scholarly journals Daily Average Wind Energy Forecasting Using Artificial Neural Networks

2017 ◽  
Vol 181 ◽  
pp. 829-836 ◽  
Author(s):  
Cristian-Dragos Dumitru ◽  
Adrian Gligor
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Energy ◽  
Energy Forecasting ◽  
Daily Average ◽  
Average Wind ◽  
Artificial Neural

Artificial neural networks for short-term energy forecasting: Accuracy and economic value

1998 ◽  
Vol 23 (1-3) ◽  
pp. 71-84 ◽  
Author(s):  
Benjamin F Hobbs ◽  
Udi Helman ◽  
Suradet Jitprapaikulsarn ◽  
Sreenivas Konda ◽  
Dominic Maratukulam
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Economic Value ◽  
Short Term ◽  
Energy Forecasting ◽  
Forecasting Accuracy ◽  
Term Energy ◽  
Artificial Neural

A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks

2005 ◽  
Vol 30 (2) ◽  
pp. 227-239 ◽  
Author(s):  
Ertugrul Çam ◽  
Erol Arcaklıoğlu ◽  
Abdullah Çavuşoğlu ◽  
Bilge Akbıyık
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Average Wind Speed ◽  
Average Wind ◽  
Artificial Neural

scholarly journals Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 2393
Author(s):  
Md Mijanur Rahman ◽  
Mohammad Shakeri ◽  
Sieh Kiong Tiong ◽  
Fatema Khatun ◽  
Nowshad Amin ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Series Data ◽  
Short Term ◽  
Energy Forecasting ◽  
Artificial Neural ◽  
Hybrid Renewable Energy

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.

scholarly journals Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3254 ◽  
Author(s):  
Jason Runge ◽  
Radu Zmeureanu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Building Energy ◽  
Data Driven ◽  
Past Century ◽  
Energy Forecasting ◽  
The Past ◽  
Artificial Neural

During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.

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