Prediction of Wind Power with Machine Learning Models

Karaman, Ömer Ali

doi:10.3390/app132011455

Cited by 13 publications

(7 citation statements)

References 43 publications

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“…The RMSE shows the standard deviation of the errors. If the RMSE is within a limited range with regard to the test and training samples, the model does not overfit [34].…”

Section: Performance Metricsmentioning

confidence: 99%

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Tanyıldızı Ağır

2024

Sustainability

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Sustainable and renewable energy sources are of great importance in today’s world. In this respect, renewable energy sources are used in many fields of technology. In order to minimize dust on PV panels and ensure their sustainability, power losses due to dust must be estimated accurately. In this way, the efficiency of a sustainable energy source will increase and serious economic savings can be achieved. In this study, a hybrid deep learning model was designed to predict losses caused by dust in PV panels installed in the Manisa Saruhanlı district. The hybrid deep learning model consists of Long Short-Term Memory (LSTM) and K-Nearest-Neighbors (KNN) algorithms. The performance of the proposed hybrid deep learning model was compared with LSTM and KNN algorithms. Sensitivity analysis was performed to statistically evaluate the prediction results. The input variables of the models were time, sunshine duration, humidity, ambient temperature and solar radiation. The output variable was the losses caused by dust in the PV panels. Hybrid LSTM-KNN, LSTM and KNN models predicted losses caused by dust in PV panels with 98.22%, 95.51% and 61.49% accuracy. The hybrid LSTM-KNN model predicted losses caused by dust in PV panels with higher accuracy than other models. Using LSTM and KNN algorithms together improved the performance of the hybrid deep learning model. With sensitivity analysis, it was found that solar radiation is the most important variable affecting the losses caused by dust in PV panels.

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“…The RMSE shows the standard deviation of the errors. If the RMSE is within a limited range with regard to the test and training samples, the model does not overfit [34].…”

Section: Performance Metricsmentioning

confidence: 99%

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Tanyıldızı Ağır

2024

Sustainability

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“…[ [35][36][37][38][39][40][41] The studies focused on estimating the power obtained from IRESs and the power consumed in the power system by using machine learning methods.…”

Section: Referencesmentioning

confidence: 99%

Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources

Atiç,

Izgi

2024

Sustainability

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Estimation of the power obtained from intermittent renewable energy sources (IRESs) is an important issue for the integration of these power plants into the power system. In this study, the expected power not served (EPNS) formula, a reliability criterion for power systems, is developed with a new method that takes into consideration the power generated from IRESs and the consumed power (CP) estimation errors. In the proposed method, CP, generated wind power (GWP), and generated solar power (GSP) predictions made with machine learning methods are included in the EPNS formulation. The most accurate prediction results were obtained with the Multi Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms used for prediction, and these results were compared. Using different forecasting methods, the relation between forecast accuracy, reserve requirement, and total cost was examined. Reliability, smart reserve planning (SRP), and total cost analysis for power systems were carried out with the CNN algorithm, which provides the most successful prediction result among the prediction algorithms used. The effect of increasing the limit EPNS value allowed by the power system operator, that is, reducing the system reliability, on the reserve requirement and total cost has been revealed. This study provides a useful proposal for the integration of IRESs, such as solar and wind power plants, into power systems.

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“…Studies using LSTM-based methods for energy estimation are available in the literature [46]. An LSTM method with physical constraints was proposed by Luo et al When compared to conventional statistical and machine learning techniques, the physically constrained LSTM model greatly increased prediction accuracy [47].…”

Section: Related Workmentioning

confidence: 99%

Multilayer LSTM Model for Wind Power Estimation in the Scada System

ÇELEBİ,

KARAMAN

2023

EJT

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Wind energy is clean energy that does not pollute the environment. However, the complex and variable operating environment of a wind turbine often makes it difficult to predict the instantaneous active power generated. In this study, a wind turbine active power estimation system based on a short-term memory network (LSTM) using time series analysis is proposed. The data obtained from the wind turbine SCADA system is used as input variables. In the proposed method, a multilayer LSTM architecture is designed to train the model. The first LSTM network consists of 64 units, and the second one consists of 32 units. This is followed by a dense layer consisting of 16 neurons. In the last layer, the architecture is finalized by using a linear activation function for the prediction process. The proposed deep learning (DL)-based LSTM prediction model takes into account environmental factors such as wind speed and wind direction for active power forecasting. The results show that the LSTM-based time series analysis method is capable of effectively capturing time series features among the data. Thus, the proposed architecture can realize high-accuracy active power forecasting.

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Prediction of Wind Power with Machine Learning Models

Cited by 13 publications

References 43 publications

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources

Multilayer LSTM Model for Wind Power Estimation in the Scada System

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