Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

Chakraborty, Debojyoti; Mondal, Jayeeta; Barua, Hrishav Bakul; Bhattacharjee, Ankur

doi:10.1016/j.ref.2023.01.006

Cited by 38 publications

(11 citation statements)

References 40 publications

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“…Previous studies, such as the work of Chakraborty et al [ 41 ], have investigated the impact of certain variables, like weather conditions, on solar power predictions. The novelty of our research, however, is twofold: firstly, it comprehensively analyzes how these variables affect solar power predictions when applying ensemble methods, a technique not extensively explored before in this context.…”

Section: Resultsmentioning

confidence: 99%

Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods

Travieso-González,

Celada-Bernal,

Lomoschitz

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods

Travieso-González,

Celada-Bernal,

Lomoschitz

et al. 2024

Heliyon

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“…These models were validated against field data from a 10kWp solar PV power plant, providing valuable insights for greenfield solar projects in the in the eastern region of India. Voting and stacking algorithms exhibited superior performance, with 313.07 RMSE, 0.96 R2-score and 314.9 RMSE, 0.96 R2-score respectively [7]. Lastly, hybrid models combining CNN and LSTM, as well as LSTM and Gaussian Process Regression GPR, showed promise in stable power generation forecasting.…”

Section: Introductionmentioning

confidence: 93%

Machine Learning-Based Short-Term Solar Power Forecasting: A Comparison Between Regression and Classification Approaches Using Extensive Australian Dataset

AOUIDAD,

BOUHELAL

2023

Preprint

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Solar energy production is an intermittent process that is affected by weather and climate conditions. This can lead to unstable and fluctuating electricity generation, which can cause financial losses and damage to the power grid. To better control power production, it is important to predict solar energy production. Big data and machine learning algorithms have yielded excellent results in this regard. This study compares the performance of two different machine learning approaches to solar energy production prediction: regression and classification. The regression approach predicts the actual power output, while the classification approach predicts whether the power output will be above or below a certain threshold. The study found that the random forest regressor algorithm performed the best in terms of accuracy, with mean absolute errors and root mean square errors of 0.046 and 0.11, respectively. However, it did not predict peak power values effectively, which can lead to higher errors. The Long Short-Term Memory (LSTM) algorithm performed better in classifying peak power values. The study concluded that classification models may be better at generalizing than regression models. This proposed approach is valuable for interpreting model performance and improving prediction accuracy.

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“…Where l is a differentiable convex loss function that measures the difference between the target 𝑦 𝑖 and the prediction 𝑦 ̂𝑖, Ω penalizes the complexity of the XGBoost model, and 𝑥 𝑖 is the input (Chakraborty, Mondal, Barua, & Bhattacharjee, 2023;Chen & Guestrin, 2016).…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Predicting medical waste generation and associated factors using machine learning in the Kingdom of Bahrain

Al-Omran,

Khan

2024

Environ Sci Pollut Res

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Effective planning and managing medical waste necessitate a crucial focus on both the public and private healthcare sectors. This study uses machine learning techniques to estimate medical waste generation and identify associated factors in a representative private and a governmental hospital in Bahrain. Monthly data spanning from 2018 to 2022 for the private hospital and from 2019 to February 2023 for the governmental hospital was utilized. The ensemble voting regressor was determined as the best model for both datasets. The model of the governmental hospital is robust and successful in explaining 90.4% of the total variance.Similarly, for the private hospital, the model variables are able to explain 91.7% of the total variance. For the governmental hospital, the significant features in predicting medical waste generation were found to be the number of inpatients, population, surgeries, and outpatients, in descending order of importance. In the case of the private hospital, the order of feature importance was the number of inpatients, deliveries, personal income, surgeries, and outpatients. These findings provide insights into the factors influencing medical waste generation in the studied hospitals and highlight the effectiveness of the ensemble voting regressor model in predicting medical waste quantities.

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Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

Cited by 38 publications

References 40 publications

Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods

Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods

Machine Learning-Based Short-Term Solar Power Forecasting: A Comparison Between Regression and Classification Approaches Using Extensive Australian Dataset

Predicting medical waste generation and associated factors using machine learning in the Kingdom of Bahrain

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