Modeling and forecasting hourly global solar radiation using clustering and classification techniques

Jiménez-Pérez, Pedro F.; Mora‐López, Llanos

doi:10.1016/j.solener.2016.06.039

Cited by 78 publications

(33 citation statements)

References 22 publications

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“…Results showed that ESDLS‐SVR performed better than ARIMA, Seasonal ARIMA (SARIMA), LS‐SVR, and Generalized Regression Neural Network (GRNN) models. Jimenez‐Perez and Mora‐Lopez presented a data‐mining approach–based solar radiation forecast method. A clustering algorithm was used to identify different types of days in the data set.…”

Section: Solar Forecastingmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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Section: Solar Forecastingmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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“…raw data clustering and classification to create multiple homogeneous data groups with uniform patterns, for facilitating more efficient AI learning processes and leading to better prediction performances. Data classification methods observed in this review include principal component analysis [204], k-means clustering algorithm [205], k-fold cross validation [206], decision tree, SVM [207], and game theoretic self-organising map (GTSOM) [208]. Significant performance improvements were observed in these studies relative to model formulation without data classification.…”

Section: Referencesmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

243

105

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“…A hybrid framework is proposed in Ref. [17] to model and forecast hourly global solar radiation data. This approach includes two different phases and uses data mining techniques in each step.…”

Section: Combination Of K-means Dt and Svm-cmentioning

confidence: 99%

Pattern Recognition and Its Application in Solar Radiation Forecasting

Ghofrani¹,

Azimi²,

Youshi³

2019

Pattern Recognition - Selected Methods and Applications

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As intermittent renewable energy sources such as wind and solar proliferate, the power systems operation and planning become more complicated due to increased uncertainties and variabilities. Accurate forecasting of these sources facilitates planning and operating the electric grid to integrate wind/solar power more reliably and efficiently. The neural network learning process can be disrupted by anomalies of wind/solar time-series data, which results in less accurate forecasting. By processing and analyzing wind/solar time-series data, machine learning and pattern recognition methods such as data clustering and classification can significantly enhance the forecast accuracy. This chapter reviews the various machine learning and pattern recognition methods proposed in the literature for time-series forecasting of solar radiation.

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Modeling and forecasting hourly global solar radiation using clustering and classification techniques

Cited by 78 publications

References 22 publications

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Pattern Recognition and Its Application in Solar Radiation Forecasting

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