Solar Power Prediction using Regression Models

Erten, Mustafa Yasin; AYDİLEK, Hüseyin

doi:10.29137/umagd.1100957

Cited by 9 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The best-fit model is achieved by error minimization to reduce the variance between the predicted values and the actual values of the output variables to the barest minimum. The relationship between the dependent variable Y and the independent variable X is determined by equation (2) [ 12 ].

where:…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons

Asiedu,

Nyarko,

Boahen

et al. 2024

Heliyon

View full text Add to dashboard Cite

where:…”

Section: Methodsmentioning

confidence: 99%

“…In Ref. [ 12 ], Support Vector Regression achieved a Root Mean Square Error (RMSE) of 318.4 for the hourly forecast of solar PV production.…”

Section: Introductionmentioning

confidence: 99%

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons

Asiedu,

Nyarko,

Boahen

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Principal component analysis (PCA) was also applied, showing improved results in the elastic regression model. The paper focuses on the strengths and weaknesses of each solar power prediction model [58].…”

Section: Supervised Learningmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

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.

show abstract

Section: Supervised Learningmentioning

confidence: 99%

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

Benti¹,

Chaka²,

Semie³

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Solar Power Prediction using Regression Models

Cited by 9 publications

References 19 publications

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons

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

Contact Info

Product

Resources

About