Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

Tian, Zhongda

doi:10.1177/01423312211046421

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…The steady-state calculation results are taken as the true value of state estimation. On this basis, Gaussian noise is superimposed to form the measured value of state estimation [ 12 , 13 ]. For oils with a viscosity of 0.00001 Ns/m2, the compressibility factor is calculated using the Aga oil model.…”

Section: State Estimation Model Of the Regional Power Gas-integrated ...mentioning

confidence: 99%

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

Chen

2022

Computational Intelligence and Neuroscience

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Aiming at the problems of single planning technology and relatively few resource types in the process of regional comprehensive energy system planning at the current stage, this paper proposes a method based on the construction of regional comprehensive energy system planning and the operation model. Through the integration of the particle swarm optimization algorithm, this method can better realize the optimization and economic operation of the regional comprehensive energy system and build a system optimization mode based on two stages of planning and operation to pursue the optimal configuration of system equipment. Through the simulation algorithm, it is found that the solution time of the traditional basic particle swarm optimization algorithm is 10.49s, while the average solution time of the particle swarm optimization system proposed in this study is 7.93s; the efficiency is increased by 24.4%, and the system operation efficiency is significantly improved, providing theoretical and technical guidance for the economic operation of the regional comprehensive energy system.

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Section: State Estimation Model Of the Regional Power Gas-integrated ...mentioning

confidence: 99%

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

Chen

2022

Computational Intelligence and Neuroscience

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“…[ 211 ] Most studies in the literature have focused on short‐term power estimation because of its simplicity and high accuracy. [ 212 ] Nonetheless, a limited number of studies focused on long‐term wind power estimation due to difficulties and low accuracy prediction. To increase accuracy, it is also emphasized in the literature that there are hybrid methods for WPF [ 20 ] .…”

Section: Process Applicationsmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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Wind energy generation is a fast developing efficient renewable source widely used for modern power generation. [1] Countries and energy companies have increased their interest in electricity generation from wind energy in the last decades, thanks to its natural, cost-free, and environmentally friendly features. [2] Wind power plants can generate electrical energy at any time of the day and are distributed energy sources for systems that need permanent energy. [3] Nonetheless, due to the disordered nature of the earth's atmosphere, there is a major uncertainty in the electrical power produced by the wind, which causes complexities in the utilization and management of wind energy sources. [4] The changes in weather conditions depending on time and location also create uncertainty in managing the supply-demand balance controlled by the grid operators. [5] In cases where the electrical energy produced by wind power plants is insufficient, auxiliary energy sources provide operational support to the electric grid. [6] Ensuring the supply-demand balance in the electricity grid is also an important condition for the electrical power quality and energy supply in the transmission system, and wind power plant operators care about the follow-up of wind power in terms of economy, operation, and planning. [7] For this, the presence of wind power forecasting (WPF) is an essential factor in the optimization of wind power plants. [8] For consumers to use electrical energy uninterruptedly, efficiently, and economically, wind power plant operators need to estimate the generated electrical energy smoothly. [9] The smooth estimation of electrical power generated by wind turbines increases the operational efficiency for system maintenance, planning, investment, and energy traders. [10,11] It is a precious procedure for electrical grids to provide a respectable commercial yield on the electrical energy market. [12] The capability of advanced WPF provides the contributions like balancing the power system and mitigation of penalties, scheduling the maintenance of wind power plants, investment regarding site location, and technoeconomic and socioeconomic benefits. [13,14] In this way, it is possible to reduce the energy cost and obtain high revenue in the energy market as a result of the contributions obtained for wind power generation systems. [15]

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Survey on Deep Fuzzy Systems in Regression Applications: A View on Interpretability

Júnior

Mendes

Souza

et al. 2023

Int. J. Fuzzy Syst.

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Deep learning (DL) has captured the attention of the community with an increasing number of recent papers in regression applications, including surveys and reviews. Despite the efficiency and good accuracy in systems with high-dimensional data, many DL methodologies have complex structures that are not readily transparent to human users. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas such as cyber-security systems, medical, financial surveillance, and industrial processes. Fuzzy logic systems (FLS) are inherently interpretable models capable of using nonlinear representations for complex systems through linguistic terms with membership degrees mimicking human thought. This paper aims to investigate the state-of-the-art of existing deep fuzzy systems (DFS) for regression, i.e., methods that combine DL and FLS with the aim of achieving good accuracy and good interpretability. Within the concept of explainable artificial intelligence (XAI), it is essential to contemplate interpretability in the development of intelligent models and not only seek to promote explanations after learning (post hoc methods), which is currently well established in the literature. Therefore, this work presents DFS for regression applications as the leading point of discussion of this topic that is not sufficiently explored in the literature and thus deserves a comprehensive survey.

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Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

Cited by 5 publications

References 54 publications

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

Survey on Deep Fuzzy Systems in Regression Applications: A View on Interpretability

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