Solar Radiation Prediction Using Machine Learning Techniques: A Review

2019 ◽  
Vol 17 (04) ◽  
pp. 684-697 ◽  
Author(s):  
Edgar Dario Obando ◽  
Sandra Ximena Carvajal ◽  
Jairo Pineda Agudelo
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Machine Learning Techniques ◽  
Learning Techniques

Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

2021 ◽  
Author(s):  
Hugo Abreu Mendes ◽  
João Fausto Lorenzato Oliveira ◽  
Paulo Salgado Gomes Mattos Neto ◽  
Alex Coutinho Pereira ◽  
Eduardo Boudoux Jatoba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Time Series ◽  
Solar Radiation ◽  
Statistical Models ◽  
Clean Energy ◽  
Machine Learning Techniques ◽  
Exogenous Variables ◽  
Learning Techniques ◽  
Photovoltaic Plants

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.

scholarly journals A benchmarking of machine learning techniques for solar radiation forecasting in an insular context

Solar Energy ◽  
2015 ◽  
Vol 112 ◽  
pp. 446-457 ◽  
Author(s):  
Philippe Lauret ◽  
Cyril Voyant ◽  
Ted Soubdhan ◽  
Mathieu David ◽  
Philippe Poggi
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Spatial Estimation of Solar Radiation Using Geostatistics and Machine Learning Techniques

2020 ◽  
Vol 53 (2) ◽  
pp. 3216-3222
Author(s):  
A. Núñez-Reyes ◽  
S. Ruiz-Moreno
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Machine Learning Techniques ◽  
Spatial Estimation ◽  
Learning Techniques

scholarly journals A Short Term Day-Ahead Solar Radiation Prediction Using Machine Learning Techniques

2018 ◽  
Vol 06 (03) ◽  
Author(s):  
Mohammad Earfan Ali ◽  
Md. Earfan Ali Khondaker
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Learning Techniques

A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques

2019 ◽  
Vol 133 ◽  
pp. 575-592 ◽  
Author(s):  
Choongwan Koo ◽  
Wenzhuo Li ◽  
Seung Hyun Cha ◽  
Shaojie Zhang
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Spatial Pattern ◽  
Machine Learning Techniques ◽  
Learning Techniques

Predicting Solar Radiation Using Machine Learning Techniques

2018 ◽  
Author(s):  
Aaftaab Moosa ◽  
Hamza Shabir ◽  
Huzefa Ali ◽  
Rishikesh Darwade ◽  
Balasaheb Gite
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Machine Learning Techniques ◽  
Learning Techniques

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Blind Spoofing Detection for Multi-Antenna Snapshot Receivers using Machine-Learning Techniques

2020 ◽  
Author(s):  
J. Rossouw van der Merwe ◽  
Ana Nikolikj ◽  
Sebastian Kram ◽  
Ivana Lukcin ◽  
Gorjan Nadzinski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Spoofing Detection
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