On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method

Ren, Zhenxing; Ji, Xinxin

doi:10.1016/j.apr.2023.101731

Cited by 8 publications

(7 citation statements)

References 74 publications

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“…A typical TS soft measurement model has a structure that can be found in [17]. Similarly, superposed c local models describe the structure of the interval-data-based (IDB) T1 TS model for the multiple-input-single-output system.…”

Section: Model Structurementioning

confidence: 99%

“…There are also unique challenges associated with using theoretical methodologies, including the requirement for a full understanding of the theories, precise geographic and meteorological data, and reliable parameters [9]. Although they take a lot of effort and demand extensive domain expertise, theoretical methods provide a deeper understanding of the PV system [17]. Furthermore, the imprecision and unexpected nature of parameter selection, as well as the immeasurability of the application circumstances, cannot be considered, which lessens resistance against changes in real-world scenarios [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the imprecision and unexpected nature of parameter selection, as well as the immeasurability of the application circumstances, cannot be considered, which lessens resistance against changes in real-world scenarios [18,19]. Data-driven soft measurement models are encouraged and outperform theoretical models in terms of measurement performance since theoretical approaches are not always characterized by outstanding performance [17,20]. Unlike the first group, the data-driven techniques make the forecast without having access to extensive historical data.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed soft measurement models then construct interval data with both upper and lower bounds using the extended Chebyshev's Inequality [50]. The antecedent space is partitioned using the improved BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm [17]. The soft measurement model is first estimated in a leastsquares optimal sense case and initial values of antecedent and consequent parameters because these parameters are however only valid for the one-step-ahead prediction.…”

Section: Introductionmentioning

confidence: 99%

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On soft measurement modeling for predicting photovoltaic power with uncertainty based on the Takagi–Sugeno model

Ren,

Zhou

2024

Meas. Sci. Technol.

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Accurate solar power forecast is becoming more essential for safe and reliable power grid operation with the increasing number of grid-connected photovoltaic (PV) power production units. However, PV power exhibits significant output fluctuation due to both external inputs and intrinsic stochasticity in system dynamics. Therefore, an efficient and reliable soft measurement model of PV power with uncertainty is demanded in practice applications. The technique described in this paper captures the impacts produced by the fundamental uncertainty observed in the data, instead of relying on unrealistic assumptions about uncertainty. A soft measurement model using the Takagi-Sugeno (TS) Fuzzy Logic System (FLS) based on several input-output time series of PV plants is presented in this study. Chebyshev's inequality from probability theory and statistics is adopted to create the confidence interval-based response envelopes for these time series at each moment. An envelope-based measure of output uncertainty and a center-valued response forecasting model can be obtained by the proposed identification technique. PV datasets are employed to demonstrate the concept, which indicates the proposed soft measurement may outperform existing methods in terms of prediction accuracy. The average values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Correlation Coefficient (R) are only 0.0787, 0.1113, and 0.9979. The average values of the prediction interval coverage probability (PICP) and the prediction interval normalized average width (PINAW) are 0.9806 and 0.1051.

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Section: Model Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On soft measurement modeling for predicting photovoltaic power with uncertainty based on the Takagi–Sugeno model

Ren,

Zhou

2024

Meas. Sci. Technol.

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“…Systematic machine learning algorithms are employed for many tasks, such as rebuilding absent components in a dataset, automating predictive analytics, and categorizing information into meaningful clusters. The system's software employs advanced algorithms to extract valuable insights from large datasets, significantly reducing the time required [7]. Machine learning algorithms can be utilized to recognize patterns and trends in data, identify abnormalities, and even construct prediction models.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

Park

2024

JMC

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Machine learning is an effective technique for optimizing real-time electronics product data analysis. It can efficiently handle large electronics product datasets, reducing processing time and resource requirements for generating insights. This study assesses the current status of methods and applications for optimizing real-time data analysis by examining existing research in machine learning-based recommendation systems for electronic products. The indicated subjects encompass using machine learning algorithms to discern characteristics and correlations from large datasets, applying machine learning for prognostic analytics and projection, and utilizing machine learning to identify anomalies. The paper provides examples of machine learning-based evaluation optimization solutions that focus on utilizing unorganized data and delivering real-time dashboards. Presented here is a discussion on the complex challenges and potential benefits associated with utilizing machine learning to optimize real-time data processing. Machine learning may efficiently expedite real-time data assessment while delivering precise and timely outcomes

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A neuro-fuzzy model to predict respiratory disease hospitalizations arising from the effects of traffic-related air pollution in São Paulo

Santana,

Miranda,

Rosa

et al. 2024

Clean Techn Environ Policy

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On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method

Cited by 8 publications

References 74 publications

On soft measurement modeling for predicting photovoltaic power with uncertainty based on the Takagi–Sugeno model

On soft measurement modeling for predicting photovoltaic power with uncertainty based on the Takagi–Sugeno model

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

A neuro-fuzzy model to predict respiratory disease hospitalizations arising from the effects of traffic-related air pollution in São Paulo

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