This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction a...

The increase of electric power demand and cost of generation, make forecasting very economical to the supply authority and useful to reduce uncertainty to the consumer. Out of various forecasting models, Box-Jenkins time-series models are useful but costly to operate. Modified BJ model, having lot of advantages, were developed for long range forecast of electrical load and a frequency domain ap...

Short-term electric load forecasting (STLF) plays the main role in making operational decisions in any electrical power system. The implementation of forecasting algorithms collides with the high computational power needed to perform the complex perdition processes on large datasets. In this paper, a cloudbased STLF algorithm is implemented. The performance analysis of the proposed system was c...

The article investigates models and methods of electric load forecasting. It is shown that the following power consumption control are currently known: instantaneous norm; at ideal rate; management on forecast value; with use average a moving time interval ("moving window" method). it better to focus those based study estimates, which source information for decisions. main requirements real-tim...

Electrical load forecasting is an essential tool used to ensure that the energy supplied by utilities meets the load and the energy lost in the system. To this end, a staff of trained personnel is needed to carry out this specialized function. Electric load forecasting is always defined as the science or art of predicting the future load of a given system, for a specified period ahead. These pr...

In order to provide more efficient and reliable power services than the traditional grid, it is necessary for smart grid accurately predict electric load. Recently, recurrent neural networks (RNNs) have attracted increasing attention in this task because can discover temporal correlation between current load data those long-ago through self-connection of hidden layer. Unfortunately, RNN prone v...

For the proper planning and operation of any electric power system, it is essential to have a reliable software tool that allows for accurate short-term forecasting consumption. Temperature one most important drivers energy consumption selecting appropriate weather station or combination stations crucial improving accuracy. In this study, we propose an algorithm determine optimal selection base...