Publication: Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks
Date
2021
Authors
Rahman M.M.
Shakeri M.
Tiong S.K.
Khatun F.
Amin N.
Pasupuleti J.
Hasan M.K.
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI AG
Abstract
This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world�s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A �hybrid-renewable-energy system� (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as �multi-layer perception� (MLP), �recurrent-neural network� (RNN), and �convolutional-neural network� (CNN), as well as �long-short-term memory� (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Description
alternative energy; artificial neural network; complexity; demand analysis; machine learning; prediction; rural area; smart grid; time series analysis