This paper presents a hybrid approach to predict the electric energy usage of weather‐
sensitive loads. The presented method utilizes the clustering paradigm along with ANN and …

Short-term electric load forecasting plays a significant role in the safe and stable operation of
the power system and power market transactions. In recent years, with the development of …

Integration of industrial consumers into the smart grid concept can be facilitated by
optimizing load forecasting for industrial consumers. Minimizing forecast errors can improve …

Integration of industrial consumers into the smart grid concept can be facilitated by
optimizing short and very short-term forecasts of load curves for industrial consumers …

This paper describes a human-machine interface for daily load forecasting using a
Recursive Artificial Neural Network (RANN). The RANN architecture and the learning …

Large scale integration of renewable energy sources is challenging the stability of the power
systems. The variability of these sources (especially of wind farms) is the main aspect which …

Aceasta lucrare se concentreaza asupra prognozei pe termen scurt a consumului de
energie electrica a consumatorilor rezidentiali utilizand retele neuronale artificiale. Lucrarea …

The research aim is to establish the optimum energy efficiency conversion line using
thermalchemical processes with application for a decentralized integrated scenario models …

Electrical energy distributor companies in Iran have to announce their energy demand at
least three 3-day ahead of the market opening. Therefore, an accurate load estimation is …

Wind capacity is undergoing the fastest rate of growth of any form of electricity generation.
The variability of these sources is the main aspect which affects the system security and …