CN104636822B - A kind of resident load prediction technique based on elman neural networks - Google Patents
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Abstract
本发明公开了一种基于elman神经网络的居民负荷预测方法,包括:获取上一年度的居民负荷历史数据以及对应的历史天气参数数据;计算各个月份的居民负荷的季节指数;采用季节指数对居民负荷历史数据进行修正;确定神经网络的输入和输出数据,并确定最优的隐含层神经元个数,从而建立基于elman的神经网络;对修正后的居民负荷历史数据以及对应的历史天气参数数据进行归一化处理,进而对建立的神经网络进行训练,将预测误差控制在预设范围内;采用训练后的神经网络对居民负荷进行预测。本发明具有适应时变特性和居民负荷的季节波动性的能力,能直接预测并反映居民负荷的动态特性,预测精度较高,可广泛应用于电力系统的负荷预测领域中。
The invention discloses a resident load forecasting method based on elman neural network, which includes: obtaining historical data of resident load and corresponding historical weather parameter data in the previous year; calculating the seasonal index of resident load in each month; Correct the historical load data; determine the input and output data of the neural network, and determine the optimal number of neurons in the hidden layer, so as to establish a neural network based on elman; correct the historical load data of residents and the corresponding historical weather parameters The data is normalized, and then the established neural network is trained to control the prediction error within the preset range; the trained neural network is used to predict the load of residents. The invention has the ability to adapt to time-varying characteristics and seasonal fluctuations of residential loads, can directly predict and reflect the dynamic characteristics of residential loads, has high prediction accuracy, and can be widely used in the field of load prediction of electric power systems.
Description
技术领域technical field
本发明涉及电力系统负荷预测技术领域,特别是涉及一种基于elman神经网络的居民负荷预测方法。The invention relates to the technical field of power system load forecasting, in particular to a residential load forecasting method based on an elman neural network.
背景技术Background technique
电力作为一种重要能源,在日常生活以及工作中都起着举足轻重的作用,随着国民经济的快速发展,全社会用电量以及各个产业用电量也稳定增长,因此用电量的使用趋势不但影响电网经营企业的生产经营决策及经济效益,还会影响到社会经济的趋势分析。合理地进行电力负荷预测是电力系统对电力资源进行调度、规划的前提条件。As an important energy source, electricity plays a pivotal role in daily life and work. With the rapid development of the national economy, the electricity consumption of the whole society and various industries has also increased steadily. Therefore, the trend of electricity consumption It not only affects the production and operation decision-making and economic benefits of power grid operating enterprises, but also affects the trend analysis of social economy. Reasonable power load forecasting is the precondition for power system to dispatch and plan power resources.
电力负荷一般可以分为工业负荷、商业负荷、居民负荷等,其中工业负荷和商业负荷在电力负荷中的比重较高,电网企业历来对这块的负荷预测比较重视,并陆续建成了负荷控制系统和用电信息采集系统以完成对工商业负荷的数据采集和负荷预测;居民用户负荷由于分布分散、规模偏小的特点,一直采取的都是集中预测的方法,即以台区或馈线负荷为单位进行预测,这种预测方法的缺点就是精度不高,尤其随着居民家用电器的逐年增多、电动自行车的普及和电动汽车的逐步推广,居民用户的用电负荷呈现稳步增长趋势和明显的季节性波动,通过集中预测的方法对居民用户负荷预测的弊病愈发显现。Power loads can generally be divided into industrial loads, commercial loads, residential loads, etc. Among them, industrial loads and commercial loads account for a relatively high proportion of power loads. Power grid companies have always paid more attention to this load forecasting, and have successively built load control systems. and electricity consumption information collection system to complete the data collection and load forecasting of industrial and commercial loads; due to the characteristics of scattered distribution and small scale of residential user loads, the method of centralized forecasting has been adopted all the time, that is, the unit of station area or feeder load Forecasting, the disadvantage of this forecasting method is that the accuracy is not high, especially with the increase of household appliances year by year, the popularity of electric bicycles and the gradual promotion of electric vehicles, the electricity load of residential users shows a steady growth trend and obvious seasonality Fluctuations, the disadvantages of residential user load forecasting through centralized forecasting methods are becoming more and more apparent.
发明内容Contents of the invention
为了解决上述的技术问题,本发明的目的是提供一种基于elman神经网络的居民负荷预测方法。In order to solve the above-mentioned technical problems, the purpose of this invention is to provide a kind of resident load forecasting method based on elman neural network.
本发明解决其技术问题所采用的技术方案是:The technical solution adopted by the present invention to solve its technical problems is:
一种基于elman神经网络的居民负荷预测方法,包括:A resident load forecasting method based on elman neural network, including:
S1、获取上一年度的居民负荷历史数据以及对应的历史天气参数数据,同时对该年度中的有效天数进行日期类型划分;S1. Obtain the historical data of residents' load and the corresponding historical weather parameter data in the previous year, and at the same time divide the valid days in the year into date types;
S2、根据获取的居民负荷历史数据,计算各个月份的居民负荷的同期平均数,进而计算所有同期平均数的总平均值后,将每个同期平均数与总平均值相除获得季节指数;S2. According to the acquired historical data of resident load, calculate the average number of residents' load in each month, and then calculate the total average value of all averages in the same period, and divide each average number by the total average value to obtain the seasonal index;
S3、采用季节指数对居民负荷历史数据进行修正,将各个月份的居民负荷历史数据除以对应的季节指数后,获得修正后的居民负荷历史数据;S3. Use the seasonal index to correct the historical data of residents' load, divide the historical data of residents' load in each month by the corresponding seasonal index, and obtain the corrected historical data of residents' load;
S4、确定神经网络的输入和输出数据,并确定最优的隐含层神经元个数,从而建立基于elman的神经网络;S4. Determine the input and output data of the neural network, and determine the optimal number of neurons in the hidden layer, thereby establishing a neural network based on elman;
S5、对修正后的居民负荷历史数据以及对应的历史天气参数数据进行归一化处理,进而根据归一化处理后的数据对建立的神经网络进行训练,将神经网络的预测误差控制在预设范围内;S5. Normalize the corrected historical resident load data and the corresponding historical weather parameter data, and then train the established neural network according to the normalized processed data, and control the prediction error of the neural network to a preset value within the scope;
S6、获取预测日前一周的居民负荷历史数据、预测日的天气参数数据和日期类型作为神经网络的输入,采用训练后的神经网络对预测日的居民负荷进行预测,进而将获得的预测数据乘以季节指数后得到居民负荷预测数据;S6. Obtain the historical data of residents' load one week before the forecast day, the weather parameter data and date type of the forecast day as the input of the neural network, use the trained neural network to predict the residents' load on the forecast day, and then multiply the obtained forecast data by Residential load forecast data is obtained after the seasonal index;
所述日期类型划分为休息日和工作日两种类型。The date type is divided into two types: rest day and working day.
进一步,所述步骤S5中所述预设范围为5%~10%。Further, the preset range in the step S5 is 5%-10%.
进一步,所述居民负荷历史数据包括每个小时的居民负荷数据,所述历史天气参数数据包括气温、日照时间和天气类型。Further, the historical resident load data includes resident load data for each hour, and the historical weather parameter data includes air temperature, sunshine time and weather type.
进一步,所述步骤S4,包括:Further, the step S4 includes:
S41、统计获取的居民负荷历史数据、历史天气参数数据和日期类型,将任一日的居民负荷数据作为神经网络的输出数据,同时将该日之前一周内的每个小时的居民负荷数据以及该日的天气参数数据和日期类型作为神经网络的输入数据;S41. Statistically obtain the historical data of resident load, historical weather parameter data and date type, use the resident load data of any day as the output data of the neural network, and at the same time the resident load data of each hour in the week before the day and the The daily weather parameter data and date type are used as the input data of the neural network;
S42、对神经网络进行初始化,根据输入输出序列确定输入结点单元向量、隐含层结点单元向量、反馈状态向量和输出结点向量,从而建立起基于elman的神经网络。S42. Initialize the neural network, and determine the input node unit vector, the hidden layer node unit vector, the feedback state vector and the output node vector according to the input and output sequences, thereby establishing an elman-based neural network.
进一步,所述基于elman的神经网络的非线性状态空间表达式为:Further, the nonlinear state space expression of the neural network based on elman is:
其中,y(k)表示m维输出节点向量,l(k)表示m维隐含层节点单元向量,x(k)表示u维输入向量,c(k)表示n维反馈状态向量,w3表示隐含层到输出层的连接权值,w2表示输入层到隐含层的连接权值,w1表示承接层到隐含层的连接权值,g(*)表示输出神经元的传递函数,f(*)表示隐含层神经元的传递函数。Among them, y(k) represents the m-dimensional output node vector, l(k) represents the m-dimensional hidden layer node unit vector, x(k) represents the u-dimensional input vector, c(k) represents the n-dimensional feedback state vector, w 3 Represents the connection weight from the hidden layer to the output layer, w 2 represents the connection weight from the input layer to the hidden layer, w 1 represents the connection weight from the receiving layer to the hidden layer, g(*) represents the transmission of the output neuron Function, f(*) represents the transfer function of hidden layer neurons.
进一步,所述步骤S5,包括:Further, the step S5 includes:
S51、根据下式对修正后的居民负荷历史数据以及对应的历史天气参数数据进行归一化处理:S51. Perform normalization processing on the corrected historical resident load data and corresponding historical weather parameter data according to the following formula:
其中,xk表示居民负荷历史数据序列或历史天气参数数据数列中的第k个参数值,k为自然数,xmax表示xk所在数据序列中的最大值,xmin表示xk所在数据序列中的最小值;Among them, x k represents the kth parameter value in the historical data sequence of residents' load or the historical weather parameter data sequence, k is a natural number, x max represents the maximum value in the data sequence where x k is located, and x min represents the data sequence where x k is located the minimum value;
S52、根据归一化处理后的数据对建立的神经网络进行误差计算、权值更新和阀值更新,进而将基于elman的神经网络的预测误差控制在预设范围内。S52. Perform error calculation, weight update, and threshold update on the established neural network according to the normalized data, and then control the prediction error of the elman-based neural network within a preset range.
进一步,所述基于elman的神经网络采用BP算法进行权值修正更新,并采用误差平方和函数进行指标函数学习,所述指标函数学习的公式为:Further, the elman-based neural network uses the BP algorithm to correct and update the weights, and uses the error sum of squares function to learn the indicator function, and the formula for the indicator function learning is:
上式中E(x)表示指标函数,表示目标输入向量。In the above formula, E(x) represents the index function, represents the target input vector.
进一步,所述步骤S6,包括:Further, the step S6 includes:
S61、获取预测日前一周的居民负荷历史数据、预测日的天气参数数据和日期类型作为神经网络的输入,采用训练后的神经网络对预测日的居民负荷进行预测,进而获得预测日当天每小时的预测数据;S61. Obtain the historical data of residents' load one week before the forecast day, the weather parameter data and date type of the forecast day as the input of the neural network, use the trained neural network to predict the residents' load on the forecast day, and then obtain the hourly load of the forecast day. forecast data;
S62、将获得的预测数据乘以季节指数后,获得每小时的居民负荷预测数据。S62. Obtain hourly resident load forecast data after multiplying the obtained forecast data by the seasonal index.
进一步,所述步骤S62之后还包括以下步骤:Further, after the step S62, the following steps are also included:
S63、获取预测日当天的实际负荷数据后,计算获得的居民负荷预测数据与实际负荷数据之间的误差值,并将误差值反馈至神经网络。S63. After obtaining the actual load data on the forecast day, calculate an error value between the obtained resident load forecast data and the actual load data, and feed back the error value to the neural network.
本发明的有益效果是:本发明的一种基于elman神经网络的居民负荷预测方法,包括:获取上一年度的居民负荷历史数据以及对应的历史天气参数数据,同时对该年度中的有效天数进行日期类型划分;根据获取的居民负荷历史数据,计算各个月份的居民负荷的同期平均数,进而计算所有同期平均数的总平均值后,将每个同期平均数与总平均值相除获得季节指数;采用季节指数对居民负荷历史数据进行修正,将各个月份的居民负荷历史数据除以对应的季节指数后,获得修正后的居民负荷历史数据;确定神经网络的输入和输出数据,并确定最优的隐含层神经元个数,从而建立基于elman的神经网络;对修正后的居民负荷历史数据以及对应的历史天气参数数据进行归一化处理,进而根据归一化处理后的数据对建立的神经网络进行训练,将神经网络的预测误差控制在预设范围内;获取预测日前一周的居民负荷历史数据、预测日的天气参数数据和日期类型作为神经网络的输入,采用训练后的神经网络对预测日的居民负荷进行预测,进而将获得的预测数据乘以季节指数后得到居民负荷预测数据。本方法通过基于季节指数的Elman神经网络的构建,结合相关地区居民负荷历史数据和相对应的历史天气参数数据,可预测获得预测日的居民负荷数据,而且能够以任意的精度逼近任意非线性映射,并不考虑外部噪声的影响,具有较高的精度,并具有适应时变特性和居民负荷的季节波动性的能力,能直接预测并反映居民负荷的动态特性,预测精度较高。而且本方法增加了季节指数特征,可以克服居民负荷的季节波动性较大和历史数据利用不完全等问题,可以有效提高预测数据的精度和预测稳定性。The beneficial effect of the present invention is: a kind of resident load forecasting method based on elman neural network of the present invention, comprises: obtain last year's resident load historical data and corresponding historical weather parameter data, carry out the valid days number in this year at the same time Date type division; according to the obtained historical data of resident load, calculate the average number of residents' load in each month, and then calculate the total average value of all averages in the same period, and divide each average number in the same period by the total average value to obtain the seasonal index ; Use the seasonal index to correct the historical data of residents' load, divide the historical data of residents' load in each month by the corresponding seasonal index, and obtain the corrected historical data of residents' load; determine the input and output data of the neural network, and determine the optimal The number of neurons in the hidden layer of the hidden layer, so as to establish a neural network based on elman; normalize the corrected historical data of residents' load and the corresponding historical weather parameter data, and then use the normalized data to establish The neural network is trained to control the prediction error of the neural network within the preset range; the historical data of residents' load one week before the forecast day, the weather parameter data and date type of the forecast day are taken as the input of the neural network, and the trained neural network is used to The residents' load on the forecast day is predicted, and then the obtained forecast data is multiplied by the seasonal index to obtain the residents' load forecast data. Through the construction of Elman neural network based on seasonal index, this method can predict the residents' load data on the forecast day by combining the historical data of residents' load in relevant areas and the corresponding historical weather parameter data, and can approach any nonlinear mapping with arbitrary precision , does not consider the influence of external noise, has high accuracy, and has the ability to adapt to time-varying characteristics and seasonal fluctuations of resident loads, can directly predict and reflect the dynamic characteristics of resident loads, and has high prediction accuracy. Moreover, this method adds seasonal index features, which can overcome the problems of large seasonal fluctuations of residents' load and incomplete utilization of historical data, and can effectively improve the accuracy and stability of forecast data.
附图说明Description of drawings
下面结合附图和实施例对本发明作进一步说明。The present invention will be further described below in conjunction with drawings and embodiments.
图1是本发明的一种基于elman神经网络的居民负荷预测方法的流程示图;Fig. 1 is a kind of flow diagram of the resident's load forecasting method based on elman neural network of the present invention;
图2是本发明的一种基于elman神经网络的居民负荷预测方法建立的神经网络的结构示意图。Fig. 2 is a structural schematic diagram of a neural network established by a resident load forecasting method based on an elman neural network in the present invention.
具体实施方式Detailed ways
为了便于下文的描述,首先给出以下名词解释:For the convenience of the following description, the following noun explanations are first given:
Elman网络:J.L.Elman于1990年首先针对语音处理问题而提出来的,它是一种典型的局部回归网络(global feed for ward local recurrent)。Elman network: J.L.Elman first proposed it for speech processing in 1990. It is a typical local regression network (global feed for ward local recurrent).
BP算法:Error Back Propagation Algorithm,误差反向传播算法,简称BP算法。BP algorithm: Error Back Propagation Algorithm, error back propagation algorithm, referred to as BP algorithm.
参照图1,本发明提供了一种基于elman神经网络的居民负荷预测方法,包括:With reference to Fig. 1, the present invention provides a kind of resident load prediction method based on elman neural network, comprising:
S1、获取上一年度的居民负荷历史数据以及对应的历史天气参数数据,同时对该年度中的有效天数进行日期类型划分;S1. Obtain the historical data of residents' load and the corresponding historical weather parameter data in the previous year, and at the same time divide the valid days in the year into date types;
S2、根据获取的居民负荷历史数据,计算各个月份的居民负荷的同期平均数,进而计算所有同期平均数的总平均值后,将每个同期平均数与总平均值相除获得季节指数;S2. According to the acquired historical data of resident load, calculate the average number of residents' load in each month, and then calculate the total average value of all averages in the same period, and divide each average number by the total average value to obtain the seasonal index;
S3、采用季节指数对居民负荷历史数据进行修正,将各个月份的居民负荷历史数据除以对应的季节指数后,获得修正后的居民负荷历史数据;S3. Use the seasonal index to correct the historical data of residents' load, divide the historical data of residents' load in each month by the corresponding seasonal index, and obtain the corrected historical data of residents' load;
S4、确定神经网络的输入和输出数据,并确定最优的隐含层神经元个数,从而建立基于elman的神经网络;S4. Determine the input and output data of the neural network, and determine the optimal number of neurons in the hidden layer, thereby establishing a neural network based on elman;
S5、对修正后的居民负荷历史数据以及对应的历史天气参数数据进行归一化处理,进而根据归一化处理后的数据对建立的神经网络进行训练,将神经网络的预测误差控制在预设范围内;S5. Normalize the corrected historical resident load data and the corresponding historical weather parameter data, and then train the established neural network according to the normalized processed data, and control the prediction error of the neural network to a preset value within the scope;
S6、获取预测日前一周的居民负荷历史数据、预测日的天气参数数据和日期类型作为神经网络的输入,采用训练后的神经网络对预测日的居民负荷进行预测,进而将获得的预测数据乘以季节指数后得到居民负荷预测数据;S6. Obtain the historical data of residents' load one week before the forecast day, the weather parameter data and date type of the forecast day as the input of the neural network, use the trained neural network to predict the residents' load on the forecast day, and then multiply the obtained forecast data by Residential load forecast data is obtained after the seasonal index;
所述日期类型划分为休息日和工作日两种类型。The date type is divided into two types: rest day and working day.
进一步作为优选的实施方式,所述步骤S5中所述预设范围为5%~10%。As a further preferred embodiment, the preset range in step S5 is 5%-10%.
进一步作为优选的实施方式,所述居民负荷历史数据包括每个小时的居民负荷数据,所述历史天气参数数据包括气温、日照时间和天气类型。As a further preferred embodiment, the historical data of residents' load includes the data of residents' load every hour, and the historical weather parameter data includes air temperature, sunshine time and weather type.
进一步作为优选的实施方式,所述步骤S4,包括:Further as a preferred embodiment, the step S4 includes:
S41、统计获取的居民负荷历史数据、历史天气参数数据和日期类型,将任一日的居民负荷数据作为神经网络的输出数据,同时将该日之前一周内的每个小时的居民负荷数据以及该日的天气参数数据和日期类型作为神经网络的输入数据;S41. Statistically obtain the historical data of resident load, historical weather parameter data and date type, use the resident load data of any day as the output data of the neural network, and at the same time the resident load data of each hour in the week before the day and the The daily weather parameter data and date type are used as the input data of the neural network;
S42、对神经网络进行初始化,根据输入输出序列确定输入结点单元向量、隐含层结点单元向量、反馈状态向量和输出结点向量,从而建立起基于elman的神经网络。S42. Initialize the neural network, and determine the input node unit vector, the hidden layer node unit vector, the feedback state vector and the output node vector according to the input and output sequences, thereby establishing an elman-based neural network.
进一步作为优选的实施方式,所述基于elman的神经网络的非线性状态空间表达式为:Further as a preferred embodiment, the nonlinear state space expression of the neural network based on elman is:
其中,y(k)表示m维输出节点向量,l(k)表示m维隐含层节点单元向量,x(k)表示u维输入向量,c(k)表示n维反馈状态向量,w3表示隐含层到输出层的连接权值,w2表示输入层到隐含层的连接权值,w1表示承接层到隐含层的连接权值,g(*)表示输出神经元的传递函数,f(*)表示隐含层神经元的传递函数。Among them, y(k) represents the m-dimensional output node vector, l(k) represents the m-dimensional hidden layer node unit vector, x(k) represents the u-dimensional input vector, c(k) represents the n-dimensional feedback state vector, w 3 Represents the connection weight from the hidden layer to the output layer, w 2 represents the connection weight from the input layer to the hidden layer, w 1 represents the connection weight from the receiving layer to the hidden layer, g(*) represents the transmission of the output neuron Function, f(*) represents the transfer function of hidden layer neurons.
进一步作为优选的实施方式,所述步骤S5,包括:Further as a preferred embodiment, the step S5 includes:
S51、根据下式对修正后的居民负荷历史数据以及对应的历史天气参数数据进行归一化处理:S51. Perform normalization processing on the corrected historical resident load data and corresponding historical weather parameter data according to the following formula:
其中,xk表示居民负荷历史数据序列或历史天气参数数据数列中的第k个参数值,k为自然数,xmax表示xk所在数据序列中的最大值,xmin表示xk所在数据序列中的最小值;Among them, x k represents the kth parameter value in the historical data sequence of residents' load or the historical weather parameter data sequence, k is a natural number, x max represents the maximum value in the data sequence where x k is located, and x min represents the data sequence where x k is located the minimum value;
S52、根据归一化处理后的数据对建立的神经网络进行误差计算、权值更新和阀值更新,进而将基于elman的神经网络的预测误差控制在预设范围内。S52. Perform error calculation, weight update, and threshold update on the established neural network according to the normalized data, and then control the prediction error of the elman-based neural network within a preset range.
进一步作为优选的实施方式,所述基于elman的神经网络采用BP算法进行权值修正更新,并采用误差平方和函数进行指标函数学习,所述指标函数学习的公式为:Further as a preferred embodiment, the elman-based neural network uses the BP algorithm to correct and update the weights, and uses the error sum of squares function to learn the indicator function, and the formula for learning the indicator function is:
上式中E(x)表示指标函数,表示目标输入向量。In the above formula, E(x) represents the index function, represents the target input vector.
进一步作为优选的实施方式,所述步骤S6,包括:Further as a preferred implementation manner, the step S6 includes:
S61、获取预测日前一周的居民负荷历史数据、预测日的天气参数数据和日期类型作为神经网络的输入,采用训练后的神经网络对预测日的居民负荷进行预测,进而获得预测日当天每小时的预测数据;S61. Obtain the historical data of residents' load one week before the forecast day, the weather parameter data and date type of the forecast day as the input of the neural network, use the trained neural network to predict the residents' load on the forecast day, and then obtain the hourly load of the forecast day. forecast data;
S62、将获得的预测数据乘以季节指数后,获得每小时的居民负荷预测数据。S62. Obtain hourly resident load forecast data after multiplying the obtained forecast data by the seasonal index.
进一步作为优选的实施方式,所述步骤S62之后还包括以下步骤:Further as a preferred implementation manner, after the step S62, the following steps are also included:
S63、获取预测日当天的实际负荷数据后,计算获得的居民负荷预测数据与实际负荷数据之间的误差值,并将误差值反馈至神经网络。S63. After obtaining the actual load data on the forecast day, calculate an error value between the obtained resident load forecast data and the actual load data, and feed back the error value to the neural network.
下面结合具体实施例对本发明做进一步说明。The present invention will be further described below in conjunction with specific embodiments.
参照图1,一种基于elman神经网络的居民负荷预测方法,包括:Referring to Figure 1, a resident load forecasting method based on the elman neural network includes:
S1、获取上一年度的居民负荷历史数据以及对应的历史天气参数数据,同时对该年度中的有效天数进行日期类型划分,本实施例将日期类型划分为休息日和工作日两种类型;S1. Obtain the historical data of residents' load and the corresponding historical weather parameter data in the previous year, and at the same time divide the valid days in the year into date types. In this embodiment, the date types are divided into two types: rest days and working days;
S2、根据获取的居民负荷历史数据,计算各个月份的居民负荷的同期平均数,进而计算所有同期平均数的总平均值后,将每个同期平均数与总平均值相除获得季节指数;S2. According to the acquired historical data of resident load, calculate the average number of residents' load in each month, and then calculate the total average value of all averages in the same period, and divide each average number by the total average value to obtain the seasonal index;
S3、采用季节指数对居民负荷历史数据进行修正,将各个月份的居民负荷历史数据除以对应的季节指数后,获得修正后的居民负荷历史数据;S3. Use the seasonal index to correct the historical data of residents' load, divide the historical data of residents' load in each month by the corresponding seasonal index, and obtain the corrected historical data of residents' load;
S4、确定神经网络的输入和输出数据,并确定最优的隐含层神经元个数,从而建立基于elman的神经网络;基于elman的神经网络包括输入层、隐含层、承接层和输出层,承接层用于记忆隐含层前一时刻的输出值并将该输出值返回给隐含层的输入,增加了反馈,对历史数据较为敏感,较为稳定;S4. Determine the input and output data of the neural network, and determine the optimal number of neurons in the hidden layer, thereby establishing a neural network based on elman; a neural network based on elman includes an input layer, a hidden layer, a succession layer and an output layer , the receiving layer is used to remember the output value of the hidden layer at the previous moment and return the output value to the input of the hidden layer, which increases feedback, is more sensitive to historical data, and is more stable;
S5、对修正后的居民负荷历史数据以及对应的历史天气参数数据进行归一化处理,进而根据归一化处理后的数据对建立的神经网络进行训练,将神经网络的预测误差控制在预设范围内;本实施例中,预设范围为5%~10%;进行误差控制,能够以任意精度逼近任意非线性映射;S5. Normalize the corrected historical resident load data and the corresponding historical weather parameter data, and then train the established neural network according to the normalized processed data, and control the prediction error of the neural network to a preset value Within the scope; in the present embodiment, preset range is 5%~10%; Carry out error control, can approach arbitrary nonlinear mapping with arbitrary precision;
S6、获取预测日前一周的居民负荷历史数据、预测日的天气参数数据和日期类型作为神经网络的输入,采用训练后的神经网络对预测日的居民负荷进行预测,进而将获得的预测数据乘以季节指数后得到居民负荷预测数据;S6. Obtain the historical data of residents' load one week before the forecast day, the weather parameter data and date type of the forecast day as the input of the neural network, use the trained neural network to predict the residents' load on the forecast day, and then multiply the obtained forecast data by Residential load forecast data is obtained after the seasonal index;
居民负荷历史数据包括每个小时的居民负荷数据,历史天气参数数据包括气温、日照时间和天气类型。Residential load historical data includes hourly residential load data, and historical weather parameter data includes temperature, sunshine time and weather type.
具体地,步骤S4包括步骤S41~S42:Specifically, step S4 includes steps S41-S42:
S41、统计获取的居民负荷历史数据、历史天气参数数据和日期类型,将任一日的居民负荷数据作为神经网络的输出数据,同时将该日之前一周内的每个小时的居民负荷数据以及该日的天气参数数据和日期类型作为神经网络的输入数据;输入数据中居民负荷数据共计168个负荷点,输出数据的居民负荷数据共计24个负荷点;S41. Statistically obtain the historical data of resident load, historical weather parameter data and date type, use the resident load data of any day as the output data of the neural network, and at the same time the resident load data of each hour in the week before the day and the The daily weather parameter data and date type are used as the input data of the neural network; there are 168 load points in the input data, and 24 load points in the output data;
S42、对神经网络进行初始化,根据输入输出序列(X,Y)确定u维输入结点单元向量x、n维隐含层结点单元向量l、n维反馈状态向量c和m维输出结点向量y,从而建立起基于elman的神经网络,本实施例建立的神经网络训练模型如图2所示。其中,X1,X2···Xu是输入层的节点,对应输入的预测日的天气参数数据,上一周修正后居民用户负荷和日期类型;Y1是输出层的节点,对应输出的预测日系统居民用户负荷;l1,l2···lN是隐含层的节点,其中隐含层节点数n(即最优的隐含层神经元个数)通过逐渐递增试凑法的,即根据逐渐增加试探的办法来确定;C1,C2···CN是承接层的节点,用来记忆隐含层单元前一时刻的输出值并返回给隐含层的输入。S42. Initialize the neural network, and determine u-dimensional input node unit vector x, n-dimensional hidden layer node unit vector l, n-dimensional feedback state vector c and m-dimensional output node according to the input and output sequence (X, Y) Vector y, thereby establishing a neural network based on elman, the neural network training model established in this embodiment is shown in Figure 2. Among them, X1, X2···Xu are the nodes of the input layer, corresponding to the input weather parameter data of the forecast day, the load and date type of the residents after correction in the previous week; Y1 is the node of the output layer, corresponding to the output forecast day system residents User load; l1, l2...lN are hidden layer nodes, where the number of hidden layer nodes n (that is, the optimal number of hidden layer neurons) is gradually increased by the trial and error method, that is, according to the gradually increased trial C1, C2... CN is the node of the receiving layer, which is used to memorize the output value of the hidden layer unit at the previous moment and return it to the input of the hidden layer.
本实施例中,基于elman的神经网络的非线性状态空间表达式为:In this embodiment, the nonlinear state space expression of the neural network based on elman is:
其中,y(k)表示m维输出节点向量,l(k)表示m维隐含层节点单元向量,x(k)表示u维输入向量,c(k)表示n维反馈状态向量,w3表示隐含层到输出层的连接权值,w2表示输入层到隐含层的连接权值,w1表示承接层到隐含层的连接权值,g(*)表示输出神经元的传递函数,f(*)表示隐含层神经元的传递函数,f(*)一般采用S函数。Among them, y(k) represents the m-dimensional output node vector, l(k) represents the m-dimensional hidden layer node unit vector, x(k) represents the u-dimensional input vector, c(k) represents the n-dimensional feedback state vector, w 3 Represents the connection weight from the hidden layer to the output layer, w 2 represents the connection weight from the input layer to the hidden layer, w 1 represents the connection weight from the receiving layer to the hidden layer, g(*) represents the transmission of the output neuron Function, f(*) represents the transfer function of hidden layer neurons, and f(*) generally adopts S function.
具体地,步骤S5包括步骤S51~S52:Specifically, step S5 includes steps S51-S52:
S51、采用最大最小法,根据下式对修正后的居民负荷历史数据以及对应的历史天气参数数据进行归一化处理:S51. Using the maximum and minimum method, normalize the corrected historical resident load data and corresponding historical weather parameter data according to the following formula:
其中,xk表示居民负荷历史数据序列或历史天气参数数据数列中的第k个参数值,k为自然数,xmax表示xk所在数据序列中的最大值,xmin表示xk所在数据序列中的最小值;Among them, x k represents the kth parameter value in the historical data sequence of residents' load or the historical weather parameter data sequence, k is a natural number, x max represents the maximum value in the data sequence where x k is located, and x min represents the data sequence where x k is located the minimum value;
S52、根据归一化处理后的数据对建立的神经网络进行误差计算、权值更新和阀值更新,进而将基于elman的神经网络的预测误差控制在预设范围内。S52. Perform error calculation, weight update, and threshold update on the established neural network according to the normalized data, and then control the prediction error of the elman-based neural network within a preset range.
由于日期类型的特殊性,这里将休息日标记为1,工作日标记为0,同样符合最大最小归一化原则,即对日期类型进行归一化不会影响其具体值。Due to the particularity of the date type, the rest day is marked as 1 and the working day is marked as 0, which also conforms to the principle of maximum and minimum normalization, that is, normalizing the date type will not affect its specific value.
步骤S52中,基于elman的神经网络采用BP算法进行权值修正更新,并采用误差平方和函数进行指标函数学习,指标函数学习的公式为:In step S52, the elman-based neural network uses the BP algorithm to correct and update the weights, and uses the error sum of squares function to learn the indicator function. The formula for learning the indicator function is:
上式中E(x)表示指标函数,表示目标输入向量。In the above formula, E(x) represents the index function, represents the target input vector.
具体地,步骤S6包括步骤S61~S63:Specifically, step S6 includes steps S61-S63:
S61、获取预测日前一周的居民负荷历史数据、预测日的天气参数数据和日期类型作为神经网络的输入,采用训练后的神经网络对预测日的居民负荷进行预测,进而获得预测日当天每小时的预测数据,即获得预测的24个负荷点;S61. Obtain the historical data of residents' load one week before the forecast day, the weather parameter data and date type of the forecast day as the input of the neural network, use the trained neural network to predict the residents' load on the forecast day, and then obtain the hourly load of the forecast day. Forecast data, that is, obtain the predicted 24 load points;
S62、将获得的预测数据乘以季节指数后,获得每小时的居民负荷预测数据;S62. After multiplying the obtained forecast data by the seasonal index, obtain the hourly resident load forecast data;
S63、获取预测日当天的实际负荷数据后,计算获得的居民负荷预测数据与实际负荷数据之间的误差值,并将误差值反馈至神经网络。在获得预测日当天的实际负荷数据后,与预测数据中的24个负荷点进行对比,分别计算24个负荷点与实际负荷数据的误差值并反馈到神经网络,可以对建立的神经网络训练模型进行调整,使其更接近实际情况。S63. After obtaining the actual load data on the forecast day, calculate an error value between the obtained resident load forecast data and the actual load data, and feed back the error value to the neural network. After obtaining the actual load data on the forecast day, compare it with the 24 load points in the forecast data, calculate the error values between the 24 load points and the actual load data, and feed them back to the neural network, so that the neural network training model can be established Make adjustments to make it closer to reality.
本发明的居民负荷预测方法,通过建立起居民负荷的神经网络预测模型,能够以任意的精度逼近任意非线性映射,并不考虑外部噪声的影响,具有较高的精度,并具有适应时变特性和居民负荷的季节波动性的能力,能直接预测并反映居民负荷的动态特性,预测精度较高。而且本方法增加了季节指数特征,可以克服居民负荷的季节波动性较大和历史数据利用不完全等问题,可以有效提高预测数据的精度和预测稳定性。The resident load prediction method of the present invention can approach any nonlinear mapping with any precision by establishing a neural network prediction model of the resident load, without considering the influence of external noise, has high precision, and has the characteristics of adapting to time-varying It can directly predict and reflect the dynamic characteristics of residential load, and the prediction accuracy is high. Moreover, this method adds seasonal index features, which can overcome the problems of large seasonal fluctuations of residents' load and incomplete utilization of historical data, and can effectively improve the accuracy and stability of forecast data.
另外,根据居民用电需求、季节气候变化形成的居民用电历史数据,预测其近期用电量,将有利于居民家庭有意识地节约用电以及减缓电力资源匮乏的紧迫性。与此同时,通过将居民负荷预测与新能源的利用相结合,更能做到诸如太阳能,风能等各种形式的新能源的充分利用,避免能源的浪费,为用户创造更大的经济效益。In addition, based on the historical data of residents' electricity consumption and seasonal climate changes, predicting their recent electricity consumption will help residents consciously save electricity and alleviate the urgency of the lack of power resources. At the same time, by combining residents' load forecasting with the use of new energy, it is possible to make full use of various forms of new energy such as solar energy and wind energy, avoid energy waste, and create greater economic benefits for users.
以上是对本发明的较佳实施进行了具体说明,但本发明创造并不限于实施例,熟悉本领域的技术人员在不违背本发明精神的前提下还可做出种种的等同变形或替换,这些等同的变型或替换均包含在本申请权利要求所限定的范围内。The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments, and those skilled in the art can also make various equivalent deformations or replacements without violating the spirit of the present invention. Equivalent modifications or replacements are all included within the scope defined by the claims of the present application.
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