Attention mechanism for developing wind speed and solar irradiance forecasting models

Brahma, Banalaxmi; Wadhvani, Rajesh; Shukla, Sanyam

doi:10.1177/0309524x20981885

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…The potential of machine learning and deep learning in this context represents a substantial enhancement of how energy forecasting is approached, offering more accurate, reliable, and dynamic predictions. Further enhancing the predictive capabilities in renewable energy forecasting, attention mechanisms, as explored by Brahma et al, have been identified as a promising frontier in improving the accuracy of wind speed and solar irradiance models [20]. These mechanisms, part of the broader suite of advanced deep learning techniques, bring a nuanced understanding of the temporal and spatial dependencies in weather patterns, which are crucial for precise energy forecasting.…”

Section: State Of the Artmentioning

confidence: 99%

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Nikulins,

Sudars,

Edelmers

et al. 2024

Energies

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This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server.

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Section: State Of the Artmentioning

confidence: 99%

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Nikulins,

Sudars,

Edelmers

et al. 2024

Energies

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show abstract

“…Subsequently, ref. [160] utilized six variants of RNN techniques, which are RNN, GRU, Content-Based Attention, Luong Attention, Self-Attention-Based RNN, and LSTM for solar radiation forecasting. A comparative analysis was conducted, using a 37-year dataset, between the two independent sites.…”

Section: Solar Irradiance Forecasting Model Based On Deep Hybrid Modelmentioning

confidence: 99%

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

et al. 2023

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The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term.

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Visualizing solar irradiance data in ArcGIS and forecasting based on a novel deep neural network mechanism

Brahma

Wadhvani

2021

Multimed Tools Appl

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Attention mechanism for developing wind speed and solar irradiance forecasting models

Cited by 8 publications

References 29 publications

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

Visualizing solar irradiance data in ArcGIS and forecasting based on a novel deep neural network mechanism

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