The aim of this paper is to present a novel approach to energy use forecasting. We propose
a nested fuzzy cognitive map in which each concept at a higher level can be decomposed …

Energy consumption forecasting is crucial in current and future power and energy systems.
With the increasing penetration of renewable energy sources, with high associated …

This paper introduced a new ensemble learning approach, based on evolutionary fuzzy
cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM …

The purposes of this research are to find a model to forecast the electricity consumption in a
household based on fuzzy cognitive map (FCM) prediction capabilities. The data analysis …

This paper proposes a hybrid approach based on local linear neuro fuzzy (LLNF) model and
fuzzy transform (F-transform), termed FT-LLNF, for prediction of energy consumption. LLNF …

This paper proposes an adaptive fuzzy expert system to concurrently estimate and forecast
both long-term electricity and natural gas (NG) consumptions with hike in prices. Using a …

Representing and analyzing the complexity of models constructed by data is a difficult and
challenging task, hence the need for new, more effective techniques emerges, despite the …

The accuracy of the prediction of buildings' energy consumption is being tackled using
existing artificial intelligence techniques. However, there is a lack of effort on the …

In view of the defects of the prediction model based on neural network, such as when doing
prediction of nonlinear sequence, it is likely to fall into local hypo-strong point, and the rate of …

Reliable consumption forecasts are crucial in several aspects of power and energy systems,
eg to take advantage of the full potential of flexibility from consumers and to support the …