Long Short-Term Memory Model for Time Series Prediction and Forecast of Solar Radiation and other Weather Parameters

Abayomi‐Alli, Adebayo; Odusami, Modupe; Abayomi‐Alli, Olusola; Misra, Sanjay; Ibeh, G. F.

doi:10.1109/iccsa.2019.00004

Cited by 14 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The differential evolution algorithm is utilized to upgrade the model, although it is difficult to balance. Alli et al [17] used the time series model-based LSTM neural network to predict solar radiation, wind speed, precipitation, relative humidity, and temperature values. Moreover, this model is able to cope with different types of weather data.…”

Section: Introductionmentioning

confidence: 99%

Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks

Alharbi

Csala

2021

Energies

View full text Add to dashboard Cite

The rapid growth of wind and solar energy penetration has created critical issues, such as fluctuation, uncertainty, and intermittence, that influence the power system stability, grid operation, and the balance of the power supply. Improving the reliability and accuracy of wind and solar energy predictions can enhance the power system stability. This study aims to contribute to the issues of wind and solar energy fluctuation and intermittence by proposing a high-quality prediction model based on neural networks (NNs). The most efficient technology for analyzing the future performance of wind speed and solar irradiance is recurrent neural networks (RNNs). Bidirectional RNNs (BRNNs) have the advantages of manipulating the information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BRNN’s output layer concurrently receives information from both the backward layers and the forward layers. The bidirectional long short-term memory (BI-LSTM) prediction model was designed to predict wind speed, solar irradiance, and ambient temperature for the next 169 h. The solar irradiance data include global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The historical data collected from Dumat al-Jandal City covers the period from 1 January 1985 to 26 June 2021, as hourly intervals. The findings demonstrate that the BI-LSTM model has promising performance in terms of evaluation, with considerable accuracy for all five types of historical data, particularly for wind speed and ambient temperature values. The model can handle different sizes of sequential data and generates low error metrics.

show abstract

Section: Introductionmentioning

confidence: 99%

Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks

Alharbi

Csala

2021

Energies

View full text Add to dashboard Cite

show abstract

An Approach to the Selection of a Time Series Analysis Method Using a Neural Network

Yashin

Moshkina

Moshkin

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In this paper, a technique for selecting individual methods in a combined time series forecasting model is described. A neural network at the input of which a vector of time series metrics is proposed. The metrics corresponds to significant characteristics of the time series. The values of the metrics are easily computed. The neural network calculates the estimated prediction error for each model from the base set of the combined model. The proposed selection method is most effective for short time series and when the base set contains a lot of complex prediction methods. The developed system was tested on the time series from the CIF 2015-2016 competitions. According to the result of the experiment, the application of the developed system allowed to reduce the average forecast error from 13 to 9 percent.

show abstract

Big Data and Deep Learning Analytics for Robust PV Power Forecast in Smart Grids

Zhang

Wang

Dehghanian

2021

Advanced Technologies for Solar Photovoltaics Energy Systems

View full text Add to dashboard Cite

Long Short-Term Memory Model for Time Series Prediction and Forecast of Solar Radiation and other Weather Parameters

Cited by 14 publications

References 36 publications

Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks

Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks

An Approach to the Selection of a Time Series Analysis Method Using a Neural Network

Big Data and Deep Learning Analytics for Robust PV Power Forecast in Smart Grids

Contact Info

Product

Resources

About