Electric load forecasting using an artificial neural network

An artificial neural network (ANN) approach is presented for electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the 1 h and 24 h-ahead forecasts in tests on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24 h ahead forecasts with a currently used forecasting technique applied to the same data     

Short-term load forecasting using an artificial neural network

An artificial neural network (ANN) method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern includes Saturday, Sunday, and Monday loads. A nonlinear load model is proposed and several structures of an ANN for short-term load forecasting were tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers was tested with various combinations of neurons, and results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives a good load forecast     

Daily electric load forecasting using artificial neural network

A multilayered-type neural network is attractive for daily electric load forecasting because the neural network can acquire a nonlinear relationship among the electric load data and their factors (weather, temperature, etc.) automatically. This paper discusses first some essential issues to be considered in neural network applications. One is difficulty of obtaining sufficient effective training data, another is the influence of abnormal learning data, and one more is the inevitable outerpolation. For these issues, the following three methods are developed in order to forecast more accurately: (1) a structure of the neural networks for insufficient training data; (2) detection and diminishing the influence of abnormal data; (3) employment of interpolation network and outerpolation network with additional data for outerpolation. Furthermore, to increase the sensitivity between electric loads and factors, (4) removal of base load is developed. Those methods work effectively to decrease the average absolute errors of peak-load forecasting and 24-hour load forecasting to 1.78 percent and 2.73 percent, respectively.     

基于快速人工神经网络的短期负荷预测

根据负荷的不确定性和非线性的特点 ,采用了ANN和AFS理论进行STLF ,分两个步骤 :在ANN中引入了平滑因子和遗忘因子 ,来加快收敛速度并解决ANN的遗忘问题 ;在AFS中对基本负荷预测值进行修正 ,引进不平均的隶属函数来体现负荷变化对温度的敏感性。实践表明该模型具有速度快、预测精度高等优点     

基于快速人工神经网络的短期负荷预测

根据负荷的不确定性和非线性的特点,采用了ANN和AFS理论进行STLF,分两个步骤：在ANN中引入了平滑因子和遗忘因子,来加快收敛速度并解决ANN的遗忘问题;在AFS中对基本负荷预测值进行修正,引进不平均的隶属函数来体现负荷变化对温度的敏感性。实践表明该模型具有速度快、预测精度高等优点。     

人工神经网络在电力系统负荷预测中的应用

论述了人工神经网络预测电力系统负荷的方法和步骤,并以BP神经网络在石嘴山地区短期负荷预测中的应用为例,探讨负荷预测的重要性。     

Weather sensitive short-term load forecasting using nonfullyconnected artificial neural network

The authors present an artificial neural network (ANN) model for forecasting weather-sensitive loads. The proposed model is capable of forecasting the hourly loads for an entire week. The model is not fully connected; hence, it has a shorter training time than the fully connected ANN. The proposed model can differentiate between the weekday loads and the weekend loads. The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model. This ANN model has been implemented on real load data. The average percentage peak error for the test cases was 1.12%     

Medium term system load forecasting with a dynamic artificial neural network model

This paper presents the development of a dynamic artificial neural network model (DAN2) for medium term electrical load forecasting (MTLF). Accurate MTLF provides utilities information to better plan power generation expansion (or purchase), schedule maintenance activities, perform system improvements, negotiate forward contracts and develop cost efficient fuel purchasing strategies. We present a yearly model that uses past monthly system loads to forecast future electrical demands. We also show that the inclusion of weather information improves load forecasting accuracy. Such models, however, require accurate weather forecasts, which are often difficult to obtain. Therefore, we have developed an alternative: seasonal models that provide excellent fit and forecasts without reliance upon weather variables. All models are validated using actual system load data from the Taiwan Power Company. Both the yearly and seasonal models produce mean absolute percent error (MAPE) values below 1%, demonstrating the effectiveness of DAN2 in forecasting medium term loads. Finally, we compare our results with those of multiple linear regressions (MLR), ARIMA and a traditional neural network model.     

One-hour-ahead load forecasting using neural network

Load forecasting has always been the essential part of an efficient power system planning and operation. Several electric power companies are now forecasting load power based on conventional methods. However, since the relationship between load power and factors influencing load power is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. Most of papers deal with 24-hour-ahead load forecasting or next day peak load forecasting. These methods forecast the demand power by using forecasted temperature as forecast information. But, when the temperature curves changes rapidly on the forecast day, load power changes greatly and forecast error would going to increase. In conventional methods neural networks uses all similar day's data to learn the trend of similarity. However, learning of all similar day's data is very complex, and it does not suit learning of neural network. Therefore, it is necessary to reduce the neural network structure and learning time. To overcome these problems, we propose a one-hour-ahead load forecasting method using the correction of similar day data. In the proposed prediction method, the forecasted load power is obtained by adding a correction to the selected similar day data     

Daily peak electric load forecasting using an artificial neural network and an improvement method for reducing the forecasting errors

This paper proposes a forecasting method for shortterm peak electric loads using a 3-layer neural network of locally active units. Each unit in the hidden layer of the neural network is activated only by input vectors in a bounded domain of vector space. This characteristic enables additional learning. Furthermore, it is supposed to provide the network structure with information that helps to improve forecasting accuracy. The neural network is applied to daily peak load forecasting simulations in summer. The results show that the proposed method is superior to a conventional neural network with the backpropagation algorithm. To make the best use of the neural network, an error-oriented method of parameter modification is also examined.     

Long-term load forecasting using improved recurrent neural network

In general, electric power companies must prepare power supply capability for maximum electric load demand because it is very difficult at present to store electric power. It takes several years and requires a great amount of money to construct power generation and transmission facilities. Therefore, it is necessary to forecast long-term load demand exactly in order to plan or operate power systems efficiently. Several methods have been investigated so far for the long-term load forecasting. However, because the electric loads consist of many complex factors, good forecasting has been very difficult. This paper proposes a long-term load forecasting method using a recurrent neural network (RNN). This is a mutually connected network that has the ability of learning patterns and past records. In general, when interpolation is used for unlearned data sets, the neural network provides reasonably good outputs. However, when extrapolation is used, such as in long-term load forecasting, some kind of tunings have been necessary to obtain good results. Therefore, to solve the problem, a method is proposed in which growth rates are used as input and output data. Using the proposed method, successful results have been obtained and comparisons have been made with the conventional methods.     

应用人工神经网络预测电力负荷

介绍了在批量处理时间序列情况下，BP神经网络辨识预测电力负荷的方法和步骤。网络成批训练，是权重矢量和偏导数矢量都同时与所有训练矢量的变化成正比地改变。由于采用附加动量项和自适应率等措施，克服了BP规则的局限性，加快了训练速度，增强了网络的泛化能力。在此基础上对某地区实际电力负荷进行了预测，取得了满意的结果。     

使用改进混沌神经网络的母线负荷预测

针对母线负荷非线性、冲击性波动、有较多“毛刺”、含有较多坏数据等特点,提出一种基于小波变换和混沌神经网络的母线负荷预测方法。该方法通过消除坏数据和噪声对负荷混沌特性分析的影响,能有效提高母线负荷预测的精度。首先对历史数据进行改进的小波阈值去噪,然后对其进行混沌特性分析,重构相空间形成训练样本．最后采用改进的混沌学习算法对网络进行训练,通过对某省某地220kV母线负荷算例分析,显示该方法能显著提高母线负荷预测的精度。     

Unit commitment using load forecasting based on artificial neural networks

This paper consists of two parts. While the first part shows the application of artificial neural networks to load forecasting using new input-output models, the second part utilizes the results from the first part in unit commitment. Based on the forecasts provided, unit commitment schedules are obtained for both hourly and daily load variations. Issues related to both problems are discussed along with an illustration of the two-step method using data obtained from a local utility. While a generation schedule such as this is not only invaluable to power system planners and operators, it is shown that this two-step process paves the way for an artificial intelligence (AI) type of method for the unit commitment problem based on the same inputs as the load forecasting method. For the chosen inputs, the simulations here show an average error of 4.3% and 3.1% in the case of the daily (twenty-four hours ahead) and hourly (one hour ahead) load forecast, respectively.     

基于人工鱼群算法神经网络的电力系统短期负荷预测

人工鱼群算法是一种新型的寻优策略,文中将人工鱼群算法用于RBF神经网络的训练过程,建立了相应的优化模型.依据人工鱼群算法的神经网络,提出一种短期负荷预测的新方法,实践表明:该方法具有预测精度高、误差小的优点,是值得广泛推广的好方法.     

Short term load forecasting of Taiwan power system using aknowledge-based expert system

A knowledge-based expert system was developed for the short-term load forecasting of the Taiwan power system. The developed expert system, which was implemented on a personal computer, was written in PROLOG using a 5-year database. To benefit from the expert knowledge and experience of the system operator, eleven different shapes, each with different means of load calculations, were established. With these load shapes at hand, some peculiar load characteristics pertaining to the Taiwan Power Company can be taken into account. The special load types considered by the expert system include the extremely low load levels during the week of the Chinese New Year, the special load characteristics of the days following a tropical storm or a typhoon, the partial shutdown of certain factories on Saturdays, the special event caused by a holiday on Friday or on Tuesday, etc. A characteristic feature of the knowledge-based expert system is that it is easy to add new information and new rules to the knowledge base. To illustrate the effectiveness of the system, short-term load forecasting is performed on the Taiwan power system by using both the developed algorithm and the conventional Box-Jenkins statistical method. It is found that a mean absolute error of 2.52% for one year is achieved by the expert system approach as compared to an error of 3.86% by the statistical method     

结合受限玻尔兹曼机的递归神经网络电力系统短期负荷预测

短期负荷预测的重要性随着电力企业的发展不断提高。传统的负荷预测虽然已经发展相对成熟,但现阶段对负荷预测的准确性要求逐渐提高。为满足发展需要,则要对现有的方法进行改进或建立新的预测方法。通过分析负荷预测数据周期性及周期内的特征,结合递归神经网络在分析时间序列数据的独特优势和受限玻尔兹曼机的强大的无监督学习能力,对结合受限玻尔兹曼机的递归神经网络的工作原理及训练过程进行了阐述。利用该网络进行了电力负荷数据预测实验验证并与其他神经网络进行了比较性实验。结果表明,所提出的神经网络较其他网络在电力短期负荷预测实验中有更高的准确性。     

一种基于RAN和专家系统的短期负荷预测方法

结合神经网络和专家库系统对地区电网进行短期负荷预测,利用神经网络的非线性函数逼近能力进行基本负荷预测,在此基础上结合天气专家库系统再进行负荷调整。此方法已用于地区电网负荷预测,预测结果表明此方法是实用有效的,精度满足实用要求。     

基于神经网络的电力系统短期负荷预测

介绍了短期负荷的特点,深入分析了温度、降雨量、时间等因素对负荷的影响。应用BP神经网络,建立了充分考虑各种因素的日最高负荷预测和日平均负荷预测模型,最后通过贵阳城南分局历史负荷进行验证,表明本论文所建立的神经网络预测模型充分考虑了各种负荷因素影响,预测精度良好,具有较好的非线性映射能力,有进一步开发应用于实际预测的良好前景。     

基于小波变换的混合神经网络短期负荷预测

准确的短期负荷预测是作出正确营销决策的依据。采用小波变换对负荷序列进行分解,对于每一分解序列,分别按照各自的特点选择出比较合适的影响因素,采用信息熵理论和主成份分析相结合的属性约简法对其进行约简,并利用动态聚类对各分解序列的样本归类,通过灰色关联分析找到与预测时刻负荷模式最接近的一些典型样本,训练各分解序列相应的神经网络预测模型,最后通过序列重构,得到完整的负荷预测结果。采用实际负荷数据进行测试,表明这一方法预测效果较好。