Time series forecasting on multivariate solar radiation data using deep learning (LSTM)
Energy management is an emerging problem nowadays and utilization of renewable energy
sources is an efficient solution. Solar radiation is an important source for electricity …
sources is an efficient solution. Solar radiation is an important source for electricity …
Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models
This article addresses two issues in solar energy forecasting from the numerical weather
prediction (NWP) models using machine learning. First, we are interested in determining the …
prediction (NWP) models using machine learning. First, we are interested in determining the …
Machine learning and deep learning models applied to photovoltaic production forecasting
M Cordeiro-Costas, D Villanueva, P Eguía-Oller… - Applied Sciences, 2022 - mdpi.com
Featured Application The comparison carried out in this paper through different Machine
Learning and Deep Learning models defines the most appropriate techniques to forecast …
Learning and Deep Learning models defines the most appropriate techniques to forecast …
Assessment of different deep learning methods of power generation forecasting for solar PV system
WC Kuo, CH Chen, SH Hua, CC Wang - Applied Sciences, 2022 - mdpi.com
An increase in renewable energy injected into the power system will directly cause a
fluctuation in the overall voltage and frequency of the power system. Thus, renewable …
fluctuation in the overall voltage and frequency of the power system. Thus, renewable …
Prediction of solar energy guided by pearson correlation using machine learning
Solar energy forecasting represents a key element in increasing the competitiveness of solar
power plants in the energy market and reducing the dependence on fossil fuels in economic …
power plants in the energy market and reducing the dependence on fossil fuels in economic …
Designing a long short-term network for short-term forecasting of global horizontal irradiance
Long short-term memory (LSTM) models based on specialized deep neural network-based
architecture have emerged as an important model for forecasting time-series. However, the …
architecture have emerged as an important model for forecasting time-series. However, the …
A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting
J Huang, M Perry - International Journal of Forecasting, 2016 - Elsevier
The aim of this work is to produce probabilistic forecasts of solar power for the Global Energy
Forecasting Competition 2014 (GEFCom2014). The task involves predicting the outputs from …
Forecasting Competition 2014 (GEFCom2014). The task involves predicting the outputs from …
Deep learning-based estimation of PV power plant potential under climate change: a case study of El Akarit, Tunisia
A Ben Othman, A Ouni, M Besbes - Energy, Sustainability and Society, 2020 - Springer
Background Several climatologists and experts in the renewable energy field agree that GHI
and DNI calculation models must be revised because of the increasingly unpredictable and …
and DNI calculation models must be revised because of the increasingly unpredictable and …
Single and blended models for day-ahead photovoltaic power forecasting
J Antonanzas, R Urraca, A Pernía-Espinoza… - … Intelligent Systems: 12th …, 2017 - Springer
Solar power forecasts are gaining continuous importance as the penetration of solar energy
into the grid rises. The natural variability of the solar resource, joined to the difficulties of …
into the grid rises. The natural variability of the solar resource, joined to the difficulties of …
An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network
In recent years, the penetration of solar power at residential and utility levels has progressed
exponentially. However, due to its stochastic nature, the prediction of solar global horizontal …
exponentially. However, due to its stochastic nature, the prediction of solar global horizontal …