Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion

Huang, Yu; Zhang, Bingzhe; Pang, Huizhen; Wang, Biao; Lee, Kwang Y.

doi:10.1016/j.renene.2022.04.055

Cited by 27 publications

(15 citation statements)

References 36 publications

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“…(2022) 316 proposed IGWO with the back-propagation neural network to predict short-term wind power. (3) Integrating techniques from other domains, such as attention mechanisms, 286 , 317 Clayton Copula function, 318 and Granular Computing, 319 into the field of wind forecasting.…”

Section: Discussionmentioning

confidence: 99%

An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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Section: Discussionmentioning

confidence: 99%

An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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“…P t indicates the output of the max pooling layer at t time that encompasses the energy utilization dataset and additional parameters utilized as input to AE through the CBLSTM layer. i t ; f t , and 0 t indicates the input, forget, and output gates; correspondingly, h t denotes the hidden state of the BLSTM cell that was upgraded at each t step in backward and forward directions [32][33][34][35][36]. Fig.…”

Section: Level I: Cblstmae-based Prediction Processmentioning

confidence: 99%

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Surendran¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer. In the presented IWSP-CSODL model, the prediction process is performed via a convolutional neural network (CNN) based long short-term memory with autoencoder (CBLSTMAE) model. To optimally modify the hyperparameters related to the CBLSTMAE model, the chicken swarm optimization (CSO) algorithm is utilized and thereby reduces the mean square error (MSE). The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios. The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.

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“…This family of copulas is used in a wide variety of applications. To mention several recent ones, e.g., Huang et al (2022) use it to analyse the correlation between the residual series of a long short-term memory neural network and a wind-speed series. Particularly, the maximum likelihood estimation of the copula parameter is utilized, i.e., the procedure that ChatGPT implements here in Section 3.3.…”

Section: The Copula Familymentioning

confidence: 99%

Composite pseudo-likelihood estimation for pair-tractable copulas such as Archimedean, Archimax and related hierarchical extensions

Górecki

Hofert

2023

Journal of Statistical Computation and Simulation

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Without writing a single line of code by a human, an example Monte Carlo simulation based application for stochastic dependence modeling with copulas is developed using a state-of-the-art large language model (LLM) fine-tuned for conversations. This includes interaction with Chat-GPT in natural language and using mathematical formalism, which, under careful supervision by a human-expert, led to producing a working code in MATLAB, Python and R for sampling from a given copula model, evaluation of the model's density, performing maximum likelihood estimation, optimizing the code for parallel computing for CPUs as well as for GPUs, and visualization of the computed results. In contrast to other emerging studies that assess the accuracy of LLMs like ChatGPT on tasks from a selected area, this work rather investigates ways how to achieve a successful solution of a standard statistical task in a collaboration of a human-expert and artificial intelligence (AI). Particularly, through careful prompt engineering, we separate successful solutions generated by ChatGPT from unsuccessful ones, resulting in a comprehensive list of related pros and cons. It is demonstrated that if the typical pitfalls are avoided, we can substantially benefit from collaborating with an AI partner. For example, we show that if ChatGPT is not able to provide a correct solution due to a lack of or incorrect knowledge, the human-expert can feed it with the correct knowledge, e.g., in the form of mathematical theorems and formulas, and make it to apply the gained knowledge in order to provide a solution that is correct. Such ability presents an attractive opportunity to achieve a programmed solution even for users with rather limited knowledge of programming techniques.

show abstract

Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion

Cited by 27 publications

References 36 publications

An overview of deterministic and probabilistic forecasting methods of wind energy

An overview of deterministic and probabilistic forecasting methods of wind energy

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Composite pseudo-likelihood estimation for pair-tractable copulas such as Archimedean, Archimax and related hierarchical extensions

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