Forecasting the Power Generation at Renewable Power Plants in Sri Lanka Using Regression Trees
暂无分享,去创建一个
[1] J. Guerrero,et al. A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting , 2023, Results in Engineering.
[2] B. Azzopardi,et al. Renewable energy integration with electric vehicle technology: A review of the existing smart charging approaches , 2023, Renewable and Sustainable Energy Reviews.
[3] Caiyan Qin,et al. A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree , 2023, Sustainability.
[4] Whei-Min Lin,et al. A Review of Modern Wind Power Generation Forecasting Technologies , 2023, Sustainability.
[5] Ashu Jain,et al. Does ANN really acquire the physics of the system? A study using conceptual components from an established water balance model , 2023, Journal of Hydroinformatics.
[6] A. Shaikh,et al. A new Approach to Seasonal Energy Consumption Forecasting using Temporal Convolutional Networks , 2023, Results in Engineering.
[7] Dr. Kirstin K. Holsman. Climate Change 2022 – Impacts, Adaptation and Vulnerability , 2023 .
[8] K. Zhao,et al. Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield , 2023, Agricultural and Forest Meteorology.
[9] Shin-Ju Chen,et al. One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods , 2023, Energies.
[10] Yongping Li,et al. Responses of hydroelectricity generation to streamflow drought under climate change , 2023, Renewable and Sustainable Energy Reviews.
[11] J. Klemeš,et al. A hybrid framework for forecasting power generation of multiple renewable energy sources , 2023, Renewable and Sustainable Energy Reviews.
[12] Jaegul Lee,et al. A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms , 2023, Energies.
[13] Jinyue Yan,et al. Interpretable machine learning for building energy management: A state-of-the-art review , 2023, Advances in Applied Energy.
[14] S. Kokin,et al. Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change , 2022, Energy Reports.
[15] E. Amoussou,et al. Modeling climate change impact on inflow and hydropower generation of Nangbeto dam in West Africa using multi-model CORDEX ensemble and ensemble machine learning , 2022, Applied Energy.
[16] J. Jayasinghe,et al. Development of wind energy prediction models using statistical, machine learning and hybrid techniques: a case study , 2022, Journal of the National Science Foundation of Sri Lanka.
[17] U. Singh,et al. SCADA system dataset exploration and machine learning based forecast for wind turbines , 2022, Results in Engineering.
[18] Chun-tian Cheng,et al. Assessment of climate change impacts on the hydro-wind-solar energy supply system , 2022, Renewable and Sustainable Energy Reviews.
[19] U. Rathnayake,et al. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP) , 2022, Case Studies in Construction Materials.
[20] J. Holechek,et al. A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050? , 2022, Sustainability.
[21] V. Maggioni,et al. Potential Impacts of Future Extreme Precipitation Changes on Flood Engineering Design Across the Contiguous United States , 2022, Water Resources Research.
[22] O. Olanrewaju,et al. Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply , 2022, Frontiers in Energy Research.
[23] B. Pushpawela,et al. Observed southwest monsoon rainfall changes in Sri Lanka and possible mechanisms , 2022, Modeling Earth Systems and Environment.
[24] S. Kamel,et al. A Critical Review of Renewable Hydrogen Production Methods: Factors Affecting Their Scale-Up and Its Role in Future Energy Generation , 2022, Membranes.
[25] S. Wi,et al. Climate change and the hydropower sector: A global review , 2022, WIREs Climate Change.
[26] Upaka Rathnayake,et al. Development of Wind Power Prediction Models for Pawan Danavi Wind Farm in Sri Lanka , 2021 .
[27] Jeevani Jayasinghe,et al. Forecasting Wind Power Generation Using Artificial Neural Network: "Pawan Danawi" - A Case Study from Sri Lanka , 2021, J. Electr. Comput. Eng..
[28] B. Khaniya,et al. Projection of Future Hydropower Generation in Samanalawewa Power Plant, Sri Lanka , 2020 .
[29] Ping-Feng Pai,et al. A Survey of Machine Learning Models in Renewable Energy Predictions , 2020, Applied Sciences.
[30] Xun Sun,et al. Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study , 2020, Scientific Reports.
[31] David Barbosa de Alencar,et al. Different Models for Forecasting Wind Power Generation: Case Study , 2017 .
[32] Jin Lin,et al. Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation , 2017, IEEE Transactions on Power Systems.
[33] Zhancheng Guo,et al. Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China , 2010 .
[34] Vincenzo Balzani,et al. The future of energy supply: Challenges and opportunities. , 2007, Angewandte Chemie.
[35] U. Rathnayake,et al. Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning , 2021, Mathematical Problems in Engineering.
[36] Guijian Liu,et al. Ecological footprint of Rawalpindi; Pakistan's first footprint analysis from urbanization perspective , 2018 .