Solar Irradiance Prediction Based on Weather Patterns Using Bagging-Based Ensemble Learners with Principal Component Analysis

Guia, Justin D. de; Concepcion, Ronnie; Calinao, Hilario A.; Tobias, Rogelio Ruzcko; Dadios, Elmer P.

doi:10.1109/r10-htc49770.2020.9356988

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The base learner in the ensemble was a pre-processed stacked LSTM model. The study showed that bagging-based ensemble learners outperformed individual learners in terms of accuracy, as evidenced by multiple metrics [195]. Another EL method used in renewable energy forecasting is the boosting technique.…”

Section: Ensemble Learning For Renewable Energy Forecastingmentioning

confidence: 93%

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

2023

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This article presents a review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

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Section: Ensemble Learning For Renewable Energy Forecastingmentioning

confidence: 93%

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

2023

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“…The base learner in the ensemble was a pre-processed stacked LSTM model. The study showed that the Bagging-based ensemble learners outperformed individual learners in terms of accuracy, as evidenced by multiple metrics [187]. Another EL method used in renewable energy forecasting is the Boosting technique.…”

Section: Elm For Renewable Energy Forecastingmentioning

confidence: 95%

Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects

Benti¹,

Chaka²,

Semie³

2023

Preprint

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This article presents a review of current advances and future prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

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“…Thornthwaite potential evapotranspiration (PET Th ) is a referential measure characterized by the mathematical relationships of environmental parameters such as the average monthly possible sunshine hour (N, h/day), number of days of each month (m, days), mean temperature (T m , • C), heat index (I, unitless), and extended heat index (α, unitless) (1-3). Here, N, m, T m , I, and α were set to minimum and maximum threshold pairs of [6.2 to 11.3 h/day], [29 to 31 days/month], [25.483 to 32.483 • C], [12.8 to 15.3], and [−6.9 to −4.3] based on initial acquired experimented data of the Philippines from 2019 to 2021 [38][39][40]. The goal of this stage was to optimize the modified PET Th (PET Th-mod ) as a function of {N, T m , I, α} (4), serving as the fitness function, through minimization using atom search (ASO), differential evolution (DE), and multiverse optimization (MVO) to reduce plant stress due to drought when there is substantial high temperature (Figure 3).…”

Section: Thornthwaite Evapotranspiration Optimization Using Advanced ...mentioning

confidence: 99%

Effects of Atom Search-Optimized Thornthwaite Potential Evapotranspiration on Root and Shoot Systems in Controlled Carica papaya Cultivation

Concepcion,

Baun,

Janairo

et al. 2023

Agronomy

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Potential evapotranspiration (PET) indicates if a cultivation area is suitable for planting. Currently, site-specific PET models that are based on large geographic regions are vulnerable to inaccurate predictions as a result of climate change and sudden changes in the environmental abiotic stressors that affect plant growth. For the aim of promoting the papaya Sinta F1 cultivar, the study optimized the standard Thornthwaite PET model by integrating three advanced physics-based metaheuristics and evolutionary computing, namely atom search (ASO), differential evolution (DE), and multiverse (MVO) optimizers. The PET value was optimized through minimization as a function of air temperature, light intensity, heat index, and extended heat index. As the PET value approaches 0, it indicates that there is more soil-water content that can be absorbed by plants. Based on the four cultivation treatments (uncontrolled, ASO, DE, and MVO) exposed in three replicates within 90 days, the ASO-optimized Thornthwaite PET-treated (ASOTh) papaya plants resulted in the highest chlorophyll a and b concentrations, densest stomatal density, concentrated root and stem xylem and phloem vessels, considerable root and stem length, most formed leaf count, and strongest action potentials coming from stem membrane for both light and dark periods. This proves the applicability of the intelligent process in modifying the Thornthwaite model for plant growth promotion. Also, through the developed ASOTh, the stem length and thickness ratio was improved for mechanical stability to facilitate more branching leaves and potential fruits during the fruiting stage, and the chlorophyll a and b ratio was enhanced, which naturally extended the light energy band for photosynthesis. Overall, the newly developed ASOTh model may be used to grow papaya seedlings year-round anywhere on Earth if there is a control system to regulate the environmental setting inside the growth chamber.

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Solar Irradiance Prediction Based on Weather Patterns Using Bagging-Based Ensemble Learners with Principal Component Analysis

Cited by 7 publications

References 12 publications

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects

Effects of Atom Search-Optimized Thornthwaite Potential Evapotranspiration on Root and Shoot Systems in Controlled Carica papaya Cultivation

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