首页|Study Results from Princess Nourah bint Abdulrahman University in the Area of Ma chine Learning Published [Federated Learning (FL) Model of Wi nd Power Prediction]
Study Results from Princess Nourah bint Abdulrahman University in the Area of Ma chine Learning Published [Federated Learning (FL) Model of Wi nd Power Prediction]
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Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Riya dh, Saudi Arabia, by NewsRx correspondents, research stated, "Wind power is a ch eap renewable energy that plays an important role in the economic development of a country." Financial supporters for this research include Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number; Princess Nourah Bint Abdulrahm an University, Riyadh, Saudi Arabia. The news correspondents obtained a quote from the research from Princess Nourah bint Abdulrahman University: "Identifying potential locations for energy product ion is challenging due to the diverse relationship between wind power potential and the weather characteristics of a location. Many machine learning models were proposed to predict the wind power production level for different locations. Th ere is also a need for a global machine-learning model to enable wind power pred iction of multiple locations with a single global model. A Federated Learning (F L) based model is proposed to train and evaluate the global model of wind power prediction of different locations using wind speed and wind direction. The propo sed wind power prediction model is implemented in Pakistan to forecast the wind power of four distinct locations in Pakistan, using Linear Regression (LR), Supp ort Vector Regression (SVR), Random Forest Regression (RFR), Extreme Gradient Bo osting Regression (XGBR), and Multilayer Perceptron Regression (MLPR) models. Th e evaluation of the model from 30% of the test dataset reveals tha t RFR outperformed with a coefficient of determination (R2) of 0.9717, a Mean Sq uared Error (MSE) of 0.0007 kW, a Root Mean Squared Error (RMSE) of 0.0256 kW, a nd a Mean Absolute Error (MAE) of 0.018 kW."
Princess Nourah bint Abdulrahman Univers ityRiyadhSaudi ArabiaAsiaCyborgsEmerging TechnologiesMachine Learnin g
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