Enhanced short-term load forecasting using artificial neural networks
AI Arvanitidis, D Bargiotas, A Daskalopulu, VM Laitsos… - Energies, 2021 - mdpi.com
The modernization and optimization of current power systems are the objectives of research
and development in the energy sector, which is motivated by the ever-increasing electricity …
and development in the energy sector, which is motivated by the ever-increasing electricity …
A comparison of neural network backpropagation algorithms for electricity load forecasting
X Pan, B Lee, C Zhang - 2013 IEEE International Workshop on …, 2013 - ieeexplore.ieee.org
Load forecasting plays a significant role in planning and operation of electrical power
networks. Artificial neural networks have been extensively employed for load forecasting …
networks. Artificial neural networks have been extensively employed for load forecasting …
Short term load forecasting in electric power systems with artificial neural networks
The demand in electric power should be predicted with the highest possible accuracy as it
affects decisively many of power system's operations. Conventional methods for load …
affects decisively many of power system's operations. Conventional methods for load …
Short-Term Load Foresting Using Combination of Linear and Non-linear Models
N Rani, SK Aggarwal, S Kumar - IEEE Access, 2024 - ieeexplore.ieee.org
Numerous short-term load forecasting models are available in the literature. However, the
improvement in forecast accuracy using the combination models has yet to be analyzed on a …
improvement in forecast accuracy using the combination models has yet to be analyzed on a …
Artificial neural network methodology for the estimation of ground enhancing compounds resistance
VP Androvitsaneas, IF Gonos… - IET Science …, 2014 - Wiley Online Library
The work presented in this study aims to develop a methodological approach for estimating
the ground resistance of several grounding systems, embedded in various ground …
the ground resistance of several grounding systems, embedded in various ground …
[PDF][PDF] Enhanced Short-Term Load Forecasting Using Artificial Neural Networks. Energies 2021, 14, 7788
AI Arvanitidis, D Bargiotas, A Daskalopulu, VM Laitsos… - 2021 - academia.edu
The modernization and optimization of current power systems are the objectives of research
and development in the energy sector, which is motivated by the ever-increasing electricity …
and development in the energy sector, which is motivated by the ever-increasing electricity …
[PDF][PDF] Short term load forecasting in Greek intercontinental power system using ANNs: a study for input variables
The scopus of this paper is to compare the performance of different structures of Artificial
Neural Networks (ANNs) regarding the input variables used for short-term forecasting of the …
Neural Networks (ANNs) regarding the input variables used for short-term forecasting of the …
[PDF][PDF] Short term load forecasting in Greek interconnected power system using ANN: A study for output variables
The purpose of this paper is to compare the performance of different structures of Artificial
Neural Networks (ANNs) regarding the output variables used for short term forecasting of the …
Neural Networks (ANNs) regarding the output variables used for short term forecasting of the …
Optimal Operation of Electric Power Production System without Transmission Losses Using Artificial Neural Networks Based on Augmented Lagrange Multiplier …
The optimal economic operation of a thermal electric power production system without
considering transmission losses is a critical problem for ships, aircrafts, island power …
considering transmission losses is a critical problem for ships, aircrafts, island power …
Intelligent Data Analysis in Electric Power Engineering Applications
This chapter presents various intelligent approaches for modelling, generalization and
knowledge extraction from data, which are applied in different electric power engineering …
knowledge extraction from data, which are applied in different electric power engineering …