Add to ORKGIt is a fact that the short-term load forecasting (STLF)in the user side is growing interest. Consequently, intelligent energy management systems (iEMSs) are including this capability in order to take autonomous decisions. In this context, this paper presents a new STLF scheme based on Adaptative Networks Fuzzy Inference Systems (ANFIS). This ANFIS has an exponential output membership functions (e-ANFIS) and has been trained by means of a novel evolutionary training algorithm (ETA). Due to the computational burden required by ETA, parallel computing was used to eliminate this problem especially for embedded applications. This new scheme has been tested with real data from an automotive factory and it shows better results in comparison ...
AbstractThis paper presents an adaptive PID Load Frequency Control (LFC) for power systems using Neu...
Essential decision-making tasks such as power management in future vehicles will benefit from the de...
In this paper Q self-orgnriizirig firzzy-neiircrI network with a new learning mechanism and rule opt...
It is a fact that the short-term load forecasting (STLF)in the user side is growing interest. Conseq...
In this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference sy...
The automotive industry is facing a transformation towards the massive digitalization and data-acqui...
This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (A...
This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (A...
* IEEE Member Abstract: The huge consumption of electric energy in these days has given the load for...
Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural n...
Microgrids (MGs) development is one of the most pursued solution for the electric grid modernization...
This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the ...
This paper presents an adaptive PID Load Frequency Control (LFC) for power systems using Neuro-Fuzzy...
[[abstract]]The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy i...
An Adaptive Neuro Fuzzy Inference System (ANFIS) based Extreme Learning Machine (ELM) theory is util...
AbstractThis paper presents an adaptive PID Load Frequency Control (LFC) for power systems using Neu...
Essential decision-making tasks such as power management in future vehicles will benefit from the de...
In this paper Q self-orgnriizirig firzzy-neiircrI network with a new learning mechanism and rule opt...
It is a fact that the short-term load forecasting (STLF)in the user side is growing interest. Conseq...
In this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference sy...
The automotive industry is facing a transformation towards the massive digitalization and data-acqui...
This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (A...
This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (A...
* IEEE Member Abstract: The huge consumption of electric energy in these days has given the load for...
Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural n...
Microgrids (MGs) development is one of the most pursued solution for the electric grid modernization...
This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the ...
This paper presents an adaptive PID Load Frequency Control (LFC) for power systems using Neuro-Fuzzy...
[[abstract]]The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy i...
An Adaptive Neuro Fuzzy Inference System (ANFIS) based Extreme Learning Machine (ELM) theory is util...
AbstractThis paper presents an adaptive PID Load Frequency Control (LFC) for power systems using Neu...
Essential decision-making tasks such as power management in future vehicles will benefit from the de...
In this paper Q self-orgnriizirig firzzy-neiircrI network with a new learning mechanism and rule opt...