Thesis

Electricity Price and Load Forecasting using Enhanced Machine Learning Techniques

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... Therefore, point forecasts have limited use in stability and security analysis of power systems. To overcome the limitation of point forecasts, deep learning methods are widely used in the field of WPF and other electricity related forecasting tasks [8][9][10]. Deep Neural Networks (DNN) have the inherent property of automatic modeling of the wind power characteristics [11]. ...
... To mitigate this risk, wind power forecasting is the most popular method. The wind power is forecasted using classical [9][10][11][12][13][14][15][16][17], statistical and artificial intelligent methods. In literature, there are two types of wind power forecasting techniques: time series [12] and multivariate [13]. ...
Chapter
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Due to the depletion of fossil fuel and global warming, the incorporation of alternative low carbon emission energy generation becomes crucial for energy systems. The wind power is a popular energy source because of its environmental and economic benefits. However, the uncertainty of wind power, makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance by wind power, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. In this proposed model, Wavelet Packet Transform (WPT) is used to decompose the wind power signals. Along with decomposed signals and lagged inputs, multiple exogenous inputs (calendar variable, Numerical Weather Prediction (NWP)) are used as input to forecast wind power. Efficient Deep Convolution Neural Network (EDCNN) is employed to forecast wind power. The proposed model’s performance is evaluated on real data of Maine wind farm ISO NE, USA.
... Therefore, point forecasts have limited use in stability and security analysis of power systems. To overcome the limitation of point forecasts, deep learning methods are widely used in the field of WPF and other electricity related forecasting tasks [8][9][10]. Deep Neural Networks (DNN) have the inherent property of automatic modeling of the wind power characteristics [11]. ...
... To mitigate this risk, wind power forecasting is the most popular method. The wind power is forecasted using classical [9][10][11][12][13][14][15][16][17], statistical and artificial intelligent methods. In literature, there are two types of wind power forecasting techniques: time series [12] and multivariate [13]. ...
Conference Paper
Full-text available
Due to the depletion of fossil fuel and global warming, the incorporation of alternative low carbon emission energy generation becomes crucial for energy systems. The wind power is a popular energy source because of its environmental and economic benefits. However, the uncertainty of wind power, makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance by wind power, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. In this proposed model, Wavelet Packet Transform (WPT) is used to decompose the wind power signals. Along with decomposed signals and lagged inputs, multiple exogenous inputs (calendar variable, Numerical Weather Prediction (NWP)) are used as input to forecast wind power. Efficient Deep Convolution Neural Network (EDCNN) is employed to forecast wind power. The proposed model's performance is evaluated on real data of Maine wind farm ISO NE, USA.
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