Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model

Abdellatif, Abdallah; Mubarak, Hamza; Ahmad, Shameem; Ahmed, Tofael; Shafiullah, GM; Hammoudeh, Ahmad; Abdellatef, Hamdan; Rahman, Md. Mahmudur; Gheni, Hassan Muwafaq

doi:10.3390/su141711083

Cited by 45 publications

(17 citation statements)

References 49 publications

(53 reference statements)

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“…It also allows for the use of various loss functions, providing flexibility in optimizing the solar forecaster for specific business requirements. Its effectiveness on complex data, along with its robustness, has sparked new iterations of it, such as the Light Gradient Boosting Model (LGBM) [39], Adaptive Boosting model (AdaBoost) [41] and Categorical Boosting model (CatBoost) [39]. In addition to LR, the authors recognize the significance of GB methods for building a solar forecaster.…”

Section: Classical Machine Learningmentioning

confidence: 99%

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Pandžić,

Capuder

2023

Energies

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Solar forecasting is becoming increasingly important due to the exponential growth in total global solar capacity each year. More photovoltaic (PV) penetration in the grid poses problems for grid stability due to the inherent intermittent and variable nature of PV power production. Therefore, forecasting of solar quantities becomes increasingly important to grid operators and market participants. This review presents the most recent relevant studies focusing on short-term forecasting of solar irradiance and PV power production. Recent research has increasingly turned to machine learning to address this challenge. The paper provides a discussion about building a solar forecasting model, including evaluation measures and machine learning method selection through analysed literature. Given that machine learning is data-driven, the focus of this review has been placed on data sources referenced in the literature. Open-access data sources have been compiled and explored. The main contribution of this paper is the establishment of a benchmark for assessing the performance of solar forecasting models. This benchmark utilizes the mentioned open-source datasets, offering a standardized platform for future research. It serves the crucial purpose of streamlining investigations and facilitating direct comparisons among different forecasting methodologies in the field of solar forecasting.

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Section: Classical Machine Learningmentioning

confidence: 99%

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Pandžić,

Capuder

2023

Energies

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“…This algorithm performs well in balancing individual regression vulnerabilities. Stacking regressor is an ensemble learning technique to combine multiple regression models via a meta-regressor [73][74][75][76]. SR is capable of stacking the output of each individual estimator and computing the final prediction by using each estimator's output as an input for a final estimator.…”

Section: Machine Learning Regressorsmentioning

confidence: 99%

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

Khosravi¹,

Arif²,

Ghaseminejad³

et al. 2022

Preprint

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Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.

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“…The study [ 64 ] reveals that the output power with the insolation and the air temperature has a linear and nonlinear correlation, correspondingly. Recently, researchers have been more interested in the ML application to increase the accuracy of the forecasters [ 61 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ].…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

“…In Table 8 , we briefly summarize the insolation forecasting ML models from studies [ 5 , 7 , 60 , 62 , 65 , 67 , 68 , 69 , 72 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 ].…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model

Cited by 45 publications

References 49 publications

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

A Review on Machine Learning Applications for Solar Plants

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