Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs
Short-term load forecasting is crucial for the operations planning of an electrical grid.
Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize …
Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize …
Analysis of classical and machine learning based short-term and mid-term load forecasting for smart grid
The evolution of advanced metering infrastructure (AMI) has increased the electricity
consumption data in real-time manifolds. Using this massive data, the load forecasting …
consumption data in real-time manifolds. Using this massive data, the load forecasting …
Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem
Swarm-based algorithms are widely accepted in different fields of engineering as they have
proven themselves effective in solving real-world optimization problems. According to the …
proven themselves effective in solving real-world optimization problems. According to the …
Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply …
This study aims to develop statistical and machine learning methodologies for forecasting
yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) …
yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) …
Energy consumption prediction model with deep inception residual network inspiration and LSTM
Predicting electricity consumption is not an easy task depending on many factors that affect
energy consumption. Therefore, electricity utilities and governments are always searching …
energy consumption. Therefore, electricity utilities and governments are always searching …
Network traffic prediction for detecting DDoS attacks in IEC 61850 communication networks
This article presents the development of a Generic Object Oriented Substation Event
(GOOSE) message traffic prediction system using a Nonlinear Autoregressive Model with …
(GOOSE) message traffic prediction system using a Nonlinear Autoregressive Model with …
Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks
This paper presents an intelligent mechanism for Short Term Load Forecasting (STLF)
models, which allows self-adaptation with respect to the load operational conditions …
models, which allows self-adaptation with respect to the load operational conditions …
Very short-term load forecaster based on a neural network technique for smart grid control
Electrical load forecasting plays a crucial role in the proper scheduling and operation of
power systems. To ensure the stability of the electrical network, it is necessary to balance …
power systems. To ensure the stability of the electrical network, it is necessary to balance …
Artificial neural networks application for top oil temperature and loss of life prediction in power transformers
The development of precise tools for power transformers temperature prediction allows a
better use of equipment's nominal capacity, extending its useful life and possibility of …
better use of equipment's nominal capacity, extending its useful life and possibility of …
Very short term load forecasting using hybrid regression and interval type-1 fuzzy inference
The growth of electricity consumption in this world is getting higher. The operation of the
electric power starts from the generation system, Transmission system, and distribution …
electric power starts from the generation system, Transmission system, and distribution …