An overview of energy demand forecasting methods published in 2005–2015

I Ghalehkhondabi, E Ardjmand, GR Weckman… - Energy Systems, 2017 - Springer
The importance of energy demand management has been more vital in recent decades as
the resources are getting less, emission is getting more and developments in applying …

Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review

M Santhosh, C Venkaiah… - Engineering …, 2020 - Wiley Online Library
Wind power is playing a pivotal part in global energy growth as it is clean and pollution‐free.
To maximize profits, economic scheduling, dispatching, and planning the unit commitment …

An attention‐based CNN‐LSTM‐BiLSTM model for short‐term electric load forecasting in integrated energy system

K Wu, J Wu, L Feng, B Yang, R Liang… - … on Electrical Energy …, 2021 - Wiley Online Library
In recent years, diverse energy has been integrated into the power system, which constitutes
a regional integrated energy system (IES). However, the coupling and complementation of …

Metaverse-driven remote management solution for scene-based energy storage power stations

Y Deng, Z Weng, T Zhang - Evolutionary Intelligence, 2023 - Springer
The Metaverse is a new Internet application and social form that integrates a variety of new
technologies. With the “carbon peak, carbon neutrality” goal and the proposal of a new …

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

J Buitrago, S Asfour - Energies, 2017 - mdpi.com
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 …

Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market

ÖÖ Bozkurt, G Biricik, ZC Tayşi - PloS one, 2017 - journals.plos.org
Load information plays an important role in deregulated electricity markets, since it is the
primary factor to make critical decisions on production planning, day-to-day operations, unit …

Application of bidirectional recurrent neural network combined with deep belief network in short-term load forecasting

X Tang, Y Dai, Q Liu, X Dang, J Xu - IEEE Access, 2019 - ieeexplore.ieee.org
The importance of conducting potential analysis of load data and ensuring the effectiveness
of feature selection cannot be overstated when it comes to enhancing the accuracy of short …

A comparison study of predictive models for electricity demand in a diverse urban environment

JE Pesantez, B Li, C Lee, Z Zhao, M Butala, AS Stillwell - Energy, 2023 - Elsevier
The increasing population migration to urban and peri-urban areas increases basic service
needs for cities worldwide. Residential electricity demand increases with more customers …

Short‐term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm

O Abedinia, N Amjady - International transactions on electrical …, 2016 - Wiley Online Library
In this paper a new model of radial basis function (RBF) neural network based on a novel
stochastic search algorithm is presented for short‐term load forecast (STLF). STLF is an …

A novel distributed paradigm for energy scheduling of islanded multiagent microgrids

M Tofighi-Milani, S Fattaheian-Dehkordi… - IEEE …, 2022 - ieeexplore.ieee.org
Restructuring in power systems has resulted in the development of microgrids (MGs) as
entities that could be operated in grid-connected or islanded modes while managing the …