[PDF][PDF] Energy Reports

A Alhendi, AS Al-Sumaiti, M Marzband, R Kumar… - 2023 - researchportal.northumbria.ac.uk
abstract Nowadays, forecasting methods have gained significant attention, particularly with
the design and development of energy systems. In fact, accurate load and price forecasting …

[HTML][HTML] Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England

A Alhendi, AS Al-Sumaiti, M Marzband, R Kumar… - Energy Reports, 2023 - Elsevier
Nowadays, forecasting methods have gained significant attention, particularly with the
design and development of energy systems. In fact, accurate load and price forecasting is …

AI application for load forecasting: a comparison of classical and deep learning methodologies

AKA Peñaloza, A Balbinot, R Leborgne - Monitoring and Control of …, 2023 - Elsevier
Electricity demand forecasting has become one of the main research topics in the energy
management system. For this reason, many different methodologies for load forecasting …

Advanced Time Series Forecasting Models for Electricity Demand Prediction: A Comparative Study

HH Mohammed, A Asem, H El-Bakry - Full Length Article, 2024 - americaspg.com
Electrical loading prediction is a key aspect of the power system governing, operating, and
scheduling. Energy suppliers can control the running system cost by using a lot of …

Deep learning for renewable power forecasting: An approach using LSTM neural networks

F Gökgöz, F Filiz - International Journal of Energy and Power …, 2018 - publications.waset.org
Load forecasting has become crucial in recent years and become popular in forecasting
area. Many different power forecasting models have been tried out for this purpose …

Analysis for non-residential short-term load forecasting using machine learning and statistical methods with financial impact on the power market

S Ungureanu, V Topa, AC Cziker - Energies, 2021 - mdpi.com
Short-term load forecasting predetermines how power systems operate because electricity
production needs to sustain demand at all times and costs. Most load forecasts for the non …

An efficient monthly load forecasting model using Gaussian process regression

A Yadav, A Kumar, RPS Rana… - 2021 IEEE 4th …, 2021 - ieeexplore.ieee.org
Load forecasting is a powerful tool which helps the electric utility to make important
decisions such as purchasing and generation of electric power, scheduling, load …

A novel electrical load forecasting model using a deep learning approach

NA Kumar, R Daniel, PK Pasam - The Internet of Energy, 2024 - taylorfrancis.com
The estimate of electricity appeal in modernistic years is becoming progressively relevant
thanks to market-free trade and, thus, the initiation of sustainable assets. To satisfy the …

Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid

G Hafeez, KS Alimgeer, I Khan - Applied Energy, 2020 - Elsevier
Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …

Short term load forecasting: A hybrid approach using data mining methods

P Borthakur, B Goswami - 2020 International Conference on …, 2020 - ieeexplore.ieee.org
Load forecasting and analysis play a crucial role in electric power management and market
planning of the grid system. In this paper, a hybrid approach using data mining methods is …