Weather Forecasting for Renewable Energy System: A Review

Meenal, R.; Binu, D.; Ramya, K.; Michael, Prawin Angel; Kumar, K. Vinoth; Ekambaram, Rajasekaran; Sangeetha, B.

doi:10.1007/s11831-021-09695-3

Cited by 74 publications

(21 citation statements)

References 87 publications

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“…The most common algorithm for bagging is random forest (RF) which can be considered as an extension to the bagging concept, and it can be used for classification and regression. It is made up and trained a large number of decision trees (DT), called predictors and each one produces their own predictions that can create higher accuracy in prediction [ 55 , 56 ]. In the RF algorithm, several decision trees are constructed through training samples (a subset of training samples are injected into each tree randomly).…”

Section: Ensemble Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.

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Section: Ensemble Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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“…Uncertainty involved in the determination of climatic variables affects the policy makers interests on the investments made in Renewable Energy. Hence, forecasting of Climatic variable promotes Sustainable Investments and Green Financing on Renewable Energy resources [8]. The mostly associated climatic variable with Renewable Energy forecasting are Solar Irradiance [9,10],Temperature and Wind Speed [11,12].Weather forecasting models are actively researched in the literature of forecasting.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Explainable SRNN-LSTM Architecture for Irradiance, Temperature and Wind Speed Forecasting

Rajasekaran,

et al. 2023

Preprint

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Sustainable Energy alternatives such as Solar Energy, Wind Energy are the best alternatives to the harmful Non-Renewable energy sources like fossil fuels. With the increasing investment made in the Sustainable Energy alternatives, forecasting of Energy production from the farms plays a crucial in the structured design of Energy farms and for integrated smart grid management. In this research, we propose SRNN-LSTM hybrid models for univariate solar irradiance forecasting, multivariate temperature forecasting and univariate forecasting of Wind Speed. The proposed models are evaluated based on RMSE, MAE, r2 score and Index of Agreement error metrics. The SRNN16LSTM16 hybrid models achieves the best performance with r2 score of 0.97 for Solar Irradiance forecasting and SRNN8LSTM8 hybrid model achieves the best performance with the r2 Score of 0.989 and RMSE of 0.122 for Wind Speed forecasting. We have also proposed a simple approach to derive the prediction intervals based on the Median of the residual values. The higher values of PICP error metric indicate that the proposed models are efficient and perform better than the state-of-the-art models used in the forecasting frameworks. We have also performed the Model Analysis using Explainable Artificial Intelligence tools such as LIME and ELI5 to analyze the importance of features in the model development process, generating and analyzing local explanations.

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“…Various climate models such as mathematical model [1], empirical model [2] [7], fuzzy and ANFIS [8], [9], datamining and internet of things (IoT) based forecasting models [10], [11] are reported in the literatures. Recently machine learning (ML) [12]- [18], deep learning [19] and hybrid models [20], [21] are widely applied for weather forecasting. In this research work, theweather prediction model is developed using machine learning algorithms like support-vector machine (SVM), linear regression and decision tree.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based smart weather prediction

Meenal

Michael

Joseph

et al. 2022

IJEECS

Self Cite

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Weather forecasting refers to the prediction of atmospheric conditions depending on a given time and location. Weather prediction is essential and it plays a significant role in many sectors namely energy and utililities, marine transportation, aviation, agriculture and forestry to a greater extent. Accurate weather forecast mechanism help the farmers for suitable planning of farming operations that will prevent crop losses. In this work, the weather parameters namely precipitation, relative humidity, wind speed and solar radiation were predicted for few Indian locations using the conventional temperature based empirical models and machine learning algorithms such as linear regression, support-vector machine (SVM) and decision tree. Forecasting of weather parameters, on which agriculture depends, will increase the overall yield and it helps farmers and agricultural-based businesses to plan better. From the current results, it is observed that machine learning (ML) based methods had a better prediction results than the physics based conventional models for weather forecasting with mean square error of 0.1397 and correlation coefficient of 0.9259. The objective of this work is to arrive at an optimized end result and a better weather prediction using the Machine learning models with lesser computational effort.

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Weather Forecasting for Renewable Energy System: A Review

Cited by 74 publications

References 87 publications

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Hybrid Explainable SRNN-LSTM Architecture for Irradiance, Temperature and Wind Speed Forecasting

Machine learning based smart weather prediction

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