Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/76046
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dc.contributor.authorSun, Weijia
dc.date.accessioned2018-09-24T12:14:05Z
dc.date.available2018-09-24T12:14:05Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10356/76046
dc.description.abstractPower system time series forecasting is an essential part of smart electric grid. It enhances the reliability and reliability and efficiency of the power system. However, the traditional forecasting methods are unable to satisfy the much higher demand of precision in forecasting. In this dissertation, two kinds of power system datasets are tried, which are electricity load and wind power. Long Short Term Memory (LSTM) network is e↵ective for these sequential data based tasks and some signal preprocessing methods could improve prediction performance. Since wind power generation rely on wind speed, which is stochastic and intermittent, it is more difficult to forecast in short term compared with electricity load forecasting. After implementing di↵erent forecasting methods, a novel approach, which combines LSTM network and Empirical Mode Decomposition (EMD), is proposed. Original data is decomposed into constitutive series through EMD. The decomposition is expressed as a function of a combination of several components. LSTM networks are used to fit the components with di↵erent complexity for prediction. In the proposed model, the network is simplified and computational efficiency is improved. Keywords—Long Short Term Memory, Empirical Mode Decomposition, Wavelet Transform, Short-Term Load Forecasting, Wind Power Forecastingen_US
dc.format.extent61 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleDeep learning for power system time series forecastingen_US
dc.typeThesis
dc.contributor.supervisorPonnuthurai N. Suganthanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
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