GMDH和变结构协整理论在电力负荷预测中的应用

鉴于未考虑时间序列的非平稳性所得到的拟合方程可能出现"伪回归"而失去准确预测的能力,引入了协整理论与误差修正模型.考虑地区产业结构发生变化将导致地区用电量时间序列发生结构突变,影响平稳性检验结果的可信度,又引入了GMDH理论,自动搜索并确定结构突变点,以改进经典结构突变理论需要事先获知一些重大波动信息等主观性影响.结合上述两点,构建了基于GMDH理论的参数变结构协整模型.算例证明了该模型在电力系统的负荷预测中的适用性以及在处理"伪回归"和"样本数据结构突变"问题方面的有效性.     

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     

Short-term load forecasting using neural networks

An artificial neural network (ANN) model for short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting the next 24-hour load profile at one time, as opposed to the usual ‘next one hour’ ANN models. The inputs to the ANN are load profiles of the two previous days and daily maximum and minimum temperature forecasts. The network is trained to learn the next day's load profile. Testing of the model with one year of data from the Greek interconnected power system resulted in a 2.66% average absolute forecast error.     

Short-term hourly load forecasting using abductive networks

Short-term load modeling and forecasting are essential for operating power utilities profitably and securely. Modern machine learning approaches, such as neural networks, have been used for this purpose. This paper proposes using the alternative technique of abductive networks, which offers the advantages of simplified and more automated model synthesis and analytical input-output models that automatically select influential inputs, provide better insight and explanations, and allow comparison with statistical and empirical models. Using hourly temperature and load data for five years, 24 dedicated models for forecasting next-day hourly loads have been developed. Evaluated on data for the sixth year, the models give an overall mean absolute percentage error (MAPE) of 2.67%. Next-hour models utilizing available load data up to the forecasting hour give a MAPE of 1.14%, outperforming neural network models for the same utility data. Two methods of accounting for the load growth trend achieve comparable performance. Effects of varying model complexity are investigated and proposals made for further improving forecasting performance.     

基于自组织映射网络的峰值负荷预测方法

应用扩展自组织映射网络研究了电力系统峰值负荷预测问题。在传统的Kohonen自组织映射(SOM)网络的学习算法的基础上,为了提高电力系统峰值负荷预测的精度,进一步提出了一种扩展的自组织映射算法。在这个SOM网络中,除了权矩阵外,还有一个输入输出对的局部梯度(Jocobian)矩阵也被存储在神经元中。这样,在输出空间中梯度信息围绕输出权值产生了一个一阶扩展,便可得到一个输出的改进估计值。同时,提出了一个Jocobian矩阵的生成算法。最后采用纽约市的电力负荷数据为研究对象,证明了所提出方法的有效性。     

Short-term hourly load forecasting using time-series modeling withpeak load estimation capability

This paper presents a new time series modeling for short term load forecasting, which can model the valuable experiences of the expert operators. This approach can accurately forecast the hourly loads of weekdays, as well as, of weekends and public holidays. It is shown that the proposed method can provide more accurate results than the conventional techniques, such as artificial neural networks or Box-Jenkins models. In addition to hourly loads, daily peak load is an important problem for dispatching centers of a power network. Most of the common load forecasting approaches do not consider this problem. It is shown that the proposed method can exactly forecast the daily peak load of a power system. Obtained results from extensive testing on the Iran's power system network confirm the validity of the developed approach     

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。     

Regression based peak load forecasting using a transformationtechnique

This paper presents a regression based daily peak load forecasting method with a transformation technique. In order to forecast the load precisely through a year, one should consider seasonal load change, annual load growth and the latest daily load change. To deal with these characteristics in the load forecasting, a transformation technique is presented. This technique consists of a transformation function with translation and reflection methods. The transformation function is estimated with the previous year's data points, in order that the function converts the data points into a set of new data points with preservation of the shape of temperature-load relationships in the previous year. Then, the function is slightly translated so that the transformed data points will fit the shape of temperature-load relationships in the year. Finally, multivariate regression analysis, with the latest daily loads and weather observations, estimates the forecasting model. Large forecasting errors caused by the weather-load nonlinear characteristic in the transitional seasons such as spring and fall are reduced. Performance of the technique which is verified with simulations on actual load data of Tokyo Electric Power Company is also described     

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.     

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     

Short-term forecasting of grid load using Box-Jenkins techniques

The paper demonstrates the use of Box-Jenkins time series analysis in short-term load forecasting, and a forecasting system developed at the Imatra Power Company is described. The forecasting algorithm is simple, fast and accurate, which makes it suitable for online forecasting. The transfer function model is used to introduce temperature effects, thus improving accuracy further. The method gives good results in other forecasting problems of electrical energy systems.     

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.     

Neural network based short-term load forecasting using weathercompensation

This paper presents a novel technique for electric load forecasting based on neural weather compensation. Our proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. Our weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error     

A real-time short-term load forecasting system using functionallink network

This paper presents a new functional-link network based short-term electric load forecasting system for real-time implementation. The load and weather parameters are modelled as a nonlinear ARMA process and parameters of this model are obtained using the functional approximation capabilities of an auto-enhanced functional link net. The adaptive mechanism with a nonlinear learning rule is used to train the link network on-line. The results indicate that the functional link net based load forecasting system produces robust and more accurate load forecasts in comparison to simple adaptive neural network or statistical based approaches. Testing the algorithm with load and weather data for a period of two years reveals satisfactory performance with mean absolute percentage error (MAPE) mostly less than 2% for a 24-hour ahead forecast and less than 2.5% for a 168-hour ahead forecast     

基于模糊推理的节假日负荷预测

由于节假日负荷成分与正常日有较大差异,加之样本较少,节假日短期负荷预测难度较大.而准确预测可以提高系统运行的可靠性和经济性.为了提高节假日负荷预测的精度,针对节假日负荷特点,利用相似日方法获得待预测日负荷归一化曲线,利用模糊推理方法对负荷水平年增长率进行调整.通过对实际负荷进行预报计算,结果表明预报精度较高,建议用于节假日短期负荷预测.     

Next day load curve forecasting using hybrid correction method

This work presents an approach for short-term load forecast problem, based on hybrid correction method. Conventional artificial neural network based short-term load forecasting techniques have limitations especially when weather changes are seasonal. Hence, we propose a load correction method by using a fuzzy logic approach in which a fuzzy logic, based on similar days, corrects the neural network output to obtain the next day forecasted load. An Euclidean norm with weighted factors is used for the selection of similar days. The load correction method for the generation of new similar days is also proposed. The neural network has an advantage of dealing with the nonlinear parts of the forecasted load curves, whereas, the fuzzy rules are constructed based on the expert knowledge. Therefore, by combining these two methods, the test results show that the proposed forecasting method could provide a considerable improvement of the forecasting accuracy especially as it shows how to reduce neural network forecast error over the test period by 23% through the application of a fuzzy logic correction. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan.     

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%     

采用分时段模糊技术处理实现的短期负荷预报

采用模糊理论对日负荷曲线预报中的天气因素进行了模糊处理,根据季节、气候的不同对天气因素做不同的模糊处理和不同数学模型,建立了专家处理系统,提高了短期负荷预测的精度.通过对河南省某市级电力系统日负荷曲线的模拟预测,虽然仅对天气做了模糊处理,但预测结果令人满意.