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An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique
Abstract Machine learning mechanism is establishing itself as a promising area for
modelling and forecasting complex time series over conventional statistical models. In this …
modelling and forecasting complex time series over conventional statistical models. In this …
Tutorial on time series prediction using 1D-CNN and BiLSTM: A case example of peak electricity demand and system marginal price prediction
J Kim, S Oh, H Kim, W Choi - Engineering Applications of Artificial …, 2023 - Elsevier
Although research on time series prediction based on deep learning is being actively carried
out in various industries, deep learning technology still has a high entry barrier for …
out in various industries, deep learning technology still has a high entry barrier for …
[HTML][HTML] Solar radiation forecasting using machine learning and ensemble feature selection
Accurate solar radiation forecasting is essential to operate power systems safely under high
shares of photovoltaic generation. This paper compares the performance of several machine …
shares of photovoltaic generation. This paper compares the performance of several machine …
[HTML][HTML] An ensemble learning based classification approach for the prediction of household solid waste generation
With the increase in urbanization and smart cities initiatives, the management of waste
generation has become a fundamental task. Recent studies have started applying machine …
generation has become a fundamental task. Recent studies have started applying machine …
A multi-population particle swarm optimization-based time series predictive technique
In several businesses, forecasting is needed to predict expenses, future revenue, and profit
margin. As such, accurate forecasting is pivotal to the success of those businesses. Due to …
margin. As such, accurate forecasting is pivotal to the success of those businesses. Due to …
Energy load forecasting: one-step ahead hybrid model utilizing ensembling
In the light of the adverse effects of climate change, data analysis and Machine Learning
(ML) techniques can provide accurate forecasts, which enable efficient scheduling and …
(ML) techniques can provide accurate forecasts, which enable efficient scheduling and …
Solar radiation forecasting with deep learning techniques integrating geostationary satellite images
The prediction of solar radiation allows estimating photovoltaic systems' power production in
advance, guaranteeing a more reliable and stable energy supply. In this work, we present a …
advance, guaranteeing a more reliable and stable energy supply. In this work, we present a …
A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields
Modeling typhoon-induced storm surges requires 10-m wind and sea level pressure fields
as forcings, commonly obtained using parametric models or a fully dynamical simulation by …
as forcings, commonly obtained using parametric models or a fully dynamical simulation by …
A variational LSTM emulator of sea level contribution from the Antarctic ice sheet
The Antarctic ice sheet (AIS) will be a dominant contributor to global mean sea level rise in
the 21st century but remains a major source of uncertainty. The Ice Sheet Model …
the 21st century but remains a major source of uncertainty. The Ice Sheet Model …
Short-term load forecasting of the greek electricity system
Featured Application In spite of the significant developments in machine learning methods
employed for short-term electrical load forecasting on a Country level, the complexity and …
employed for short-term electrical load forecasting on a Country level, the complexity and …