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An overview on wind power forecasting methods
2015 International Conference on Machine Learning and Cybernetics (ICMLC), 2015With the continually increasing growth in wind generation being integrated into the electric networks, it brings about significant challenges for decision-makers of power system operation due to its high volatility and uncertainty. One efficient approach to tackling such a problem is using reliable forecasting tools.
Songjian Chai +3 more
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ANN Approach to WECS Power Forecast
2005 IEEE Conference on Emerging Technologies and Factory Automation, 2006In this work-in-progress the problem with the future integration of large quantity of wind generators in the Portuguese electric grid is presented. A method based in artificial neural networks (ANN) is used to predict the average hourly wind speed. The work starts by choosing the patterns set length, the ANN structure and the learning method.
Pedro M. Fonte, José Carlos Quadrado
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2007
While considerable progress has been made in wind power forecasting in the last decade or so, in terms of better understanding of the processes involved and higher accuracy of the forecasts, there is still plenty of scope for improvement. Clearly forecasts for the wind power production of whole regions or supply areas are more accurate than forecasts ...
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While considerable progress has been made in wind power forecasting in the last decade or so, in terms of better understanding of the processes involved and higher accuracy of the forecasts, there is still plenty of scope for improvement. Clearly forecasts for the wind power production of whole regions or supply areas are more accurate than forecasts ...
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ANN backpropagation power consumption forecasting
IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON), 2015The paper is focusing on artificial neural networks (ANN) based load forecasting. It is applied for hourly load and daily load curves forecasting. The backpropagation algorithm is presented. The basic propagation algorithm is completed with conjugated gradient and parabolic interpolation. A software tool has been developed in Matlab environment.
Raluca Schiopu +4 more
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2019
Solar energy is a promising environmentally-friendly energy source. Yet its variability affects negatively the large-scale integration into the electricity grid and therefore accurate forecasting of the power generated by PV systems is needed. The objective of this thesis is to explore the possibility of using machine learning methods to accurately ...
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Solar energy is a promising environmentally-friendly energy source. Yet its variability affects negatively the large-scale integration into the electricity grid and therefore accurate forecasting of the power generated by PV systems is needed. The objective of this thesis is to explore the possibility of using machine learning methods to accurately ...
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The Power Load Forecasting by Kernel PCA
2010We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3 ...
Fang-Tsung Liu +3 more
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Adversarial Attacks to Solar Power Forecast
2021 IEEE Global Communications Conference (GLOBECOM), 2021Ningkai Tang, Shiwen Mao, R. Mark Nelms
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Modeling and Forecasting Photovoltaic Power Production
2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2023Diogo Ribeiro +3 more
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