0950-7051/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.knosys.2012.08.015 ⇑ Corresponding author. Tel.: +86 15811424568; fa E-mail address: [email protected] (S. Guo). Accurate annual power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power indus...

Knowing that electrical load is a non storable resource; short term electric load forecasting becomes an important tool to optimise dispatching of electrical load in regular system planning. Several techniques have been used to accomplish this task, from traditional linear regression and BoxJenkins to artificial intelligence approaches such as Artificial Neural Networks (ANN). This work present...

In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on differe...

This paper presents the development and application of advanced neural networks to face successfully the problem of the shortterm electric load forecasting. Several approaches including Gaussian encoding backpropagation (BP), window random activation, radial basis function networks, real-time recurrent neural networks and their innovative variations are proposed, compared and discussed in this ...

− This paper describes a neural network system for power electric load forecasting of telecommunication station. Getting an accuracy useful for contractual purpose a separately daily forecast of both main load and its oscillation is proposed. For the mean daily forecast we used a three layers multilayer perceptron (MLP), while to the oscillation forecasting we realized a system composed by a ML...

A daily peak load forecasting technique that uses artificial neural network presented in this paper. A neural network of used to predict the daily peak load for a period available using one step ahead prediction load to the actual load. The ith index is used as load for the ith day of the year following networks are trained by the back propagation algorithm. from the Nigerian national electric ...

This paper presents a new hybrid method for the short-term load forecasting in electric power systems based on particle swarm optimization (PSO) and relevance vector machine (RVM). In this method, we firstly develop a type of kernel as the kernel function of the RVM model, and then its parameter is optimized by the PSO, finally the established RVM forecast mode is applied to short-term load for...

The short-term load forecasting (STLF), with lead times ranging from a few hours to several days ahead, helps grid operators to make a cost effective scheduling of resources, purchase of energy, maintenance and security analysis studies. The use of reliable load forecasting models is necessary for a rational use of electricity, taking into account that it is not storable. Climatic conditions ce...

The modeling of the relationships between the power loads and the variables that influence the power loads especially in the abnormal days is the key point to improve the performance of short-term load forecasting systems. To integrate the advantages of several forecasting models for improving the forecasting accuracy, based on data mining and artificial neural network techniques, an ensemble d...