Digital forecasting control of objects with a transportation lag

Questions of how to use the method of forecasting control in digital systems for controlling objects with a transportation lag are considered. The methodology of synthesizing forecasting regulators is described. A comparison of the regulation quality of a variant under study with existing systems for compensating a lag is made.     

Short-term load forecasting with local ANN predictors

A new technique for artificial neural network (ANN) based short-term load forecasting (STLF) is presented in this paper. The technique implemented active selection of training data, employing the k-nearest neighbors concept. A novel concept of pilot simulation was used to determine the number of hidden units for the ANNs. The ensemble of local ANN predictors was used to produce the final forecast, whereby the iterative forecasting procedure used a simple average of ensemble ANNs. Results obtained using data from two US utilities showed forecasting accuracy comparable to those using similar techniques. Excellent forecasts for one-hour-ahead and five-days-ahead forecasting, robust behavior for sudden and large weather changes, low maximum errors and accurate peak-load predictions are some of the findings discussed in the paper     

Demand forecasting using fuzzy neural computation, with specialemphasis on weekend and public holiday forecasting

This paper describes the implementation and forecasting results of a hybrid fuzzy neural technique, which combines neural network modeling, and techniques from fuzzy logic and fuzzy set theory for electric load forecasting. The strengths of this powerful technique lie in its ability to forecast accurately on weekdays, as well as, on weekends, public holidays, and days before and after public holidays. Furthermore, use of fuzzy logic effectively handles the load variations due to special events. The fuzzy-neural network (FNN) has been extensively tested on actual data obtained from a power system for 24-hour ahead prediction based on forecast weather information. Very impressive results, with an average error of 0.62% on weekdays, 0.83% on Saturdays and 1.17% on Sundays and public holidays have been obtained. This approach avoids complex mathematical calculations and training on many years of data, and is simple to implement on a personal computer     

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.     

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     

小波变换与BP神经网络耦合的洪水预报方法

为了发掘小波变换与BP神经网络相耦合在洪水预报中的应用价值,提出了基于小波变换和BP神经网络耦合的洪水预报方法.根据小波变换与BP神经网络耦合方式的不同,分别构建了两种结构的网络模型:松散耦合与紧致耦合模型,并利用实测流量时间序列分别对这两种模型进行了训练与验证.计算结果表明,两种模型的预报效果与3层常规BP神经网络相比均得到了不同程度的提高,松散耦合模型的预报效果总体上最优.小波变换与BP神经网络相耦合在洪水预报方面有良好的应用前景.     

蚁群灰色神经网络组合模型在电力负荷预测中的应用

灰色GM(1,1)预测模型,在负荷预测中得到了广泛应用,但是也有其局限性.当数据灰度越大,预测精度越差,并且不太适合经济长期后推若干年的预测,在一定程度上是由模型中的参数造成的,为此引入向量,建立蚁群灰色模型,然后与神经网络模型相组合,即建立蚁群灰色神经网络组合预测模型.实证分析表明,该预测方法是合理有效的,与传统的预测方法相比,提高了预测精度,具有较好的实用价值.     

Insolation forecasting by a multi-stage neural network

A single-stage neural network has been proposed to forecast next day insolation. In this paper, a multi-stage neural network is developed to reduce forecasting error further. A first-stage neural network forecasts average atmospheric pressure for the next day from atmospheric pressure data of the previous day. A second-stage neural network forecasts insolation level for the next day from the average atmospheric pressure and weather data of the previous day. A third-stage neural network forecasts next day insolation from the insolation level and weather data of the previous day. Meteorological data of Omaezaki, Shizuoka at April 1994 were chosen as input data. The insolation values forecasted by the multi-stage and the single-stage neural networks are compared with the measurement values. The results show that the forecasting error is reduced to 24% (by the multi-stage) from 33% (by the single-stage). © 1998 Scripta Technica, Electr Eng Jpn, 125(4): 26–33, 1998     

蚁群灰色神经网络组合模型在电力负荷预测中的应用

灰色GM(1,1)预测模型,在负荷预测中得到了广泛应用,但是也有其局限性。当数据灰度越大,预测精度越差,并且不太适合经济长期后推若干年的预测,在一定程度上是由模型中的参数 造成的,为此引入向量 ,建立蚁群灰色模型,然后与神经网络模型相组合,即建立蚁群灰色神经网络组合预测模型。实证分析表明,该预测方法是合理有效的,与传统的预测方法相比,提高了预测精度,具有较好的实用价值。     

基于主成分分析L-M神经网络高峰负荷预测研究

在论述电网高峰负荷预测重要性和特点的基础上,将高峰负荷样本,按节气工作日和节假日样本进行聚类,从输入空间入手,采用主成分分析法,减少输入向量的维数,并保留原来输入向量的有用信息,再利用L-M优化算法的多层神经网络预测模型对辽宁电网高峰负荷进行了模拟预测,预测精度令人满意.     

Long-term load forecasting via a hierarchical neural model with time integrators

A novel hierarchical hybrid neural model to the problem of long-term load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets – one on top of the other –, and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load and mean-load during the next two years. The results are presented and evaluated in the paper.     

基于主成分分析L-M神经网络高峰负荷预测研究

在论述电网高峰负荷预测重要性和特点的基础上,将高峰负荷样本,按节气工作日和节假日样本进行聚类,从输入空间入手,采用主成分分析法,减少输入向量的维数,并保留原来输入向量的有用信息,再利用L-M优化算法的多层神经网络预测模型对辽宁电网高峰负荷进行了模拟预测,预测精度令人满意。     

电力系统短期负荷预测的模糊神经网络方法

针对电力系统短期负荷预测问题,考虑气象因素对负荷的影响,提出了一种模糊神经网络的短期负荷预测方法,首先根据评价函数选取相似日学习样本,然后利用隶属函数对影响负荷的特征因素向量的分量进行模糊处理,采用反向传播算法,对24点每点建立一个预测模型,提高了学习效能.本方法适合在短期负荷预测中使用,具有较好的预测精度.     

Recurrent neural networks for short-term load forecasting

Forecasting the short-term load entails the construction of a model, and, using the information available, estimating the parameters of the model to optimize the prediction performance. It follows that the more closely the chosen model approximates the actual physical generating process, the higher the expected performance of the forecasting system. In this paper it is postulated that the load can be modeled as the output of some dynamic system, influenced by a number of weather, time and other environmental variables. Recurrent neural networks, being members of a class of connectionist models exhibiting inherent dynamic behavior, can thus be used to construct empirical models for this dynamic system. Because of the nonlinear dynamic nature of these models, the behavior of the load prediction system can be captured in a compact and robust representation. This is illustrated by the performance of recurrent models on the short-term forecasting of the nation-wide load for the South African utility, ESKOM. A comparison with feedforward neural networks is also given     

Electricity price forecasting using artificial neural networks

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to “fit” and “extrapolate” the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price.     

电力系统短期负荷预测的模糊神经网络方法

针对电力系统短期负荷预测问题 ,考虑气象因素对负荷的影响 ,提出了一种模糊神经网络的短期负荷预测方法 ,首先根据评价函数选取相似日学习样本 ,然后利用隶属函数对影响负荷的特征因素向量的分量进行模糊处理 ,采用反向传播算法 ,对 2 4点每点建立一个预测模型 ,提高了学习效能。本方法适合在短期负荷预测中使用 ,具有较好的预测精度。     

高精度数字式激光器恒温控制系统

全固态单频单模激光器的频率稳定性以及最佳匹配输出功率对温度非常敏感.本文介绍以AT89S52单片机为核心的激光器恒温控制系统的硬件与软件的设计与实现,以及热敏电阻的线性插值化处理,温度设定精度最小可达0.01℃.实验结果表明:该激光器恒温控制系统的温度控制稳定度可达5m℃,并且温度设定非常灵活.     

Multi-substation control central load area forecasting by using HP-filter and double neural networks (HP-DNNs)

Electricity load demand forecasting of Thailand using Hodrick–Prescott (HP) filters and double-neural networks (DNNs) is presented in this article by dividing whole country area into multi-substation areas. The signals of load demand in each subarea will be decomposed to trend and cycling signals by HP-filter before sent to DNNs for load demand forecast. The trend signals show close relationship with economic affecting features, while the cycling signals demonstrate strong relationship with weather features. These obvious correlations will be used for feature input selections. In the finally stage, the forecasting results from each subarea will be composed for the whole country area result. Comparing to other forecasting models, this approach not only reduce complexity of the forecasting model but also decrease mean absolute percent error (MAPE) as 1.42%. Moreover, this method can be applied to other load forecasting in power system and any application that can be separated into subarea.     

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。     

Short-term wind power forecasting using ridgelet neural network

Rapid growth of wind power generation in many countries around the world in recent years has highlighted the importance of wind power prediction. However, wind power is a complex signal for modeling and forecasting. Despite the performed research works in the area, more efficient wind power forecast methods are still demanded. In this paper, a new prediction strategy is proposed for this purpose. The forecast engine of the proposed strategy is a ridgelet neural network (RNN) owning ridge functions as the activation functions of its hidden nodes. Moreover, a new differential evolution algorithm with novel crossover operator and selection mechanism is presented to train the RNN. The efficiency of the proposed prediction strategy is shown for forecasting of both wind power output of wind farms and aggregated wind generation of power systems.