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Short-term load in power system is nonlinear and non-stationary. To cope with the problem that the training error of neural network prediction model is increased while the generalization ability is reduced caused by the large input fluctuation, the rough neurons with upper and lower inputs are introduced into the radial basis function (RBF) neural networks, a power system short-term load forecasting...
Short-term load forecasting is the basis of power system regulation, and it affects many decisions of power system. In order to deal with the challenge of decline in prediction accuracy caused by reduction of cost, and improve the forecasting accuracy and speed, an improved extreme learning machine algorithm, which combines prior knowledge of residential electricity consumption habits is proposed...
Accurate and robust load forecasting models play an important role in power system planning. Due to smaller size and inherent property of good classification, Radial Basis Function Neural Network (RBFNN) is always preferred over other neural network structures. It is used by researchers as an effective tool for Short-Term Load Forecasting (STLF). The smaller size of this network may lead its output...
This paper presents an hourly load forecast of small load area using Artificial Neural Network (ANN). For this case-study duration of February-2010 to Januray-2011 is considered. In this study ANN is trained and tested for by providing two different input vectors. In this paper the input vector design and the data is mainly focused. Also, suitable ANN topology is also discussed. Further the training...
Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first...
The correct schedule, planning and operation of power system has a tight correlation between accurate load forecast. Aims at the variant feature of power system short-term load, the author took a widely study and discussion on the method of artificial neural network applied on power system short-term load forecasting. At the base of three layered BP neural network, the author studied the meteorological...
In this paper, we introduce a load forecasting method for short-term load forecasting which is based on a two-stage hybrid network with weighted self-organizing maps (SOM) and autoregressive (AR) model. In the first stage, a weighted SOM network is applied to split the past dynamics into several clusters in an unsupervised manner. Then in the second stage, a local linear AR model is associated with...
The objective of this research is to analyze the capacity of the Multilayer Perceptron Neural Network (MLP) versus Self-Organizing Map Neural Network (SOM) for Short-Term Load Forecasting. The MLP is one of the most commonly used networks. It can be used for classification problems, model construction, series forecasting and discrete control. For the forecasting problems, a backpropagation (BP) algorithm...
Short-term forecasting is required by utility planners and electric system operators for tactical operational planning and day-to-day decision making. The forecasting is intended to obtain the system load demand over a period of hours or days, and it plays an important role in determining unit commitment, spinning reserve, economic power interchange, load management etc… Electrical load has a time-varying...
An accurate and efficient Short-Term Load Forecasting (STLF) plays a vital role for economic operational planning of both regulated power systems and electricity markets. Therefore, many techniques and approaches for STLF problem have been presented in the literature. However, there is still an essential need to develop more accurate load forecast method. This paper presents the application of artificial...
In the analysis of predicting power load forecasting based on least squares neural network, the instability of the time series could lead to decrease of prediction accuracy. On the other hand,neural network and chaos theories parameters must be carefully predetermined in establishing an efficient model. In order to solve the problems mentioned above, in this paper, the neural network and chaos theory...
One of the basic requirements for power systems is accurate short-term load forecasting (STLF). In this study, the application of artificial neural networks is explored for designing of short-term load forecasting systems for electricity market of Iran. In this paper, two seasonal artificial neural networks (ANNs) are designed and compared; so that model 2 (hourly load forecasting model) is partitioning...
In this study, unit commitment (UC) problem is solved for an optimum schedule of generating units based on the load data forecasted by using artificial neural network (ANN) model and ANN model with autoregressive (AR). Low-cost generation is important in power system analysis. Under forecasting or over forecasting will result in the requirement of purchasing power from spot market or an unnecessary...
An optimized neural network modeling approach with genetic algorithm for short-term load forecasting based on only multiple delayed historical power load data is proposed. Genetic algorithm is used to globally optimize the number of delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg-Marquardt algorithm with Bayesian regularization...
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