A novel solar power prediction model based on stacked BiLSTM deep learning and improved extreme learning machine

Bourakadi, Dounia El; Ramadan, Hiba; Yahyaouy, Ali; Boumhidi, Jaouad

doi:10.1007/s41870-022-01118-1

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…BILSTM [30] is a type of RNN (Recurrent Neural Network) [28] that addresses the challenge of gradient vanishing and exploding during training. In this study, LSTM is to classify words, CRF (Conditional Random Field) [29], and continuously obtain restrictive rules from the training data to ensure the predicted labels.…”

Section: Bilstm Algorithmmentioning

confidence: 99%

“…However, the unidirectional LSTM model can only learn the historical information but cannot learn information about future periods, so the bidirectional BILSTM appears. The BILSTM [30] adopts two neurons to obtain the semantic information from the forward and backward directions.It finally inputs the results of the two hidden layers into the same output layer by splicing. Figure 4 shows BILSTM neural network structure.…”

Section: Bilstm Algorithmmentioning

confidence: 99%

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Design of agricultural question answering information extraction method based on improved LSTM algorithm

Tang,

Aridas,

Talip

2024

Preprint

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With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the IM-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python's Scrapy crawler framework to obtain the imformation of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT(Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF(Conditional Random Field) and BILSTM algorithm, the result shows that the IM-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, so as to Improve the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers' needs and interests, the system can better recommend relevant and practical information.

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Section: Bilstm Algorithmmentioning

confidence: 99%

Section: Bilstm Algorithmmentioning

confidence: 99%

Design of agricultural question answering information extraction method based on improved LSTM algorithm

Tang,

Aridas,

Talip

2024

Preprint

View full text Add to dashboard Cite

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“…In essence, the use of machine learning in solar energy is a major innovation that has the potential to improve the accessibility, efficiency, and integration of renewable energy into our energy systems [179]. It is projected that the use of machine learning technology in solar energy will increase as a result of its improvement, which will result in the creation of more inventive solutions in the field of renewable energy technologies [180], [181]. The following (Table II) is the summary of the use of different ML techniques in the domain of solar energy: With a mean absolute percentage error (MAPE) of 5.1% and a root mean squared error (RMSE) of 0.29, the RBF model was able to predict solar radiation efficiently.…”

Section: A Ai and ML Techniques In Renewable Energy Forecasting 1) So...mentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

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“…Deep learning methods, particularly Gated Recurrent Unit (GRU), have been effective in enhancing wind farm efficiency by providing precise predictions of FOWT performance [15,16]. These methods are adept at adapting to the nonlinear relationships between inputs and outputs, a critical feature for optimizing performance, devising maintenance strategies, and integrating wind energy into power grids.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

Barooni,

Velioglu Sogut

2024

Clean Technol.

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The design and optimization of floating offshore wind turbines (FOWTs) pose significant challenges, stemming from the complex interplay among aerodynamics, hydrodynamics, structural dynamics, and control systems. In this context, this study introduces an innovative method for forecasting the dynamic behavior of FOWTs under various conditions by merging Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network. This model outperforms traditional numerical models by delivering precise and efficient predictions of dynamic FOWT responses. It adeptly handles computational complexities and reduces processing duration, while maintaining flexibility and effectively managing nonlinear dynamics. The model’s prowess is showcased through an analysis of a spar-type FOWT in a multivariate parallel time series dataset using the CNN–GRU structure. The outcomes are notably promising, underscoring the model’s proficiency in accurately forecasting the performance of FOWTs.

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A novel solar power prediction model based on stacked BiLSTM deep learning and improved extreme learning machine

Cited by 14 publications

References 19 publications

Design of agricultural question answering information extraction method based on improved LSTM algorithm

Design of agricultural question answering information extraction method based on improved LSTM algorithm

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

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