Study of GOES-R Thermodynamic Indices for Short-Term Forecasting of Convective Weather Events Using Machine Learning

2021 ◽  
Author(s):  
Caroline Menegussi Soares ◽  
Gutemberg Borges França ◽  
Manoel Valdonel de Almeida ◽  
Vinícius Albuquerque de Almeida
Keyword(s):  
Machine Learning ◽  
Short Term ◽  
Weather Events ◽  
Short Term Forecasting ◽  
Convective Weather

Hybrid short-term forecasting of the electric demand of supply fans using machine learning

2020 ◽  
Vol 29 ◽  
pp. 101144 ◽  
Author(s):  
Jason Runge ◽  
Radu Zmeureanu ◽  
Mathieu Le Cam
Keyword(s):  
Machine Learning ◽  
Short Term ◽  
Short Term Forecasting

scholarly journals Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach

2020 ◽  
Vol 10 (23) ◽  
pp. 8400 ◽  
Author(s):  
Abdelkader Dairi ◽  
Fouzi Harrou ◽  
Ying Sun ◽  
Sofiane Khadraoui
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Solar Power ◽  
Power Production ◽  
Superior Performance ◽  
Support Vector ◽  
Learning Models ◽  
Short Term ◽  
Learning Methods ◽  
Short Term Forecasting

The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models.

scholarly journals Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)

2019 ◽  
Vol 9 (4) ◽  
pp. 4548-4553
Author(s):  
N. T. Dung ◽  
N. T. Phuong
Keyword(s):  
Machine Learning ◽  
Electricity Market ◽  
Business Strategy ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecast Errors ◽  
Short Term ◽  
Load Profile ◽  
Short Term Forecasting

Short-term load forecasting (STLF) plays an important role in business strategy building, ensuring reliability and safe operation for any electrical system. There are many different methods used for short-term forecasts including regression models, time series, neural networks, expert systems, fuzzy logic, machine learning, and statistical algorithms. The practical requirement is to minimize forecast errors, avoid wastages, prevent shortages, and limit risks in the electricity market. This paper proposes a method of STLF by constructing a standardized load profile (SLP) based on the past electrical load data, utilizing Support Regression Vector (SVR) machine learning algorithm to improve the accuracy of short-term forecasting algorithms.

Short-Term Forecasting for Bohai Sea Ice Management Based on Machine Learning Theory

2021 ◽  
Vol 35 (4) ◽  
pp. 05021001
Author(s):  
Songsong Yu ◽  
Dayong Zhang ◽  
Siyin Li ◽  
Qixin Lv ◽  
Qianjin Yue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Learning Theory ◽  
Bohai Sea ◽  
Short Term ◽  
Short Term Forecasting

scholarly journals Short-term forecasting of CO2 emission intensity in power grids by machine learning

2020 ◽  
Vol 277 ◽  
pp. 115527
Author(s):  
Kenneth Leerbeck ◽  
Peder Bacher ◽  
Rune Grønborg Junker ◽  
Goran Goranović ◽  
Olivier Corradi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emission Intensity ◽  
Co2 Emission ◽  
Power Grids ◽  
Short Term ◽  
Co2 Emission Intensity ◽  
Short Term Forecasting
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