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Title: Electricity Price Time Series Forecasting in Deregulated Markets Using Recurrent Neural Network Based Approaches
Keywords: Recurrent Neural Networks, Electricity Price, Chaos Theory, Multiscale Dynamics, Fixed Point, Gradient Descent
Issue Date: 30-Oct-2011
Source: VISHAL SHARMA (2011-10-30). Electricity Price Time Series Forecasting in Deregulated Markets Using Recurrent Neural Network Based Approaches. ScholarBank@NUS Repository.
Abstract: In the past decade, electricity price time series system originating from recently deregulated electricity markets has been the focus of study for many researchers and power system engineers. These are complex dynamical systems which have tipping points at which sudden shifts to a spiking dynamical regime occurs. Although there are several techniques available for short term forecasting of electricity prices, very little has been done for accurate prediction of spikes along with otherwise volatile region of time series. High volatility and intermittent spikes are hallmarks of chaos taking place in electricity price time series. Modeling these systems require a dynamic approach with accurate approximation capabilities, such as recurrent neural networks. Recently recurrent neural networks have gained immense interest due to their unconventional ability to solve complex problems. However training them in complex dynamic environments such as electricity price time series is a challenging task due to various issues, which mainly include problem of local optima. However this problem can be rectified through intelligent learning of RNN incorporating heuristic knowledge of the system. Recently electricity price time series has been extensively investigated using nonlinear systems theory. Utilization of the extracted system invariant information to assist in solving issue of local optima can open a new dimension in recurrent neural network (RNN) learning and modeling. This thesis focuses on extraction of invariant dynamics of electricity price time series and incorporates them for developing RNN based pure as well as hybrid models for modeling electricity price time series and accurate prediction of price in spiking and nonspiking regime. 8 In this thesis, three RNN based approaches have been developed. First a novel recurrent neural network learning algorithm based on fixed point dynamics of time series system has been developed. This approach has been shown to bring the trained RNN model closer to exact nonlinear system. In the second approach, it has been proposed to hybridize the Recurrent Neural Network and a multi-scale excitable dynamic model to closely resemble the dynamic properties and spiking characteristics of time series system for obtaining an accurate forecasting model. This approach exploits the universal dynamic nonlinear approximation properties of RNN and spiking characteristics of self coupled FitzHugh Nagumo model. Fitz-HughNagumo (FHN) has been shown to exhibit dynamics close to electricity price due to presence of multiple scale dynamics. RNN trained using Evolutionary Strategies (ES) has been used for obtaining the parameter values of a coupled equation system (FHN). In third approach, the dynamic mechanism behind spike adding in time series has been extensively studied. Slow-fast dynamics and the corresponding complex homoclinic/heteroclinic scenarios, which are the underlying mechanism behind irregular spiking in time series have been exploited for modelling of multi-scale neural networks which are trained using singular perturbation theory and gradient descent algorithm. The developed models have been tested on various markets worldwide for different seasons. After extensive comparison with benchmarks, it has been demonstrated that the results are improved considerably. To give an overview, the main contributions of this thesis are- ¿ Extraction of invariant measures of electricity price time series and confirm the presence of multiple scale dynamics in time series. 9 ¿ Development of novel learning algorithm for RNN training incorporating invariant measures of time series. ¿ Development of a multi-scale neural network models and their learning algorithm employing singular perturbation theorem and use them for forecasting of price in deregulated electricity markets. The proposed approach improved prediction accuracy in spiking region.
Appears in Collections:Ph.D Theses (Open)

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