Machine Learning Techniques for Renewable Energy Forecasting: A Comprehensive Review

Gaamouche, Rajae; Chinnici, Marta; Limam, Mohamed; Abakarim, Youness; Hasnaoui, Abdennebi El

doi:10.1007/978-3-030-96429-0_1

Cited by 4 publications

(2 citation statements)

References 119 publications

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“…The paper titled "Exergoeconomic and exergoenvironmental analysis and optimization of an integrated double-flash-binary geothermal system and dual-pressure ORC using zeotropic mixtures; multi-objective optimization" by Chet et al [25] focuses on the optimization of an integrated geothermal system that combines double-flash and binary processes with a dual-pressure Organic Rankine Cycle (ORC). The study uses zeotropic mixtures for the working fluid in the ORC, aiming to improve the system's thermodynamic efficiency and environmental performance.…”

Section: A Next Phase Of Energy Transitionsmentioning

confidence: 99%

“…Beyond material considerations, ML applications extend to optimizing battery components, predicting life cycles, and mitigating failure modes, ultimately enhancing the overall performance and longevity of high-specific-energy cells. Machine Learning for Renewable Energy Forecasting by Gaamouche et al [25] and advances in Lithium-Ion Battery Technologies" by Grey et al [26] presents a potential understanding of predictive modelling potential in Integrating multi-dimensional datasets, it also increases the efficiency of data transformation by processing the data through many neural networks and increases the accuracy in the process.…”

Section: Introductionmentioning

confidence: 99%

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Harnessing Multidimensional Insights and Advanced Machine Learning for Optimized Energy Efficiency: Revolutionizing Sustainable Systems through Predictive Optimization, Ensemble Learning and IoT Integration for Enhanced Heating and Cooling Load Management

Anand,

Parwekar,

Bali

2024

eatp

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Integrating Multidimensional Insights for Enhanced Feature Selection in Energy Transition Models presents a comprehensive approach to enhancing the energy efficiency of sustainable energy systems. The purpose of this research is to find the categorical features that can be boosted with ensemble learning for finding most relevant aspect in energy generation. The study leverages sophisticated machine learning techniques, including deep learning and ensemble methods, to improve the prediction and optimization of heating and cooling loads in systems using application of Advanced Machine Learning Algorithms. In this research article, we are trying to focus on critical energy consumption areas like heating and cooling. These are crucial aspects of building energy consumption, and the study's emphasis on these areas demonstrates an understanding of key factors in energy efficiency. This research represents a significant step forward in applying machine learning to sustainable design and energy savings. It underscores the potential of machine learning in transforming the way systems are designed and operated for better energy efficiency. Understanding the application of machine learning algorithms to cross-domain optimization, such as integrating building energy systems with electric vehicles and smart grid technologies, can create synergies that enhance overall energy efficiency. This holistic approach can lead to more significant energy savings by optimizing across multiple domains simultaneously. We also focus on improving the scalability and generalization capabilities of machine learning models to ensure they can be effectively applied across different types of buildings and geographic locations. It involves developing models that can adapt to diverse conditions without retraining. It enhances collaboration with IoT Devices and strengthening the collaboration between machine learning systems and IoT (Internet of Things) devices can enhance the granularity and precision of energy management. IoT devices can provide detailed, real-time data, which, when analyzed by advanced machine learning algorithms, can lead to more nuanced and effective energy-saving. The model is performing reasonably well, with the ability to predict values that correlate with the actual data. Feature Y1 is by far the most predictive of the model's output, which could mean that focusing on this feature could lead to improvements in the model's performance. The accuracy of our model is near 97% with further scope to improve with ensemble learning and XG boosting.

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Section: A Next Phase Of Energy Transitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Harnessing Multidimensional Insights and Advanced Machine Learning for Optimized Energy Efficiency: Revolutionizing Sustainable Systems through Predictive Optimization, Ensemble Learning and IoT Integration for Enhanced Heating and Cooling Load Management

Anand,

Parwekar,

Bali

2024

eatp

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Assessment of Machine Learning Algorithms for Predicting Potential Solar and Wind Energy Locations

Mhamdi,

Kerrou,

Sarhan

et al. 2024

Lecture Notes in Networks and Systems

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Data-driven economic predictive control for sustainable management of renewable energy systems

Shermatova,

Ibragimova,

Yuldashev

2024

E3S Web Conf.

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The transition to renewable energy sources is driven by the need to reduce greenhouse gas emissions, mitigate climate change, and enhance energy security. Renewable sources, such as solar, wind, and hydropower, are inherently intermittent, making their integration into the power grid complex. This paper emphasizes the significance of predictive modelling for renewable energy optimization and it establishes the connection between machine learning and economic model predictive control techniques for the realization of sustainable energy management of renewable sources. Machine Learning based frameworks can assist energy providers in preparing for fluctuating sustainable energy supplies by predicting energy demand and forecasting the power production capabilities in energy plants. Moreover, combining smart grid designs with proposed predictive control technique can ensure consumer satisfaction while adhering to sustainability requirements.

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Machine Learning Techniques for Renewable Energy Forecasting: A Comprehensive Review

Cited by 4 publications

References 119 publications

Harnessing Multidimensional Insights and Advanced Machine Learning for Optimized Energy Efficiency: Revolutionizing Sustainable Systems through Predictive Optimization, Ensemble Learning and IoT Integration for Enhanced Heating and Cooling Load Management

Harnessing Multidimensional Insights and Advanced Machine Learning for Optimized Energy Efficiency: Revolutionizing Sustainable Systems through Predictive Optimization, Ensemble Learning and IoT Integration for Enhanced Heating and Cooling Load Management

Assessment of Machine Learning Algorithms for Predicting Potential Solar and Wind Energy Locations

Data-driven economic predictive control for sustainable management of renewable energy systems

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