Forecasting wind power generation by a new type of radial basis function-based neural network

Chang, G.W.; Lu, H. J.; Chen, Y. Y.; Chang, Yung‐Ruei

doi:10.1109/pesgm.2017.8273959

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Over the years, EGF has gained importance due to the growing need for renewable energy technologies (RETs) integration into and the decentralization of conventional grid energy from distributed resources. Furthermore, EGF has become a critical aspect of energy dynamics due to its potential to improve energy efficiency and facilitate demand-side management (Chang et al, 2018, Verma et al, 2016. On the whole, it has the potential to bolster grid resilience against the twin scourges of global warming and climate change using data and technological advancements.…”

Section: Energy Grid Forecasting (Egf)mentioning

confidence: 99%

“…In the study by Zhang et al (2015) similar day and Elman neural networks were utilised for short-term forecasting of power generation from wind energy installations. On the other hand, Chang et al (2018) proposed the use of a novel type of radial basis function-based neural network to forecast the power generation capacity of wind energy. Recently, Wang et al (2022) explored the nonparametric probabilistic forecasting of wind power generation using a quadratic slope quantile function and an autoregressive recurrent neural network.…”

Section: Energy Grid Forecasting (Egf)mentioning

confidence: 99%

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Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

Zaidi

2024

IJEEP

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The research landscape on the applications of advanced computational tools (ACTs) such as machine/deep learning and neural network algorithms for energy and power generation (EPG) was critically examined through publication trends and bibliometrics data analysis. The Elsevier Scopus database and the PRISMA methodology were employed to identify and screen the published documents, whereas the bibliometric analysis software VOSviewer was used to analyse the co-authorships, citations, and keyword occurrences. The results showed that 152 documents have been published on the topic comprising conference proceedings (58.6%) and articles (41.4%) between 2004 and 2022. Publication trends analysis revealed the number of publications increased from 1 to 31 or by 3,000% over the same period, which was ascribed to the growing scientific interest and research impact of the topic. Stakeholder analysis revealed the top authors/researchers are Anvari M, Ghaderi SF and Saberi M, whereas the most prolific affiliation and nations actively engaged in the topic are the North China Electric Power University, and China, respectively. Conversely, the top funding agency actively backing research on the topic is the National Natural Science Foundation of China (NSFC). Co-authorship analysis revealed high levels of collaboration between researching nations compared to authors and affiliations. Hotspot analysis revealed three major thematic focus areas namely; Energy Grid Forecasting, Power Generation Control, and Intelligent Energy Optimization. In conclusion, the study showed that the application of ACTs in EPG is an active, multidisciplinary, and impact area of research with potential for more impactful contributions to research and society at large.

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Section: Energy Grid Forecasting (Egf)mentioning

confidence: 99%

Section: Energy Grid Forecasting (Egf)mentioning

confidence: 99%

Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

Zaidi

2024

IJEEP

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“…NN has a strong talent to train and learn from a dataset. The applications of NN in power industry include monitoring active power filter, forecasting electrical loads in short term, analyzing and forecasting electrical energy consumption of a building, forecasting the wind power production, and forecasting electric prices [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the RBF has been catching the attention of researchers recently with the function approximation of its hidden neurons [17]. This research includes enhance the performance of RBF and apply RBF in various industries [7,[18][19][20][21]. RBF parks under the group of feedforward NN, which organizes its hidden neurons with function approximation theory.…”

Section: Introductionmentioning

confidence: 99%

Harmonic current classification using hybrid FAM-RBF neural network

Leow

Yap

Wong

2020

IJEECS

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In this paper, the type of customers of electricity in Malaysia is classified into the type of electricity consumers based on the harmonic current data. A hybrid of Fuzzy Adaptive Resonance Theory with Mapping Algorithm (Fuzzy ARTMAP) and Radial Basis Function (RBF) neural network is developed (namely FAM-RBF), and it is used to classify the harmonic current into types of consumers. The result of the proposed neural network is discussed, and compared with other neural networks in this paper. The comparison result shows that the proposed FAM-RBF obtained the best performance result and is a truthful neural network to be used in this application.

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Multi-step Ahead Wind Power Forecasting Based on Recurrent Neural Networks

Tang

et al. 2018

2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Forecasting wind power generation by a new type of radial basis function-based neural network

Cited by 7 publications

References 7 publications

Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

Harmonic current classification using hybrid FAM-RBF neural network

Multi-step Ahead Wind Power Forecasting Based on Recurrent Neural Networks

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