Forecasting with artificial neural networks:: The state of the art
Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous
surge in research activities in the past decade. While ANNs provide a great deal of promise …
surge in research activities in the past decade. While ANNs provide a great deal of promise …
Big data driven smart energy management: From big data to big insights
K Zhou, C Fu, S Yang - Renewable and sustainable energy reviews, 2016 - Elsevier
Large amounts of data are increasingly accumulated in the energy sector with the
continuous application of sensors, wireless transmission, network communication, and cloud …
continuous application of sensors, wireless transmission, network communication, and cloud …
Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches
Background: With the development of smart grids, accurate electric load forecasting has
become increasingly important as it can help power companies in better load scheduling …
become increasingly important as it can help power companies in better load scheduling …
Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
This paper presents a recurrent neural network model to make medium-to-long term
predictions, ie time horizon of⩾ 1 week, of electricity consumption profiles in commercial and …
predictions, ie time horizon of⩾ 1 week, of electricity consumption profiles in commercial and …
Neural networks for short-term load forecasting: A review and evaluation
HS Hippert, CE Pedreira… - IEEE Transactions on …, 2001 - ieeexplore.ieee.org
Load forecasting has become one of the major areas of research in electrical engineering,
and most traditional forecasting models and artificial intelligence techniques have been tried …
and most traditional forecasting models and artificial intelligence techniques have been tried …
Energy models for demand forecasting—A review
L Suganthi, AA Samuel - Renewable and sustainable energy reviews, 2012 - Elsevier
Energy is vital for sustainable development of any nation–be it social, economic or
environment. In the past decade energy consumption has increased exponentially globally …
environment. In the past decade energy consumption has increased exponentially globally …
When pandemics impact economies and climate change: Exploring the impacts of COVID-19 on oil and electricity demand in China
N Norouzi, GZ de Rubens, S Choupanpiesheh… - Energy research & …, 2020 - Elsevier
Despite all the scientific and technological developments in the past one hundred years,
biologic issues such as pandemics are a constant threat to society. While one of the aspects …
biologic issues such as pandemics are a constant threat to society. While one of the aspects …
A review of deep learning with special emphasis on architectures, applications and recent trends
Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
A comprehensive review of the load forecasting techniques using single and hybrid predictive models
A Al Mamun, M Sohel, N Mohammad… - IEEE …, 2020 - ieeexplore.ieee.org
Load forecasting is a pivotal part of the power utility companies. To provide load-shedding
free and uninterrupted power to the consumer, decision-makers in the utility sector must …
free and uninterrupted power to the consumer, decision-makers in the utility sector must …
Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy
This paper describes a generic methodology to develop mathematical and computational
models of different components of the smart grid architecture model (SGAM). The SGAM …
models of different components of the smart grid architecture model (SGAM). The SGAM …