemented in a system (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or a combination thereof. These various embodiments may include implementation in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor The processor, which may be a special purpose or general purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device. An output device.
用于实施本发明的方法的程序代码可以采用一个或多个编程语言的任何组合来编写。这些程序代码可以提供给通用计算机、专用计算机或其他可编程数据处理装置的处理器或控制器,使得程序代码当由处理器或控制器执行时使流程图和/或框图中所规定的功能/操作被实施。程序代码可以完全在机器上执行、部分地在机器上执行,作为独立软件包部分地在机器上执行且部分地在远程机器上执行或完全在远程机器或服务器上执行。Program code for implementing the methods of the invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions specified in the flowcharts and/or block diagrams/ The operation is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
在本发明的上下文中,机器可读介质可以是有形的介质,其可以包含或存储以供指令执行系统、装置或设备使用或与指令执行系统、装置或设备结合地使用的程序。机器可读介质可以是机器可读信号介质或机器可读储存介质。机器可读介质可以包括但不限于电子的、磁性的、光学的、电磁的、红外的、或半导体系统、装置或设备,或者上述内容的任何合适组合。机器可读存储介质的更具体示例会包括基于一个或多个线的电气连接、便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM或快闪存储器)、光纤、便捷式紧凑盘只读存储器(CD-ROM)、光学储存设备、磁储存设备、或上述内容的任何合适组合。In the context of this disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, laptop disks, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
为了提供与用户的交互,可以在计算机上实施此处描述的系统和技术,该计算机具有:用于向用户显示信息的显示装置(例如,CRT(阴极射线管)或者LCD(液晶显示器)监视器);以及键盘和指向装置(例如,鼠标或者轨迹球),用户可以通过该键盘和该指向装置来将输入提供给计算机。其它种类的装置还可以用于提供与用户的交互;例如,提供给用户的反馈可以是任何形式的传感反馈(例如,视觉反馈、听觉反馈、或者触觉反馈);并且可以用任何形式(包括声输入、语音输入或者、触觉输入)来接收来自用户的输入。To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user ); and a keyboard and pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and may be provided in any form, including Acoustic input, voice input or tactile input) to receive input from the user.
可以将此处描述的系统和技术实施在包括后台部件的计算系统(例如,作为数据服务器)、或者包括中间件部件的计算系统(例如,应用服务器)、或者包括前端部件的计算系统(例如,具有图形用户界面或者网络浏览器的用户计算机,用户可以通过该图形用户界面或者该网络浏览器来与此处描述的系统和技术的实施方式交互)、或者包括这种后台部件、中间件部件、或者前端部件的任何组合的计算系统中。可以通过任何形式或者介质的数字数据通信(例如,通信网络)来将系统的部件相互连接。通信网络的示例包括:局域网(LAN)、广域网(WAN)和互联网。The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., A user's computer having a graphical user interface or web browser through which the user can interact with implementations of the systems and technologies described herein), or including such backend components, middleware components, or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communications network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.
计算机系统可以包括客户端和服务器。客户端和服务器一般远离彼此并且通常通过通信网络进行交互。通过在相应的计算机上运行并且彼此具有客户端-服务器关系的计算机程序来产生客户端和服务器的关系。服务器可以是云服务器,也可以为分布式系统的服务器,或者是结合了区块链的服务器。Computer systems may include clients and servers. Clients and servers are generally remote from each other and typically interact over a communications network. The relationship of client and server is created by computer programs running on corresponding computers and having a client-server relationship with each other. The server can be a cloud server, a distributed system server, or a server combined with a blockchain.
应该理解,可以使用上面所示的各种形式的流程,重新排序、增加或删除步骤。例如,本发明中记载的各步骤可以并行地执行也可以顺序地执行也可以不同的次序执行,只要能够实现本发明的技术方案所期望的结果,本文在此不进行限制。It should be understood that various forms of the process shown above may be used, with steps reordered, added or deleted. For example, each step described in the present invention can be executed in parallel, sequentially, or in different orders. As long as the desired results of the technical solution of the present invention can be achieved, there is no limitation here.
上述具体实施方式,并不构成对本发明保护范围的限制。本领域技术人员应该明白的是,根据设计要求和其他因素,可以进行各种修改、组合、子组合和替代。任何在本发明的精神和原则之内所作的修改、等同替换和改进等,均应包含在本发明保护范围之内。The above-mentioned specific embodiments do not constitute a limitation on the scope of the present invention. It will be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions are possible depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.
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CN118194137B (en) * | 2024-05-16 | 2024-09-13 | 国网江西省电力有限公司南昌供电分公司 | Block chain-based carbon emission monitoring method |
CN118246639A (en) * | 2024-05-27 | 2024-06-25 | 广东大爱天下能源集团有限公司 | Electric power intelligent management method and system based on artificial intelligence |
CN118246639B (en) * | 2024-05-27 | 2024-07-30 | 广东大爱天下能源集团有限公司 | Electric power intelligent management method and system based on artificial intelligence |
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