In this study, we aim to provide an efficient load prediction system projected for different local feeders predict the Medium- and Long-term Load Forecasting. This model improves future requirements expansions, equipment retailing or staff recruiting electric utility company. We aimed improve ahead forecasting by using hybrid approach optimizing parameters of our models. used Long Short-Term Me...

This paper presents results obtained by numerical study of sloshing effects in medium sized rectangular tanks subjected to a long-period seismic excitation. The Smoothed Particle Hydrodynamics (SPH) method is used, and the wave displacement and pressure are analysed. The intensity of sloshing effects depends not only on the peak ground acceleration but also on the dominant frequency of ground m...

The importance of forecasting has become more evident with the appearance of the open electricity market and the restructuring of the national energy sector. This paper presents a new approach to load forecasting in the medium voltage distribution network in Portugal. The forecast horizon is short term, from 24 hours up to a week. The forecast method is based on the combined use of a regression...

the ability of artificial neural network (ann) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real  concern. as the most applicable network, the ann with multi-layer back propagation perceptrons is used to approximate functions. throughout the current work, the daily effective temperature is determined, and then the weather data w...

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ∼ logN . In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fig...

Planing the operation in modern power systems requires suitable anticipation of load evolution at different levels of distribution network. Under this perspective, load forecasting performs an important task, allowing the optimization of investments and the adequate exploitation of existing distribution networks. This paper describes the models developed for current intensity forecasting at pri...

The ability of Artificial Neural Network (ANN) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real  concern. As the most applicable network, the ANN with multi-layer back propagation perceptrons is used to approximate functions. Throughout the current work, the daily effective temperature is determined, and then the weather data w...

This paper develops medium term electric load forecasting using neural networks, based on historical series of electric load, economic and demographic variables. The neural network chosen for this work is the Time Lagged Feedforward Network (TLFN), which combines conventional network topology (multilayer perceptron) with good handling of time dependencies by means of gamma memory. This is a ver...

Electricalcircuit Yes Yes Medium (12% error predicting cell voltage and thermal characteristics , 5% error predicting cycle aging) medium Medium (>15 parameters) Medium Electricalcircuit Yes No Medium Medium High (>30 parameters) Medium Thermostati c charge method: high Discretetime Yes No Medium (1% compared to Hspice continuous-ti me model) Medium Medium (>15 parameters) Medium Dynamic Power ...