A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
Forecasting methods in energy planning models
KB Debnath, M Mourshed - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy planning models (EPMs) play an indispensable role in policy formulation and energy
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
A survey of learning-based intelligent optimization algorithms
W Li, GG Wang, AH Gandomi - Archives of Computational Methods in …, 2021 - Springer
A large number of intelligent algorithms based on social intelligent behavior have been
extensively researched in the past few decades, through the study of natural creatures, and …
extensively researched in the past few decades, through the study of natural creatures, and …
Short-term load forecasting with deep residual networks
We present in this paper a model for forecasting short-term electric load based on deep
residual networks. The proposed model is able to integrate domain knowledge and …
residual networks. The proposed model is able to integrate domain knowledge and …
Convolutional and recurrent neural network based model for short-term load forecasting
H Eskandari, M Imani, MP Moghaddam - Electric Power Systems Research, 2021 - Elsevier
The consumed electrical load is affected by many external factors such as weather, season
of the year, weekday or weekend and holiday. In this paper, it is tried to provide a high …
of the year, weekday or weekend and holiday. In this paper, it is tried to provide a high …
Empirical mode decomposition based ensemble deep learning for load demand time series forecasting
X Qiu, Y Ren, PN Suganthan, GAJ Amaratunga - Applied soft computing, 2017 - Elsevier
Load demand forecasting is a critical process in the planning of electric utilities. An
ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep …
ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep …
Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting
Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also
has a great interest to investors and energy policy maker as well as government. Literature …
has a great interest to investors and energy policy maker as well as government. Literature …
Short-term electrical load forecasting through heuristic configuration of regularized deep neural network
An accurate electrical load forecasting is essential for optimal grid operation. The paper
presents a methodology for the short-term commercial building electrical load forecasting …
presents a methodology for the short-term commercial building electrical load forecasting …
Reinforced Deterministic and Probabilistic Load Forecasting via -Learning Dynamic Model Selection
Both deterministic and probabilistic load forecasting (DLF and PLF) are of critical importance
to reliable and economical power system operations. However, most of the widely used …
to reliable and economical power system operations. However, most of the widely used …
Computational intelligence on short-term load forecasting: A methodological overview
Electricity demand forecasting has been a real challenge for power system scheduling in
different levels of energy sectors. Various computational intelligence techniques and …
different levels of energy sectors. Various computational intelligence techniques and …