Wind Speed Prediction Based on Statistical and Deep Learning Models

Tyass, Ilham; Khalili, Tajeddine; Rafik, Mohamed; Bellat, Abdelouahad; Raihani, Abdelhadi; Mansouri, Khalifa

doi:10.14710/ijred.2023.48672

Cited by 7 publications

(3 citation statements)

References 56 publications

(37 reference statements)

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“…Firstly, the multi-agent system emerges as a significant asset for coordinating the diverse entities within the microgrid [16], such as renewable energy sources, storage systems, and consumers, allowing effective communication and decentralized decision-making. Simultaneously, renewable energy prediction through advanced models and artificial intelligence algorithms 1238 is a key element in anticipating the availability of clean energy sources, thereby facilitating their optimal integration into the microgrid [17]- [19]. Thus, the microgrid can aim for advanced, resilient, cost-effective, and environmentally friendly energy management, providing a promising energy future for local communities.…”

Section: Mechanisms For Achieving Primary Objectivesmentioning

confidence: 99%

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

Tyass,

Bellat,

Raihani

et al. 2024

IJPEDS

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In the dynamic landscape of renewable energies, microgrid systems emerge as a promising avenue for fostering sustainable local energy generation. However, the effective management of energy resources holds the key to unlocking their full potential. This study assumes the task of creating a multi-objective optimization algorithm for microgrid energy management. At its core, the algorithm places a premium on seamlessly integrating renewable energy sources and orchestrating efficient storage coordination. Leveraging the prowess of a multi-agent system, it allocates and utilizes energy resources. Through the combination of renewable sources, storage mechanisms, and variable loads, the algorithm promotes energy efficiency and ensures a steady power supply. This transformative solution is underscored by the algorithm's remarkable performance in practical simulations and validations across diverse microgrid scenarios, offering a prevue into the future of sustainable energy utilization.

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Section: Mechanisms For Achieving Primary Objectivesmentioning

confidence: 99%

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

Tyass,

Bellat,

Raihani

et al. 2024

IJPEDS

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“…Convolutional Neural Network, and Long Short-Term Memory, with historical photovoltaic power values and sky images as inputs (Limouni & Yaagoubi, 2022). The authors considered the LSTM-based model to be superior to all other methods at that time (Tyass & Khalili, 2023;Nhat & Huu, 2023). However, the use of a single nonlinear regression can easily fall into local optima and has certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic power prediction based on sky images and tokens-to-token vision transformer

Dai,

Huo,

et al. 2023

Int. J. Renew. Energy Dev.

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Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this paper, a new deep neural network structure based on vision transformer is proposed to combine sky images and Tokens-To-Token（T2T） for photovoltaic power prediction. The method uses an incremental tokenization module to aggregate neighboring image patches into tokens, which capture the local structural information of the clouds. Then, an efficient T2T-ViT backbone network is used to extract the global attentional relationships of the tokens for power prediction. In order to evaluate the performance of the proposed model, the method was compared with several deep learning architectures such as ResNet and GoogleNet on a dataset collected by the National Renewable Energy Laboratory in Colorado, USA. The results of power prediction were analysed using training loss, prediction error, and linear regression, and they show that the proposed method achieves higher prediction accuracy and lower error compared to the existing methods, especially in short- and ultra-short-term prediction. The paper demonstrates the potential of applying Transformer models to computer vision tasks for renewable energy forecasting. The results show that the proposed method achieves higher prediction accuracy and lower error than several deep learning architectures, such as ResNet and GoogleNet, especially in short- and ultra-short-term prediction.

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“…Tyass et al[22] Medium-term forecastingForecasted the wind speed by combining the statistical SARIMA model with the deep neural network model. MAPE ranged from 10.50% to 15.94% for LSTM model and 10.67% to 16.10 for SARIMA model.…”

mentioning

confidence: 99%

Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

Ponkumar

Jayaprakash

Karthick³

2023

Energies

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Accurate wind power forecasting plays a crucial role in the planning of unit commitments, maintenance scheduling, and maximizing profits for power traders. Uncertainty and changes in wind speeds pose challenges to the integration of wind power into the power system. Therefore, the reliable prediction of wind power output is a complex task with significant implications for the efficient operation of electricity grids. Developing effective and precise wind power prediction systems is essential for the cost-efficient operation and maintenance of modern wind turbines. This article focuses on the development of a very-short-term forecasting model using machine learning algorithms. The forecasting model is evaluated using LightGBM, random forest, CatBoost, and XGBoost machine learning algorithms with 16 selected parameters from the wind energy system. The performance of the machine learning-based wind energy forecasting is assessed using metrics such as mean absolute error (MAE), mean-squared error (MSE), root-mean-squared error (RMSE), and R-squared. The results indicate that the random forest algorithm performs well during training, while the CatBoost algorithm demonstrates superior performance, with an RMSE of 13.84 for the test set, as determined by 10-fold cross-validation.

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Wind Speed Prediction Based on Statistical and Deep Learning Models

Cited by 7 publications

References 56 publications

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

Photovoltaic power prediction based on sky images and tokens-to-token vision transformer

Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

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